Root cutters: Novel tillage methods to control creeping perennial weeds with a low risk of soil erosion and nutrient leaching

Root cutters: Novel tillage methods to control creeping perennial weeds with a low risk of soil erosion and nutrient leaching | 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 Root cutters: Novel tillage methods to control creeping perennial weeds with a low risk of soil erosion and nutrient leaching Björn Ringselle, Trond Børresen, Anneli Lundkvist, Kjell Mangerud, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3827798/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Nov, 2025 Read the published version in Agronomy for Sustainable Development → Version 1 posted You are reading this latest preprint version Abstract Perennial weeds are a major obstacle for reducing pesticides and tillage. Three multi-year experiments were conducted in Norway and Sweden to determine if a) the horizonal and vertical root/rhizome cutters (HRC and VRC, respectively) can provide effective non-chemical control of multiple perennial weed species comparable to more intensive tillage methods (Experiments 1–2), b) without increasing the risk of soil erosion and nutrient leaching (Experiment 3), and c) if integrating the VRC with the HRC, mowing or disc harrow can increase the efficacy against perennial weeds (Experiment 1). All treatments were spring plowed in Experiment 1 and 3, and autumn plowed in Experiment 2. In Experiment 1, the rotary tiller was the most suppressive against Sonchus arvensis and Elymus repens but increased Stachys palustris shoot numbers. HRC treatments were not significantly worse than the rotary tiller and increased crop yield by 28%, reduced total perennial shoot biomass by 46–51% and reduced S. arvensis and E. repens shoot biomass by 52% and 80%, respectively, compared to an untreated control. In Experiment 2, HRC treatments reduced Cirsium arvense shoot numbers by 71% compared to the untreated control but failed to control E. repens . HRC treatment depth (7 vs. 15 cm) did not significantly affect control efficacy. Experiment 3 showed that HRC did not increase soil, water or nutrient losses compared to the untreated control and resulted in 60% less soil and 52% less phosphorous losses than disc harrowing. Treatments with VRC reduced the shoot biomass of E. repens by 40% and S. arvensis by 22%, compared to without VRC. Novelly, the results show that in plowed systems, HRC provides control of multiple perennial weed species that is comparable to more intensive tillage methods, but with little risk of soil and nutrient losses; and integrating VRC into control strategies improves perennial weed control efficacy. Agronomy Conservation agriculture regenerative agriculture organic agriculture integrated pest management Elytrigia repens Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction To create a more sustainable future for humanity, it is imperative to reduce the ecological footprint of our production systems. The reduction or elimination of pesticide use and/or tillage is a common requirement or goal of most agricultural sustainability concepts [e.g., no pesticides allowed in organic farming (Migliorini and Wezel 2017 ), reduced-till to no-till in conservation agriculture (Nichols et al. 2015 ), reduced or eliminated tillage and pesticide use in regenerative agriculture (Newton et al. 2020 ), priority of preventive measures over direct weed control in integrated weed management (IWM) (Riemens et al. 2022 )] and programs aimed at increasing agricultural sustainability [e.g., the European Farm to Fork strategy, which aims to reduce pesticide use and nutrient losses in the EU by 50% by 2030 (Wesseler 2022 )]. For pesticides this is due to concerns over environmental persistence, groundwater pollution, effects on non-target organisms and toxicity to humans (Van Bruggen et al. 2018 ) – and as a result many pesticides have been banned or had their use restricted within the EU. Tillage, on the other hand, can both be detrimental to the treated soil by e.g. removing the protective soil cover, destroying soil aggregates and damaging soil-living organisms, but also to the surrounding environment by increasing the risk of soil erosion and nutrient leaching (Klik and Rosner 2020 ). Weeds are the major obstacle for reducing pesticides and tillage. On average, the potential of weeds to cause yield losses are more than twice that of other pests such as insects, fungi and pathogens (Oerke 2006 ). As a consequence, herbicides make up a large proportion of the pesticides sold, especially in cereal and grassland dominated regions such as northern Europe (Antier et al. 2020 ). Tillage-tolerant creeping perennial weeds, such as Elymus repens (L.) Gould (couch grass), Cirsium arvense (L.) Scop. (creeping thistle) and Sonchus arvensis L. (perennial sow-thistle), are especially problematic to control without herbicides or tillage due to the persistence of their underground storage organs (e.g. thickened roots capable of producing shoots, taproots, tubers, rhizomes) (Håkansson 2003 ; DiTommaso and Prostak 2021 ). In modern industrial agriculture, herbicides are the main control method for perennial weeds, especially glyphosate and other systemic herbicides that are transported down to their underground storage organs, thus killing the whole plant. However, glyphosate is currently facing increasing scrutiny within the EU, and while it was renewed again in 2023, a potential ban is continuously being discussed, which would severely limit the options of conventional farmers (Fogliatto et al. 2020 ), in particular their ability to control perennial weeds such as E. repens , for which alternative herbicides are expensive and/or not allowed in sufficiently high doses for control (Ringselle et al. 2020 ). The main alternatives to herbicides to control perennial weeds are using a combination of preventive measures (e.g. the inclusion of regularly mown competitive leys or cover crops, which can be effective against some perennial weed species (Thomsen et al. 2015 )) and/or using intensive tillage (e.g., multiple harrowing operations to fragment the roots/rhizomes followed by moldboard plowing) (Ringselle et al. 2020 ; Soares et al. 2023 ). An increased use of preventive measures would be beneficial to agricultural sustainability and create more diverse cropping systems which do not promote a few highly competitive weed species like monocultures do (Adeux et al. 2019 ). However, many effective preventive measures (e.g. leys) are underutilized due to low profitability or other agronomic concerns; and some perennial weed species such as E. repens are difficult to control through preventive measures alone. Thus, because of problems with perennial weeds, efforts to reduce herbicide use usually ends up increasing tillage use and vice versa. While some forms of tillage can have beneficial effects (e.g. reducing some plant diseases (Bankina et al. 2018 ) and preparing the soil for the next crop), the intensive tillage used to control perennial weeds is energy and time-demanding, and can result in extended periods of bare soil, which increases the risk of soil erosion and nutrient leaching (Aronsson et al. 2015 ; Klik and Rosner 2020 ). Intensive tillage is also difficult to combine with cover crops that could otherwise reduce nutrient leaching (Melander et al. 2016 ); and intensive tillage in spring can delay sowing, which often reduces the yield of spring-sown crops (Brandsæter et al. 2017 ). Perennial weed species also vary in the traits that affect their susceptibility to different control methods (e.g., underground storage organs occur at different soil depths and with varying degrees of dormancy (Liew et al. 2013 )), which complicates control efforts as methods and timing often have to be adjusted to suit each species. There are some non-chemical alternatives to tillage that can destroy roots/rhizomes (e.g., steaming, electricity, solarization), but in general they are very energy demanding, slow and/or may not reach very deeply into the soil (Ringselle et al. 2020 ). Thus, there is a need for non-chemical tools that can effectively and resource-efficiently control perennial weeds with minimal soil disturbance and low risk of soil and nutrient losses. The Kverneland Group (together with researchers participating in a series of research projects, starting with the “Optimising Subsidiary Crop Applications in Rotations” (OSCAR) project) have developed two root/rhizome cutter (RC) prototypes that could potentially strike the golden balance between being able to fragment the roots/rhizomes of perennial weeds, but with minimal soil disturbance, and thus theoretically have a low risk of soil and nutrient losses. The first prototype, the vertical RC (VRC; see Fig. 1 A-B), cuts vertically through the soil using coulter disks and can reach 12 cm soil depth, meaning that it is likely to be most effective against species which roots/rhizomes are typically found in the upper part of the soil profile, such as E. repens (Ringselle et al. 2018 , 2023 ; Brandsæter et al. 2020 ). The second prototype, the horizontal RC (HRC), cuts horizontally using wide shears to a maximum depth of 30 cm (see Fig. 1 C-D), but the depth can be adjusted as desired, making it potentially effective against both perennial weed species with shallow and deep roots/rhizomes, such as C. arvense which roots can reach more than a meter into the soil (Favrelière et al. 2020 ). The VRC has been tested against E. repens in leys, showing that fragmenting its rhizome network once in a crisscross pattern can reduce E. repens rhizome biomass by 38%, while twice can reduce it by 63% (Ringselle et al. 2018 ). This supported previous results that have shown a large reductive effect of fragmenting the underground storage organs of perennial weed species (e.g., Bergkvist et al. 2017 ). Moreover, the VRC treatment resulted in an increase in Italian ryegrass ( Lolium multiflorum Lam.) and white clover ( Trifolium repens L.), and the beneficial effect on Italian ryegrass was higher when it was performed in the growing crop compared to prior to crop sowing (170% vs 78%). Further experiments in an established ley have shown that the VRC does not operate well under hard soil conditions (Ringselle et al. 2023 ). The HRC has been shown to be able to reduce C. arvense shoot numbers, patch expansion and root carbohydrate content, though in these studies it was not as effective as moldboard plowing (Weigel and Gerowitt 2022 ; Weigel et al. 2023 ). So far, no studies have been published that demonstrate the efficacy of the RCs in controlling multiple perennial weed species with different root/rhizome traits, nor how the RCs affect soil erosion or nutrient leaching. The current study will fill these gaps by presenting the results from a series of experiments from Norway and Sweden comparing the RCs’ effect on multiple perennial weed species over multiple years compared to other tillage treatments (Experiment 1 & 2), the effect of combining the VRC with other tillage treatments (Experiment 1), and the effect of the HRC on soil erosion and nutrient leaching (Experiment 3). The tested hypotheses were that: 1) the RCs will result in significantly less perennial weed biomass and shoot numbers, and higher crop yield, than an untreated control and mowing, and will not have a significantly worse effect than more intensive tillage methods (i.e. disc harrows, stubble harrows and rotary tillers), 2) integrating VRC with other control methods (e.g. HRC, disc harrow, mowing) will increase the control efficacy against perennial weeds, 3) perennial weed species with relatively shallow roots/rhizomes such as E. repens , S. arvensis , Vicia cracca L. (tufted vetch) and Stachys palustris L. (marsh woundwort) (typical growth depth of the regenerative plant organs of these species is up to 10–30 cm, with species ordered from most to least shallow), will be more affected by a shallow treatment (i.e. the VRC, or the HRC used at 7 cm depth), while perennial weed species with deeper roots/rhizomes such as C. arvense will be more greatly reduced by a deeper treatment (i.e. the HRC used at 15 cm depth), and 4) using the HRC in the cereal stubble will not increase the water, soil and nutrient losses compared to an untreated control, but will result in significantly lower losses than using a disc harrow. 2. Materials and Methods 2.1. Experiment 1 and 2 2.1.1 Study sites, experimental design and treatments Experiment 1 was performed in Ås, Norway (59°66′N 10°76′E) from 2016–2019. The soil at the Ås site is a silty clay loam with poor natural drainage and classified as an epistagnic albeluvisol (siltic), according to the WRB system (World Reference Base 2006 ). The site had naturally established populations of E. repens, S. arvensis, C. arvense, S. palustris and V. cracca that dominated the weed flora, and there were no prominent annual weeds. All plots were fertilized with dried chicken manure [“Marihøne Pluss” 8 (%N) – 4 (%P) – 5 (%K)] corresponding to 80–100 kg total N ha − 1 . The fields were sown with spring barley ( Hordeum vulgare L.) in 2016 and 2017, and oat (Avena sativa L.) in 2018 and 2019 (Table 1 ). Weather data for the site of Experiment 1 and 3 is given in Table 2 . Experiment 2 was performed at Ultuna close to Uppsala, Sweden (59°48′N, 17°39′E) from 2017–2019. The soil at the Ultuna site is a heavy clay soil, and classified as a vertisol, according to the WRB system (World Reference Base 2006 ). The site had naturally established populations of C. arvense and E. repens. Only Chenopodium album L. (lamb’s quarters) was a prominent annual weed. Mineral fertilization was applied each year at sowing as NP 27 − 3 with a N-supply of 80 kg ha − 1 . The fields were sown with spring barley ( Hordeum vulgare L.) in all experimental years. Weather data for the site of Experiment 2 are given in Table 3 . Both Experiment 1 and 2 used complete randomized block designs with 4 blocks. Experiment 1 used 2 x 14 m plots, while Experiment 2 used 6 x 7 m plots. In Experiment 1, there were 2-meter margins between all plots, which were stubble-harrowed in the autumn to control weeds. Prior to the experiments, both sites had been organically farmed for many years with small-grain cereals dominating the rotation. Levelling, fertilizing, seedbed preparation, sowing and rolling were common for all experimental plots in both experiments. Additionally, spring plowing was used in all experimental plots in Experiment 1, and autumn plowing in Experiment 2 (Table 1 ), as this is common practice in Norway and Sweden, respectively. The following five treatments were performed in both Experiment 1 and 2: 1) Untreated control, 2) Disc harrow 12 cm depth, 3) Stubble harrow 12 cm depth, 4) HRC 7 cm depth, and 5) HRC 15 cm depth. In Experiment 1 an additional five treatments were performed: 6) Mowing, 7) Mowing + VRC 12 cm depth, 8) VRC 12 cm depth + HRC 12 cm depth, 9) VRC 12 cm depth + Disc harrow 12 cm depth, and 10) Rotary tiller 12 cm depth. Implement specifications for the treatments are provided in Table 4 . In both the VRC + HRC and VRC + Disc harrow treatments, the VRC was performed after the other tillage treatment. Timing of the treatments are given in Table 1 . A second stubble treatment was performed if the perennial weeds had had sufficient time to reach their compensation stage (Ringselle et al. 2021 ) there after the threshing and the soil conditions allowed for tillage (e.g., not too wet). 2.1.2. Assessment Assessments were done in the autumn for both Experiment 1 and 2 (Table 1 ). Experiment 1 was assessed using four 0.5 m 2 (thus, 2 m 2 in total per plot) randomly placed quadrants for all measurements. In the quadrants all shoots were counted and all perennial shoot biomass collected. The biomass samples were dried at 70°C for 72 h to determine the dry weight. An experimental combine harvested 1.5 m x 7 m in the middle of each plot. The grain yield of the plots was weighed at harvest and dried for storage. Grain moisture at harvest, grain weight per hectoliter and screening percentage were determined. Final grain yield was adjusted to 85% dry matter. In Experiment 2 shoot numbers were assessed using four 1 m 2 randomly placed quadrants (4 m 2 total per plot) for E. repens , while C. arvense and C. album shoots were counted across the middle of the whole plot in a two-meter-wide strip (i.e., 14 m 2 ). For shoot biomass four subplots with an area of 0.25 m 2 were randomly selected in each plot (thus, 1 m 2 in total per plot). All plant material was harvested, and separated into spring barley, C. arvense , E. repens , C. album and other weeds. The plant material was dried at 105˚C until constant weight was achieved, and dry weight was recorded. Before drying, spring barley plants were separated into ears and straws. After drying, twenty ears from each plot were separated into kernels and remains in order to estimate the grain yield production. The ears consisted of about 82% grains and the grain yield (15% water content) was estimated as grain yield = (0.82 × ear weight) × 1.15. All data were calculated to density (shoots m − 2 ) and aboveground dry matter (DM) (g m − 2 ) before statistical analysis. 2.2. Experiment 3 2.2.1 Study site, experimental design and treatments Experiment 3 was performed from 2016–2019 in Ås, Norway (59° 39’ 08.26 N 10° 50’ 12.58 E, altitude 96 m) at an experimental site established by Njøs and Hove ( 1986 ). The soil is a silty clay loam with 27% clay, 62% silt, 11% sand and 2.4% organic matter, and is described as an albeluvisol according to the WRB system (World Reference Base 2006 ). The area has been land levelled and the slope is 13%. Experiment 3 used a complete randomized block design with three blocks and plots measuring 21 x 8 m. The site had been growing small-grain cereals prior to experiments. Spring plowing, spring harrowing, levelling, fertilizing, seedbed preparation, cereal sowing and rolling were common for all experimental plots. Straw was left on the soil surface after harvest. The treatments were: 1) Untreated control, 2) Disc harrow 10 cm depth and 3), HRC 15 cm depth. Management dates can be found in Table 1 , weather data in Table 3 and details on the treatment machinery in Table 4 . 2.2.2 Assessment The surface runoff [soil, phosphorous (P), phosphates (Po4_p) and nitrogen (N)] was collected by a pipe system and the runoff was measured by tilting bucket. The number of tilts is recorded by a mechanical counter. Water sampling was volume proportional by storing a small volume of water from every second tilt in a plastic container. There were two containers for every plot so that both small runoff episodes (1–2 mm of runoff), and larger episodes (up to 50 mm of runoff) could be sampled. In our study surface runoff was measured from tillage operation in autumn to plowing in spring. Precipitation was recorded manually and was 312 mm in 2016–2017, 460 mm in 2017–2018 and 483 mm in 2018–2019. 2.3 Statistical analyses The three experiments are randomized complete block designs, Experiment 1 and 2 used four blocks and Experiment 3 used three blocks. The same plots within the blocks were observed in each of the years. In Experiment 1 transformed response variables were used in the analyses concerning weed number and weed biomass, to achieve approximate normality and equal variance. The transformation used was ln(y + 1) where y is the original response variable and ln(∙) is the natural logarithm function. For yield no transformation was used. In Experiment 2 and 3 the original response variables were used in the analyses without any transformation. All response variables were modelled using mixed linear models. For most response variables treatment and year were fixed factors, and significant interactions were also included in the model. The exception was the response variables E. repens and C. arvense number in Experiment 2 where time, with four levels, was used instead of year because there were two observation times in both 2018 and 2019. Several potential covariates were used, and depending on their significance the final models contain different covariates, in some situations no covariates. In all the models block was a random factor. To take into account that observations from the same plot can be correlated, an AR(1) covariance structure was used, except for the sum of E. repens and C. arvense biomass in Experiment 2 where a compound-symmetry covariance structure was used. To compare and order the least squares means of the levels of fixed effects Tukey-Kramer's multiple comparison method was used. The calculations were done using proc glimmix in SAS 9.4 (Sas Institute Inc., Cary. NC. USA.). 3. Results and discussion 3.1 Initial perennial weed abundance and yearly variation In the pre-treatment sampling, there were on average 160 E. repens and 168 S. arvensis shoots m − 2 in Experiment 1, and 108 E. repens , 5.4 C. arvense and 7.1 C. album plants m − 2 in Experiment 2. Stachys palustris , V. cracca and C. arvense in Experiment 1 were not counted pre-treatment. There was a great deal of yearly variation within the data, but almost no interactions between year and treatment (Table 5 – 8 ). One major factor for the yearly variation was the 2018 summer drought, which resulted in a much lower yield (1983 vs. 3005 kg/ha) in 2018 than 2019 in Experiment 2, but not Experiment 1, and resulted in a lower perennial weed biomass for all species in both Experiment 1 and 2, except C. arvense . Perennial weed shoot numbers were not likewise affected by the 2018 drought, instead the pattern varied depending on species, for example in Experiment 1 the number of S. arvensis shoots increased on average each year, while E. repens had on average significantly more shoots in 2017 than in 2018 and 2019. In Experiment 3, the surface runoff, soil erosion and P leaching were all greatest in the 2017–2018 period, for example 862 kg ha − 1 soil was lost on average in 2017–2018 compared to 369 kg ha − 1 in 2016–2017 and 135 kg ha − 1 in 2018–2019. 3.2 Effect of tillage treatments on crop yield and perennial weed abundance In Experiment 1, the rotary tiller resulted in the highest cereal yield, 40% higher than the untreated control and approximately 27% higher than the mowed or mowing + VRC treatments (Fig. 2 A; Table 5 ). The disc harrow (+ 30%), HRC at 7 cm depth (+ 28%), VRC + HRC (+ 33%) and VRC + disc harrow (+ 36%) treatments also resulted in significantly higher cereal yields then the untreated control (Fig. 2 A). In Experiment 2 there was no significant difference in crop yield between treatments (Fig. 2 B; Table 5 ). In Experiment 1, the rotary tiller had the greatest effect on the total perennial weed biomass (-67%), compared to the untreated control, but the effect was not significantly greater than the VRC + HRC (-66%), VRC + disc harrow (-66%), HRC 15 cm (-51%), disc harrow (-51%) or HRC 7 cm (46%) treatments (Fig. 3 ; Table 6 ). Similar to crop yield, the stubble harrow, mowing and mowing + VRC treatments did not significantly reduce total perennial weed biomass. In Experiment 2 there was no significant difference between treatments for total perennial weed biomass, only for shoot numbers (Table 7 ). On a species level, in Experiment 1, the rotary tiller was the most suppressive of E. repens and S. arvensis , reducing E. repens biomass by 94% and shoot numbers by 88%, and S. arvensis biomass by 65% and shoot numbers by 45%, compared to the untreated control (Fig. 3 – 4 ; Table 6 – 8 ); though the VRC + disc harrow and VRC + HRC treatments had an almost identical effect on E. repens and S. arvensis except that they did not quite significantly reduce the number of E. repens shoots compared to the untreated control (Fig. 3 – 4 ). On its own, the disc harrow did not quite significantly reduce E. repens biomass or shoots numbers, but reduced S. arvensis biomass by 56% and shoot numbers by 36%, compared to the untreated control. The HRC treatments (7 cm and 15 cm depth) hovered around significantly reducing both E. repens and S. arvensis , with the 7 cm treatment significantly reducing S. arvensis biomass by 52% and the 15 cm treatment significantly reducing E. repens biomass by 80%, and while neither significantly reduced their shoot numbers on their own, the contrast between the two HRC treatments and the untreated control showed a significant reduction in both E. repens (-71%; P = 0.01) and S. arvensis (-26%; P = 0.004) shoot numbers. In Experiment 2, the disc harrow reduced the number of E. repens shoots by 75% compared to the untreated control, while the HRC 7 cm reduced the number of C. arvense shoots by 71% (Fig. 4 B); the same pattern could be seen for the perennial weed biomass, but without significant differences (Fig. 3 B). In Experiment 1, S. palustris appears to have increased in most treatments that suppressed E. repens and S. arvensis – albeit only significantly for the rotary tiller, which had a higher number of shoots than the mowed treatment (9.1 vs. 2.3 plants/m 2 , respectively; Fig. 4 A; Table 7 ). There are not many studies on S. palustris , but it has sometimes been reported as being especially tolerant of tillage (e.g. Korsmo 1954 ) which, in combination with the decrease in its competitors, could explain its increase in the tilled treatments that reduced E. repens and S. arvensis . There was no significant treatment effect on V. cracca in Experiment 1 (Table 6 – 8 ), which may be explained by relatively patchy occurrence of V. cracca in the field compared to E. repens and S. arvensis . Hypothesis, which stated the RCs would result in significantly less perennial weed biomass and shoot numbers, and higher crop yield, than the untreated control and mowing, but would not have a significantly worse effect that more intensive tillage methods (i.e. disc harrows, stubble harrows and rotary tillers), was mostly supported for the HRC, but only partly for the VRC. Overall, the HRC treatments reduced the weed biomass and/or shoot numbers of E. repens , S. arvensis and C. arvense compared to mowing and the untreated control and was almost never significantly worse than the more intensive tillage treatments, with the clearest exception being the failure to control E. repens in Experiment 2 despite an 80% reduction of E. repens shoot biomass in Experiment 1. One reason for this failure could be because autumn plowing was used in Experiment 2 (compared to spring plowing in Experiment 1), which means that the plowing was conducted relatively soon after the HRC was used – potentially reducing the impact of the HRC treatment. A not yet published experiment testing different combinations of HRC and plowing at different depths showed that using both HRC and plowing at the same depth had no additive effects on C. arvense , most likely because the effect was too similar (Brandsæter et al. Unpublished). Previous studies have shown that plowing time has a relatively minor importance on E. repens , while spring plowing is more effective than autumn plowing on C. arvense and S. arvensis (Brandsæter et al. 2017 ); but this was only investigated with or without disc harrowing, so it is possible that the HRC followed by autumn plowing is less effective against E. repens than when combined with spring plowing. Despite the failure to control E. repens in Experiment 2, however, the HRC showed that it can have a strong reductive effect on E. repens , S. arvensis and C. arvense , supporting previous studies that fragmenting the roots/rhizomes of perennial weeds has a negative effect on their growth and propagation (e.g. Thomsen et al. 2013 ; Ringselle et al. 2018 ). One limitation of this study is that it only studied the aboveground biomass, which may differ significantly from the effect on the belowground biomass (cf. Ringselle et al. 2015 ), but this is compensated by the 2–3 year duration of the experiments. No treatment with VRC (Mow + VRC, HRC + VCR, disc harrow + VCR) in Experiment 1 reduced E. repens or S. arvensis biomass or numbers significantly more than the corresponding treatment without VRC (mowing, HRC 7 cm, HRC 15 cm or disc harrow). However, contrasts between the two treatment groups showed that treatments with VRC reduced total perennial weed biomass by 21% (P = 0.005), S. arvensis shoot numbers by 19% (P = 0.03) and shoot biomass by 22% (P = 0.03), and E. repens shoot biomass by 40% (P = 0.04) (though not E. repens shoot numbers (P = 0.2)), compared to treatments without VRC. These results support Hypothesis 2, which stated that integrating the VRC with other control methods would increase the efficacy against perennial weeds. This is the first time that the VRC has been shown to have a reductive effect on S. arvensis , as previous studies have focused on E. repens ; and can, together with the effect of the HRC, be contrasted with previous work that show a relatively limited effect of root fragmentation on S. arvensis growth and reproduction (e.g. Anbari et al. 2011 , 2016a , b ). A reduction of 40% shoot biomass of E. repens corresponds well with the 38% shoot biomass reduction achieved with VRC in Ringselle et al. ( 2018 ), even though the treatments in Experiment 1 were performed in autumn and only in one direction rather than, as in Ringselle et al. ( 2018 ), being performed in early summer in a crisscross pattern. Bergkvist et al. ( 2017 ) found that, at least for E. repens , rhizome fragmentation in a crisscross pattern is much more effective in early summer than in autumn. Brandsæter et al. ( 2020 ) found that the VRC did not significantly reduce E. repens shoot biomass when performed in one direction in autumn. The results provided little to no support to Hypothesis 3, which stated that shallow root/rhizome fragmentation would be more effective against perennial weed species with relatively shallow roots/rhizomes, and deeper fragmentations more effective against those with relatively deep roots. There was little to no difference between the two HRC treatments (7 and 15 cm) in either Experiment 1 and 2 in their effect on perennial weed biomass or shoot numbers, or crop yield. However, the VRC had a higher effect on E. repens than on S. arvensis , but this could either be because fewer S. arvensis roots were fragmented, or that the fragmentation had a lesser effect on S. arvensis . Two factors may have contributed to the minimal difference between the HRC treatments of different depths: 1) the experiments were conducted in a plowed system, and 2) even the deeper treatment was not that deep. In untilled systems E. repens rhizomes grow relatively close to the soil surface, while they are distributed down to the plowing depth in plowed systems (Lemieux et al. 1993 ). Thus, in a plowed system a 15 cm HRC treatment would still affect many E. repens and S. arvensis roots/rhizomes but in a plowless system it might fragment fewer roots/rhizomes, especially for E. repens . A deeper HRC treatment, for example 25 cm depth, might have resulted in a greater contrast in treatment effects as, even in a plowed system, it would likely affect fewer E. repens and S. arvensis roots/rhizomes but still be likely to be effective against the more deeper-rooted C. arvense . 3.3 Effects on soil erosion and leaching The results showed clear support for Hypothesis 4, which stated that using the HRC in the cereal stubble will not increase the water, soil and nutrient losses compared to an untreated control, but will result in significant lower losses than using a disc harrow. In Experiment 3, the HRC 15 cm treatment did not result in a higher level of water surface runoff, soil loss or nutrient leaching of P, N or Po4_p compared to the untreated control, and resulted in 60% less soil loss and a tendency (p = 0.06) towards 52% lower P leaching, than the disc harrow (Tables 5 & 8 ). The results are limited to one site and its soil type/environment, but the fact that it is a three-year experiment, and the clearness of the results (i.e. no indication of increase in any measure compared to the untreated control), provides a good indication that the results may be generally applicable, but more studies under more soil types and production systems are needed. Another limitation is that only one HRC treatment was tested in Experiment 3, using it once in autumn, while in Experiment 1–2, the stubble treatments were sometimes repeated – which is often the case and could potentially increase the risk of soil and nutrient losses. Yet even multiple HRC treatments would most likely have a lower risk than multiple treatments of more intensive tillage such as disc harrowing. For example Aronsson et al. ( 2015 ) found that a single duckfoot cultivation in autumn increased N leaching compared to the untreated control (20 vs. 17 kg N ha − 1 ), and that two duckfoot cultivations or two disc harrow cultivations increased the N leaching even further to 26 kg N ha − 1 . Moreover, unlike disc harrowing, the HRC can be performed in a cover crop without killing it, and combining the HRC with a cover crop could further reduce soil and nutrient losses. 3.4 Implications for management The results show that in a plowed system the HRC can provide a control efficacy of multiple perennial weed species ( E. repens , S. arvensis , C. arvense ) that is comparable to more intensive tillage methods such as disc harrowing, stubble harrowing and rotary tillage, but with a much lower low risk of water, soil and nutrient losses. Thus, the HRC is a clear alternative to more intensive tillage for achieving a more environmentally friendly control of perennial weeds in plowed systems, for example in organic farming. The results from these experiments indicate that in a plowed system the HRC depth does not need to be adapted to suit different perennial weed species, but further studies on HRC depth are needed. The influence of autumn vs. spring plowing may also need to be studied further, as plowing time may affect the HRC differently than other tillage methods. With its minimal disturbance of the soil and soil cover, and possibility to combine with cover crops, the HRC is likely to be very relevant for conservation agriculture, regenerative agriculture and other farming systems that eliminate or strongly reduce the use of tillage, especially plowing. For these systems there are still many questions that need answering, such as how effective the HRC is against perennial weeds when it is not followed by plowing, how effective the HRC is as a part of integrated strategies such as combining it with cover crops, mowing and the VRC, and how the treatments must be adapted to a plowless system. For instance, with E. repens rhizomes growing closer to the surface in untilled systems (Lemieux et al. 1993 ) it may be necessary to use shallow HRC treatments to control E. repens in these systems, but this could be far more damaging to a cover crop than a deeper HRC treatment. The fragmentation effect may also be less on roots/rhizomes growing closer to the soil as less energy is needed to reach the soil surface (Håkansson 1967 ), but this may be compensated for by the use of a cover crop and/or mowing (Kolberg et al. 2018 ); and it is possible that since less tilled soils can have a greater soil health with a higher degree of soil organic carbon, higher microbial and fungal activity and a different microbial community (Krauss et al. 2020 ) that fragmented roots/rhizomes would break down faster than in plowed soils. Moreover, the shallow rhizomes could also make E. repens more susceptible to VRC treatments, increasing the synergy from applying both HRC and VRC. Some of these questions have been studied in the AC/DC-weeds-project ( Applying and Combining Disturbance and Competition for an agro-ecological management of creeping perennial weeds ) that ran 2019–2022 and/or will be studied in the SUSWECO-project ( Sustainable weed control in cereals by combining subsidiary crops and minimal soil disturbances ) that will run 2023–2027. The results that have been published from AC/DC-weeds so far show that, without plowing, up to six HRC treatments may be needed to achieve sufficient control of C. arvense (Weigel and Gerowitt 2022 ), yet only two HRC treatments combined with a cover crop was sufficient to reduce C. arvense expansion to the same degree as plowing (Weigel et al. 2023 ). The results show that even though the VRC did not provide very high suppression of perennial weeds (which is congruent with previous results), when combined with other tillage methods (e.g. HRC, disc harrow), it increased the efficacy – with the VRC + HRC and VRC + disc harrow treatments being almost indistinguishable from the far more intensive rotary tiller in their effect. That the effect was achieved when only running in one direction, rather than as a crisscross patterns (as in e.g. Ringselle et al. 2018 , 2023 ) increases the potential of the VRC as a control method as running it twice to achieve a crisscross pattern is very labor intensive and disturbs the soil more. More experiments where the VRC is used, preferably in the spring, as a component of an integrated strategy, would be desirable. For example, a spring VRC application in a cereal crop followed by post-harvest HRC applications in a cover crop could potentially be a very potent strategy to control multiple perennial weed species with a very low risk of soil erosion and nutrient leaching. The experiments have shown that the RCs can be effective against multiple perennial weed species with different traits. It can be hypothesized that they would be effective against other perennial weed species that are negatively affected by rhizome/root fragmentation, such as Calystegia sepium (L.) R.Br. (hedge bindweed) (Rask and Andreasen 2007 ), and Cyperus aromaticus (L.) (Navua sedge) (Chadha et al. 2022 ), but it would have to be tested to be confirmed. It would be especially interesting to test the RCs against perennial weed species with other types of underground storage organs such as Cyperus esculentus L. (Yellow nutsedge), which has rhizomes and tubers (Feys et al. 2023 ), Cynodon dactylon (L.) Pers (Bermudagrass), which has rhizomes and stolons (Soares et al. 2023 ), and against Rumex obtusifolius L. (Broad-leaved dock) which has a large taproot (Ringselle et al. 2019 ). 4. Conclusions The HRC can significantly reduce the biomass and/or shoot numbers of multiple perennial weed species ( E. repens , S. arvensis , C. arvense ) and increase the crop yield compared to an untreated or mowed control. Overall, the effect of the HRC is not worse than more intensive tillage methods such as disc harrow, stubble harrow or rotary tiller. Moreover, the HRC does not increase soil, water and nutrient losses compared to an untreated control, and results in less soil loss and P leaching than the use of a disc harrow. In this study, using a HRC treatment depth of 7 or 15 cm did not differ for E. repens , S. arvensis or C. arvense control. Overall, treatments that integrated the VRC reduced E. repens and S. arvensis shoot biomass and shoot numbers more than treatments without VRC but was more effective against E. repens than S. arvensis . Novelly, the results show that the HRC can provide a much-needed soil friendly alternative to more intensive tillage to control perennial weeds in plowed systems, for example in organic agriculture. The results also show the VRC’s potential to increase the efficacy of integrated strategies against multiple perennial weed species, and that it can seemingly be effective even if run only in one direction rather than in a crisscross pattern. In addition to organic farming, the RCs show great potential to be used for perennial weed control in plowless systems to reduce these systems’ reliance on herbicides, but more studies are needed in such systems. Declarations Funding Majority of the funding was provided by the project “Rootcutter – Innovative Technology for Weed Control” (project no. 256441/E50) which was funded by the Norwegian Research Council through the BIONÆR-Bionæring program. The HRC and VRC prototypes were provided by the Kverneland Group as in-kind. Additional minor funding was provided by the C.F. Lundström foundation (project no. CF2023-0010) and the Swedish Research Council FORMAS to work on the scientific publication. Conflicts of interest The authors declare that there are no conflicts of interest, however for the sake of transparency, please note that Björn Ringselle is an associate editor at the journal of Agronomy for Sustainable Development. Ethics approval Not applicable. Consent to participate Not applicable. Consent to publication Not applicable. Availability of data and material Data will be provided upon request. Code availability Not applicable. Authors contributions Lars Olav Brandsæter (LOB), Trond Børresen (TB), Kjell Mangerud (KJ), Anneli Lundkvist (AL) and Theo Verwijst (TW) acquired the major funding and contributed to the study conception and design. Björn Ringselle (BR) acquired the minor funding. LOB, TB and KJ had the main responsibility for Experiment 1 and 3 and AL and TW the main responsibility for Experiment 2. Torfinn Torp (TT) conducted all statistical analyses. Øystein Skagestad (ØS) collated the management information and wrote a first draft of the introduction and M&M, while BR completed the manuscript and produced all figures. All authors except KJ (deceased) commented on the final draft. BR acted as corresponding author. References Adeux G, Vieren E, Carlesi S, et al (2019) Mitigating crop yield losses through weed diversity. 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A second stubble treatment was performed if the perennial weeds had had sufficient time to reach their compensation stage and the soil conditions allowed for tillage (e.g. not too wet, as was the case in Experiment 2 in 2017). Experiment Operation 2016 2017 2018 2019 1 Spring plowing, all plots 11 May 26 May 22 May 23 Apr Establishment (leveling, fertilizing, seedbed preparation, rolling, sowing*), all plots 12 May 27 May 22 May 24-25 Apr Threshing, all plots 27 Aug 20 Sep 26 Aug 2 Sep Main registration - Weeds 24-31 Aug 21-28 Sep 27 Aug-3 Sep 9-13 Sep First stubble treatment (HRC, VRC, disc harrow, stubble harrow, rotary tiller, mowing) 7 Sep 28-29 Sep 6-7 Sep Second stubble treatment (HRC, VRC, disc harrow, stubble harrow, rotary tiller, mowing) 26-27 Sep Not applied Not applied 2 Establishment (leveling, fertilizing, seedbed preparation, rolling, sowing**), all plots 13 May 13 May 2 May Main registration - Weed shoots 14-16 Aug 2-3 Jul 8-9 Aug Main registration - Grain / weed biomass / 2 nd weed shoots 1-2 Sep 21-28 Aug 1 Sep Threshing, all plots 18 Sep 1 Sep First stubble treatment (HRC, disc harrow, stubble harrow) 4 Oct 3 Sep Second stubble treatment (HRC, disc harrow, stubble harrow) Not applied 26-27 Sep Autumn plowing, all plots 15 Oct 24 Oct 16 Oct 3 Spring plowing, all plots 25 Apr 23 May 30 Apr Establishment (fertilizing, seedbed preparation, rolling, sowing***), all plots 10 May 24 May 11 May Threshing, all plots 28 Sep 25 Sep 21 Sep Stubble treatment (HRC, disc harrow) 25 Oct 20 Sep 28 Sep *Spring barley (2016: cv. “Thule” 210 kg/ha, 2017: cv. “Brage” 200 kg/ha), Oats (2018: cv. “Niklas” 220 kg/ha, 2019: cv. “Niklas” 230 - 240 kg/ha) **Spring barley 400 kernels/plot, all years ***Spring oat 210 kg/ha, all years Table 2. Records of monthly precipitation, temperature [taken from NMBU weather station SN17850 (NCCS 2023)] and radiation [taken from weather station Ås (LMT 2023)] for 2016–2019 in Ås, Norway, where both Experiment 1 and 3 took place. Mean temperature (°C) Radiation (∑ MJ m -2 ) Precipitation (∑ mm) 2016 2017 2018 2019 Mean* 2016 2017 2018 2019 Mean* 2016 2017 2018 2019 Mean* Jan -6.8 -1.4 -2.3 -4.2 -2.8 32 32 27 37 33 59 61 88 26 66 Feb -1.3 -1.9 -4.7 0.2 -2.5 104 94 94 78 95 78 63 57 97 50 Mar 2.1 2.1 -3.5 1.8 0.6 190 201 241 220 224 57 43 23 84 45 Apr 5.5 4.6 5.2 7.7 5.4 313 359 375 434 356 69 44 30 19 50 May 11.8 11.1 15.1 9.7 10.7 496 391 595 455 490 72 67 34 111 62 Jun 15.9 14.5 17.0 14.5 14.5 565 512 640 474 553 80 95 86 126 77 Jul 16.4 16.1 20.5 17.0 16.7 512 530 621 541 509 69 41 29 52 82 Aug 14.8 14.6 15.5 16.2 15.7 378 399 380 367 394 135 133 55 101 96 Sep 14.3 11.6 12.2 11.0 11.5 265 179 252 222 251 37 122 129 184 90 Oct 5.4 6.7 6.8 5.0 6.1 102 118 123 107 110 25 139 44 128 105 Nov 0.5 1.3 3.0 0.2 1.8 40 51 29 32 39 79 101 134 134 99 Dec 0.7 -2.0 -1.8 0.0 -2.0 19 21 19 19 19 26 66 85 71 72 *means for the period 1991–2020 Table 3. Records of monthly precipitation, temperature, and radiation for 2017–2019 at Ultuna, Sweden, where Experiment 2 was conducted, compared with means for the period 1991–2020 (Anonymous 2019; SMHI 2023). Mean temperature (°C) Radiation (∑ MJ m -2 ) Precipitation (∑ mm) 2017 2018 2019 Mean* 2017 2018 2019 Mean* 2017 2018 2019 Mean* May 10.5 15.4 10.2 10.7 741 746 540 586 17 8 54 38 June 14.7 16.4 17.6 14.9 703 671 678 616 53 9 24 58 July 16.5 21.7 16.6 17.6 716 703 604 607 32 78 55 59 Aug 15.9 17.9 17.1 16.4 469 486 494 473 32 64 61 67 Sept 12.2 12.9 11.9 11.8 233 301 302 290 41 37 37 48 Oct 6.9 7.5 6.1 6.4 122 149 124 133 57 20 68 52 *means for the period 1991–2020 Table 4. Details of the machines and implements used in Experiments 1-3 Experiment Model name Tilling implement PTO (rpm) Forward Speed (km h-1 ) 1-3 Kverneland Horizontal Root/Rhizome Cutter (HRC) (prototype) (2.5 m) 54 cm wide flat shares like a goosefoot share, cuts the roots/rhizomes to an even depth throughout the whole width. - 7 1 Kverneland Vertical Root/Rhizome Cutter (VRC) (prototype) 1.5 m) The 36 cm diameter discs make cuts 10 cm apart. - 5 1 Rotary tiller Howard L-tine Cultivator. Vertically aligned PTO-driven L-tines 1000 5 1 Kverneland FH 180 Chopper Stubble and pasture mower. 540 5-7 1 & 3 Kverneland Disc Harrow Disc diameter 35 cm. Kverneland, Norway. - 5-6 2 Stubble Harrow Goosefoot shares - 5-6 2 Väderstad Carrier disc cultivator (4.25 m) Disc diameter 45 cm. - 2 Väderstad Swift Cultivator (4 m) Goosefoot shares, width 24 cm - Table 5 . ANOVA-table of the effects of treatment and year on crop yield in Experiment 1 and 2, and surface runoff, soil loss, phosphorous (P), Po4_p (phosphate) and nitrogen (N) leaching in Experiment 3. Initial number (No) of Elymus repens shoots was used as a covariate for crop yield as it was significant. LN = natural logarithm. Experiment 1 Experiment 2 Experiment 3 Crop yield (kg ha -1 ) Crop yield (kg ha -1 ) Surface runoff (mm) Soil loss (kg ha -1 ) P loss (kg ha -1 ) Po4_p (kg ha -1 ) Total N (kg ha -1 ) Treatment <.0001 0.3 0.5 0.04 0.06 0.7 0.2 Year <.0001 <.0001 0.0002 0.0002 <.0001 0.01 0.9 Year*Treatment 1 0.3 1 0.5 0.3 0.3 1 ElymusNo 0.01 0.005 Transformation LN LN LN LN Table 6 . ANOVA-table of the effects of treatment and year on the shoot biomass of Elymus repens , Sonchus arvensis , Stachys palustris , Vicia cracca and the total perennial weed shoot biomass (all four species plus C. arvense ) in Experiment 1, and on the shoot biomass of E. repens , C. arvense and the total perennial weed shoot biomass in Experiment 2. Different covariates of initial dry weight (DW) or shoot numbers (No) of different perennial weed species were used depending on whether they had a significant effect. LN = natural logarithm. Shoot biomass Experiment 1 Experiment 2 Elymus repens Sonchus arvensis Stachys palustris Vicia cracca Total per. weed biomass Elymus repens Cirsium arvense Total per. weed biomass Treatment <.0001 <.0001 0.2 0.2 <.0001 0.09 0.5 0.6 Year <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.2 0.003 Year*Treatment 0.04 0.1 0.3 0.3 0.1 ElymusDW 0.0002 0.04 SonchusNo 0.001 0.009 ElymusNo 0.0002 0.04 CirsiumNo 0.1 Transformation LN+1 LN+1 LN+1 LN+1 LN+1 Table 7 . ANOVA-table of the effects of treatment and year/time on the shoot numbers of Elymus repens , Sonchus arvensis , Stachys palustris , and Vicia cracca in Experiment 1, and on E. repens and C. arvense in Experiment 2. For Experiment 2 time was used rather than year as shoot numbers were counting twice each year for a total of four times over two years. Different covariates of initial dry weight (DW) or shoot numbers (No) of different perennial weed species were used depending on whether they had a significant effect. LN = natural logarithm. Weed numbers Experiment 1 Experiment 2 Elymus repens Sonchus arvensis Stachys palustris Vicia cracca Elymus repens Cirsium arvense Treatment 0.004 <.0001 0.02 0.4 0.02 0.04 Year/Time <.0001 <.0001 <.0001 0.2 <.0001 0.002 ElymusDW 0.003 SonchusNo 0.002 0.0006 0.1 ElymusNo 0.0002 CirsiumNo 0.003 Transformation LN+1 LN+1 LN+1 LN+1 Table 8 . The effect of the three different treatments between 2016-2019 in Experiment 3 on surface runoff, soil loss, phosphorous (P), Po4_p (phosphate) and nitrogen (N) leaching. HRC = Horizontal root/rhizome cutter. ± show the confidence intervals at 95%. Surface runoff (mm) Soil loss (kg/ha) P loss (kg/ha) Po4_p (kg/ha) N loss (kg/ha) Disc harrow 10 cm depth 213 ±186 a 449 -341/+1419 a 0.75 -0.48/+1.37 a 0.07 -0.03/+0.04 a 3.4 -2.7/+14.6 a Untreated control 193 ±186 a 228 -173/+720 ab 0.46 -0.3/+0.85 a 0.06 -0.02/+0.03 a 2.0 -1.7/+8.9 a HRC 15 cm depth 180 ±186 a 183 -139/+578 b 0.36 -0.23/+0.66 a 0.06 -0.02/+0.03 a 1.7 -1.4/+7.4 a P-value 0.5 0.04 0.06 0.7 0.2 Additional Declarations The authors declare no competing interests. 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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-3827798","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264980723,"identity":"fad831b0-a968-47a2-86a3-f2970e01e02c","order_by":0,"name":"Björn Ringselle","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACA4YDYJoHTD4oYJBjYCZJS4IBgzERWpABUEtiAyGHmTMePva5gOGejDn/8ocPEgxs0re3MzB/+IBHi2XDseTZMxiKeSxnvDE2SDBIy51zmIFNcgY+hx04Y8zMw5DAY3DjDJtEgsHh3BnMDGzMPMRpOf78R4LB/3QJZgbmz3+I0nK+wQzo/QMJQC0M0vi8D/ILM48ByBYeY6DDkg1nMDO2Sfbg0WIucfgwM09Fgr3B+eMPP3yosJOX4D98+MMPfNZIHGCAxI5EAkyIsQGfBgYGfpg8/wH8CkfBKBgFo2DkAgCWJ0csKvaTgwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-7081-1277","institution":"RISE Research Institutes of Sweden","correspondingAuthor":true,"prefix":"","firstName":"Björn","middleName":"","lastName":"Ringselle","suffix":""},{"id":264980724,"identity":"874a8c8e-9d3f-4425-b041-b6d6a0ff4e1a","order_by":1,"name":"Trond Børresen","email":"","orcid":"","institution":"Norwegian University of Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Trond","middleName":"","lastName":"Børresen","suffix":""},{"id":264980725,"identity":"84704001-a856-457b-ae3f-68ba33debba1","order_by":2,"name":"Anneli Lundkvist","email":"","orcid":"https://orcid.org/0000-0001-5310-7205","institution":"Swedish University of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Anneli","middleName":"","lastName":"Lundkvist","suffix":""},{"id":264980726,"identity":"a040c03c-5284-4362-b18a-f7c8a366d5b7","order_by":3,"name":"Kjell Mangerud","email":"","orcid":"","institution":"Norwegian Institute of Bioeconomy Research","correspondingAuthor":false,"prefix":"","firstName":"Kjell","middleName":"","lastName":"Mangerud","suffix":""},{"id":264980727,"identity":"84dd5c50-bb27-4776-8cdd-b7dff8746b91","order_by":4,"name":"Øystein Skagestad","email":"","orcid":"","institution":"Norwegian University of Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Øystein","middleName":"","lastName":"Skagestad","suffix":""},{"id":264980728,"identity":"8e9b59a3-90fc-429f-8e2c-aa517719770f","order_by":5,"name":"Torfinn Torp","email":"","orcid":"https://orcid.org/0000-0003-4834-6353","institution":"Norwegian Institute of Bioeconomy Research","correspondingAuthor":false,"prefix":"","firstName":"Torfinn","middleName":"","lastName":"Torp","suffix":""},{"id":264980729,"identity":"3db8a0cf-1ba6-4d5e-8bfd-f52eaff68a3d","order_by":6,"name":"Theo Verwijst","email":"","orcid":"https://orcid.org/0000-0003-3991-4849","institution":"Swedish University of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Theo","middleName":"","lastName":"Verwijst","suffix":""},{"id":264980730,"identity":"c17f62bc-63c2-4dc3-aa54-6d254e8b5ce0","order_by":7,"name":"Lars Olav Brandsæter","email":"","orcid":"https://orcid.org/0000-0002-0878-9128","institution":"Norwegian University of Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lars","middleName":"Olav","lastName":"Brandsæter","suffix":""}],"badges":[],"createdAt":"2024-01-01 16:23:44","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3827798/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3827798/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13593-025-01059-6","type":"published","date":"2025-11-06T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49725396,"identity":"6a19c1bd-f46d-4e78-9076-b81f6762c486","added_by":"auto","created_at":"2024-01-17 04:08:59","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1390453,"visible":true,"origin":"","legend":"\u003cp\u003eThe vertical root/rhizome cutter (VRC) used in soil (A) and in a growing crop (B); and the\u003cem\u003e \u003c/em\u003ehorizontal root/rhizome cutter (HRC) as schematic (C) and working in the field (D).\u003cem\u003e \u003c/em\u003eThe soil disturbance from both VRC and HRC is minimal compared to conventional tillage, in particular compared to moldboard plowing and rotary tilling.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3827798/v1/a7a1db8c0a466d4f826e0f7b.jpeg"},{"id":49725970,"identity":"0f8ac430-a9ff-4367-aa2f-b436f022521c","added_by":"auto","created_at":"2024-01-17 04:16:58","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":285779,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eweed biomass. \u003c/strong\u003eResults from A) Experiment 1 in Norway on the effect of different treatments on the aboveground shoot dry weight (DW) biomass of \u003cem\u003eE. repens\u003c/em\u003e, \u003cem\u003eS. arvensis\u003c/em\u003eand the combined shoot biomass of all five perennial weed species present at the site (\u003cem\u003eE. repens\u003c/em\u003e, \u003cem\u003eS. arvensis\u003c/em\u003e, \u003cem\u003eS. palustris\u003c/em\u003e, \u003cem\u003eV. cracca\u003c/em\u003e, \u003cem\u003eC. arvense\u003c/em\u003e), averaged over the sampling years 2017-2019; and B) Experiment 2 in Sweden on the effect of different treatments on the shoot DW biomass of \u003cem\u003eC. arvense\u003c/em\u003e and \u003cem\u003eE. repens\u003c/em\u003e, average over the sampling years 2018-2019. All treatments were plowed in spring each year in Experiment 1, and in autumn in Experiment 2. VRC = vertical root/rhizome cutter, HRC = Horizontal root/rhizome cutter. Letters show the result of Tukey test at α =0.05.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3827798/v1/16ff7353d5ca17f518d4a7b9.jpeg"},{"id":49725397,"identity":"900cd9a8-bb0f-4fdc-8686-8fb2cc439013","added_by":"auto","created_at":"2024-01-17 04:08:59","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":547568,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eweed numbers. \u003c/strong\u003eResults from A) Experiment 1 in Norway on the effect of different tillage treatments on the shoot numbers of \u003cem\u003eE. repens\u003c/em\u003e, \u003cem\u003eS. arvensis\u003c/em\u003e and \u003cem\u003eS. palustris\u003c/em\u003e, averaged over the sampling years 2017-2019; and B) Experiment 2 in Sweden on the effect of different treatments on the shoot numbers of \u003cem\u003eCirsium arvense\u003c/em\u003eand \u003cem\u003eE. repens\u003c/em\u003e, average over the sampling years 2018-2019. All treatments were plowed in spring each year in Experiment 1, and in autumn in Experiment 2. VRC = vertical root/rhizome cutter, HRC = Horizontal root/rhizome cutter. Letters show the result of Tukey test at α =0.05.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3827798/v1/36b9176e036f204bea520aef.jpeg"},{"id":49725394,"identity":"8905d226-57a9-4eac-a43a-f2d9969d6e0d","added_by":"auto","created_at":"2024-01-17 04:08:58","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":505966,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCrop yield. \u003c/strong\u003eResults of different treatments on crop yield in A) Experiment 1 in Norway, averaged over the sampling years 2017-2019; and B) Experiment 2 in Sweden, average over the sampling years 2018-2019. All treatments were plowed in spring each year in Experiment 1, and in autumn in Experiment 2. VRC = vertical root/rhizome cutter, HRC = Horizontal root/rhizome cutter. Letters show the result of Tukey test at α =0.05.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3827798/v1/fe4d9099d361060931981fa9.jpeg"},{"id":95394762,"identity":"a3a7e11c-0331-4d81-b804-6072d2c72040","added_by":"auto","created_at":"2025-11-07 14:45:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4081318,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3827798/v1/5c2b6b15-831a-4062-82fe-41813e885333.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eRoot cutters: Novel tillage methods to control creeping perennial weeds with a low risk of soil erosion and nutrient leaching\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTo create a more sustainable future for humanity, it is imperative to reduce the ecological footprint of our production systems. The reduction or elimination of pesticide use and/or tillage is a common requirement or goal of most agricultural sustainability concepts [e.g., no pesticides allowed in organic farming (Migliorini and Wezel \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), reduced-till to no-till in conservation agriculture (Nichols et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), reduced or eliminated tillage and pesticide use in regenerative agriculture (Newton et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), priority of preventive measures over direct weed control in integrated weed management (IWM) (Riemens et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)] and programs aimed at increasing agricultural sustainability [e.g., the European Farm to Fork strategy, which aims to reduce pesticide use and nutrient losses in the EU by 50% by 2030 (Wesseler \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)]. For pesticides this is due to concerns over environmental persistence, groundwater pollution, effects on non-target organisms and toxicity to humans (Van Bruggen et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) \u0026ndash; and as a result many pesticides have been banned or had their use restricted within the EU. Tillage, on the other hand, can both be detrimental to the treated soil by e.g. removing the protective soil cover, destroying soil aggregates and damaging soil-living organisms, but also to the surrounding environment by increasing the risk of soil erosion and nutrient leaching (Klik and Rosner \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWeeds are the major obstacle for reducing pesticides and tillage. On average, the potential of weeds to cause yield losses are more than twice that of other pests such as insects, fungi and pathogens (Oerke \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). As a consequence, herbicides make up a large proportion of the pesticides sold, especially in cereal and grassland dominated regions such as northern Europe (Antier et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Tillage-tolerant creeping perennial weeds, such as \u003cem\u003eElymus repens\u003c/em\u003e (L.) Gould (couch grass), \u003cem\u003eCirsium arvense\u003c/em\u003e (L.) Scop. (creeping thistle) and \u003cem\u003eSonchus arvensis\u003c/em\u003e L. (perennial sow-thistle), are especially problematic to control without herbicides or tillage due to the persistence of their underground storage organs (e.g. thickened roots capable of producing shoots, taproots, tubers, rhizomes) (H\u0026aring;kansson \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; DiTommaso and Prostak \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn modern industrial agriculture, herbicides are the main control method for perennial weeds, especially glyphosate and other systemic herbicides that are transported down to their underground storage organs, thus killing the whole plant. However, glyphosate is currently facing increasing scrutiny within the EU, and while it was renewed again in 2023, a potential ban is continuously being discussed, which would severely limit the options of conventional farmers (Fogliatto et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), in particular their ability to control perennial weeds such as \u003cem\u003eE. repens\u003c/em\u003e, for which alternative herbicides are expensive and/or not allowed in sufficiently high doses for control (Ringselle et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe main alternatives to herbicides to control perennial weeds are using a combination of preventive measures (e.g. the inclusion of regularly mown competitive leys or cover crops, which can be effective against some perennial weed species (Thomsen et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)) and/or using intensive tillage (e.g., multiple harrowing operations to fragment the roots/rhizomes followed by moldboard plowing) (Ringselle et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Soares et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). An increased use of preventive measures would be beneficial to agricultural sustainability and create more diverse cropping systems which do not promote a few highly competitive weed species like monocultures do (Adeux et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, many effective preventive measures (e.g. leys) are underutilized due to low profitability or other agronomic concerns; and some perennial weed species such as \u003cem\u003eE. repens\u003c/em\u003e are difficult to control through preventive measures alone. Thus, because of problems with perennial weeds, efforts to reduce herbicide use usually ends up increasing tillage use and vice versa.\u003c/p\u003e \u003cp\u003eWhile some forms of tillage can have beneficial effects (e.g. reducing some plant diseases (Bankina et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and preparing the soil for the next crop), the intensive tillage used to control perennial weeds is energy and time-demanding, and can result in extended periods of bare soil, which increases the risk of soil erosion and nutrient leaching (Aronsson et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Klik and Rosner \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Intensive tillage is also difficult to combine with cover crops that could otherwise reduce nutrient leaching (Melander et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); and intensive tillage in spring can delay sowing, which often reduces the yield of spring-sown crops (Brands\u0026aelig;ter et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Perennial weed species also vary in the traits that affect their susceptibility to different control methods (e.g., underground storage organs occur at different soil depths and with varying degrees of dormancy (Liew et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)), which complicates control efforts as methods and timing often have to be adjusted to suit each species. There are some non-chemical alternatives to tillage that can destroy roots/rhizomes (e.g., steaming, electricity, solarization), but in general they are very energy demanding, slow and/or may not reach very deeply into the soil (Ringselle et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, there is a need for non-chemical tools that can effectively and resource-efficiently control perennial weeds with minimal soil disturbance and low risk of soil and nutrient losses.\u003c/p\u003e \u003cp\u003eThe Kverneland Group (together with researchers participating in a series of research projects, starting with the \u0026ldquo;Optimising Subsidiary Crop Applications in Rotations\u0026rdquo; (OSCAR) project) have developed two root/rhizome cutter (RC) prototypes that could potentially strike the golden balance between being able to fragment the roots/rhizomes of perennial weeds, but with minimal soil disturbance, and thus theoretically have a low risk of soil and nutrient losses. The first prototype, the vertical RC (VRC; see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B), cuts vertically through the soil using coulter disks and can reach 12 cm soil depth, meaning that it is likely to be most effective against species which roots/rhizomes are typically found in the upper part of the soil profile, such as \u003cem\u003eE. repens\u003c/em\u003e (Ringselle et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Brands\u0026aelig;ter et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The second prototype, the horizontal RC (HRC), cuts horizontally using wide shears to a maximum depth of 30 cm (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-D), but the depth can be adjusted as desired, making it potentially effective against both perennial weed species with shallow and deep roots/rhizomes, such as \u003cem\u003eC. arvense\u003c/em\u003e which roots can reach more than a meter into the soil (Favreli\u0026egrave;re et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe VRC has been tested against \u003cem\u003eE. repens\u003c/em\u003e in leys, showing that fragmenting its rhizome network once in a crisscross pattern can reduce \u003cem\u003eE. repens\u003c/em\u003e rhizome biomass by 38%, while twice can reduce it by 63% (Ringselle et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This supported previous results that have shown a large reductive effect of fragmenting the underground storage organs of perennial weed species (e.g., Bergkvist et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, the VRC treatment resulted in an increase in Italian ryegrass (\u003cem\u003eLolium multiflorum\u003c/em\u003e Lam.) and white clover (\u003cem\u003eTrifolium repens\u003c/em\u003e L.), and the beneficial effect on Italian ryegrass was higher when it was performed in the growing crop compared to prior to crop sowing (170% vs 78%). Further experiments in an established ley have shown that the VRC does not operate well under hard soil conditions (Ringselle et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The HRC has been shown to be able to reduce \u003cem\u003eC. arvense\u003c/em\u003e shoot numbers, patch expansion and root carbohydrate content, though in these studies it was not as effective as moldboard plowing (Weigel and Gerowitt \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Weigel et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSo far, no studies have been published that demonstrate the efficacy of the RCs in controlling multiple perennial weed species with different root/rhizome traits, nor how the RCs affect soil erosion or nutrient leaching. The current study will fill these gaps by presenting the results from a series of experiments from Norway and Sweden comparing the RCs\u0026rsquo; effect on multiple perennial weed species over multiple years compared to other tillage treatments (Experiment 1 \u0026amp; 2), the effect of combining the VRC with other tillage treatments (Experiment 1), and the effect of the HRC on soil erosion and nutrient leaching (Experiment 3). The tested hypotheses were that: 1) the RCs will result in significantly less perennial weed biomass and shoot numbers, and higher crop yield, than an untreated control and mowing, and will not have a significantly worse effect than more intensive tillage methods (i.e. disc harrows, stubble harrows and rotary tillers), 2) integrating VRC with other control methods (e.g. HRC, disc harrow, mowing) will increase the control efficacy against perennial weeds, 3) perennial weed species with relatively shallow roots/rhizomes such as \u003cem\u003eE. repens\u003c/em\u003e, \u003cem\u003eS. arvensis\u003c/em\u003e, \u003cem\u003eVicia cracca\u003c/em\u003e L. (tufted vetch) and \u003cem\u003eStachys palustris\u003c/em\u003e L. (marsh woundwort) (typical growth depth of the regenerative plant organs of these species is up to 10\u0026ndash;30 cm, with species ordered from most to least shallow), will be more affected by a shallow treatment (i.e. the VRC, or the HRC used at 7 cm depth), while perennial weed species with deeper roots/rhizomes such as \u003cem\u003eC. arvense\u003c/em\u003e will be more greatly reduced by a deeper treatment (i.e. the HRC used at 15 cm depth), and 4) using the HRC in the cereal stubble will not increase the water, soil and nutrient losses compared to an untreated control, but will result in significantly lower losses than using a disc harrow.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Experiment 1 and 2\u003c/h2\u003e\n \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\n \u003ch2\u003e2.1.1 Study sites, experimental design and treatments\u003c/h2\u003e\n \u003cp\u003eExperiment 1 was performed in \u0026Aring;s, Norway (59\u0026deg;66\u0026prime;N 10\u0026deg;76\u0026prime;E) from 2016\u0026ndash;2019. The soil at the \u0026Aring;s site is a silty clay loam with poor natural drainage and classified as an epistagnic albeluvisol (siltic), according to the WRB system (World Reference Base \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). The site had naturally established populations of \u003cem\u003eE. repens, S. arvensis, C. arvense, S. palustris\u003c/em\u003e and \u003cem\u003eV. cracca\u003c/em\u003e that dominated the weed flora, and there were no prominent annual weeds. All plots were fertilized with dried chicken manure [\u0026ldquo;Marih\u0026oslash;ne Pluss\u0026rdquo; 8 (%N) \u0026ndash; 4 (%P) \u0026ndash; 5 (%K)] corresponding to 80\u0026ndash;100 kg total N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The fields were sown with spring barley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.) in 2016 and 2017, and oat \u003cem\u003e(Avena sativa\u003c/em\u003e L.) in 2018 and 2019 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Weather data for the site of Experiment 1 and 3 is given in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eExperiment 2 was performed at Ultuna close to Uppsala, Sweden (59\u0026deg;48\u0026prime;N, 17\u0026deg;39\u0026prime;E) from 2017\u0026ndash;2019. The soil at the Ultuna site is a heavy clay soil, and classified as a vertisol, according to the WRB system (World Reference Base \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). The site had naturally established populations of \u003cem\u003eC. arvense\u003c/em\u003e and \u003cem\u003eE. repens.\u003c/em\u003e Only \u003cem\u003eChenopodium album\u003c/em\u003e L. (lamb\u0026rsquo;s quarters) was a prominent annual weed. Mineral fertilization was applied each year at sowing as NP 27\u0026thinsp;\u0026minus;\u0026thinsp;3 with a N-supply of 80 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The fields were sown with spring barley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.) in all experimental years. Weather data for the site of Experiment 2 are given in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eBoth Experiment 1 and 2 used complete randomized block designs with 4 blocks. Experiment 1 used 2 x 14 m plots, while Experiment 2 used 6 x 7 m plots. In Experiment 1, there were 2-meter margins between all plots, which were stubble-harrowed in the autumn to control weeds. Prior to the experiments, both sites had been organically farmed for many years with small-grain cereals dominating the rotation. Levelling, fertilizing, seedbed preparation, sowing and rolling were common for all experimental plots in both experiments. Additionally, spring plowing was used in all experimental plots in Experiment 1, and autumn plowing in Experiment 2 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), as this is common practice in Norway and Sweden, respectively.\u003c/p\u003e\n \u003cp\u003eThe following five treatments were performed in both Experiment 1 and 2: 1) Untreated control, 2) Disc harrow 12 cm depth, 3) Stubble harrow 12 cm depth, 4) HRC 7 cm depth, and 5) HRC 15 cm depth. In Experiment 1 an additional five treatments were performed: 6) Mowing, 7) Mowing\u0026thinsp;+\u0026thinsp;VRC 12 cm depth, 8) VRC 12 cm depth\u0026thinsp;+\u0026thinsp;HRC 12 cm depth, 9) VRC 12 cm depth\u0026thinsp;+\u0026thinsp;Disc harrow 12 cm depth, and 10) Rotary tiller 12 cm depth. Implement specifications for the treatments are provided in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. In both the VRC\u0026thinsp;+\u0026thinsp;HRC and VRC\u0026thinsp;+\u0026thinsp;Disc harrow treatments, the VRC was performed after the other tillage treatment. Timing of the treatments are given in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. A second stubble treatment was performed if the perennial weeds had had sufficient time to reach their compensation stage (Ringselle et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) there after the threshing and the soil conditions allowed for tillage (e.g., not too wet).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.1.2. Assessment\u003c/h2\u003e\n \u003cp\u003eAssessments were done in the autumn for both Experiment 1 and 2 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Experiment 1 was assessed using four 0.5 m\u003csup\u003e2\u003c/sup\u003e (thus, 2 m\u003csup\u003e2\u003c/sup\u003e in total per plot) randomly placed quadrants for all measurements. In the quadrants all shoots were counted and all perennial shoot biomass collected. The biomass samples were dried at 70\u0026deg;C for 72 h to determine the dry weight. An experimental combine harvested 1.5 m x 7 m in the middle of each plot. The grain yield of the plots was weighed at harvest and dried for storage. Grain moisture at harvest, grain weight per hectoliter and screening percentage were determined. Final grain yield was adjusted to 85% dry matter.\u003c/p\u003e\n \u003cp\u003eIn Experiment 2 shoot numbers were assessed using four 1 m\u003csup\u003e2\u003c/sup\u003e randomly placed quadrants (4 m\u003csup\u003e2\u003c/sup\u003e total per plot) for \u003cem\u003eE. repens\u003c/em\u003e, while \u003cem\u003eC. arvense\u003c/em\u003e and \u003cem\u003eC. album\u003c/em\u003e shoots were counted across the middle of the whole plot in a two-meter-wide strip (i.e., 14 m\u003csup\u003e2\u003c/sup\u003e). For shoot biomass four subplots with an area of 0.25 m\u003csup\u003e2\u003c/sup\u003e were randomly selected in each plot (thus, 1 m\u003csup\u003e2\u003c/sup\u003e in total per plot). All plant material was harvested, and separated into spring barley, \u003cem\u003eC. arvense\u003c/em\u003e, \u003cem\u003eE. repens\u003c/em\u003e, \u003cem\u003eC. album\u003c/em\u003e and other weeds. The plant material was dried at 105˚C until constant weight was achieved, and dry weight was recorded. Before drying, spring barley plants were separated into ears and straws. After drying, twenty ears from each plot were separated into kernels and remains in order to estimate the grain yield production. The ears consisted of about 82% grains and the grain yield (15% water content) was estimated as grain yield = (0.82 \u0026times; ear weight) \u0026times; 1.15. All data were calculated to density (shoots m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) and aboveground dry matter (DM) (g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) before statistical analysis.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Experiment 3\u003c/h2\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1 Study site, experimental design and treatments\u003c/h2\u003e\n \u003cp\u003eExperiment 3 was performed from 2016\u0026ndash;2019 in \u0026Aring;s, Norway (59\u0026deg; 39\u0026rsquo; 08.26 N 10\u0026deg; 50\u0026rsquo; 12.58 E, altitude 96 m) at an experimental site established by Nj\u0026oslash;s and Hove (\u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e). The soil is a silty clay loam with 27% clay, 62% silt, 11% sand and 2.4% organic matter, and is described as an albeluvisol according to the WRB system (World Reference Base \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). The area has been land levelled and the slope is 13%.\u003c/p\u003e\n \u003cp\u003eExperiment 3 used a complete randomized block design with three blocks and plots measuring 21 x 8 m. The site had been growing small-grain cereals prior to experiments. Spring plowing, spring harrowing, levelling, fertilizing, seedbed preparation, cereal sowing and rolling were common for all experimental plots. Straw was left on the soil surface after harvest. The treatments were: 1) Untreated control, 2) Disc harrow 10 cm depth and 3), HRC 15 cm depth. Management dates can be found in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, weather data in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and details on the treatment machinery in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2 Assessment\u003c/h2\u003e\n \u003cp\u003eThe surface runoff [soil, phosphorous (P), phosphates (Po4_p) and nitrogen (N)] was collected by a pipe system and the runoff was measured by tilting bucket. The number of tilts is recorded by a mechanical counter. Water sampling was volume proportional by storing a small volume of water from every second tilt in a plastic container. There were two containers for every plot so that both small runoff episodes (1\u0026ndash;2 mm of runoff), and larger episodes (up to 50 mm of runoff) could be sampled. In our study surface runoff was measured from tillage operation in autumn to plowing in spring. Precipitation was recorded manually and was 312 mm in 2016\u0026ndash;2017, 460 mm in 2017\u0026ndash;2018 and 483 mm in 2018\u0026ndash;2019.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Statistical analyses\u003c/h2\u003e\n \u003cp\u003eThe three experiments are randomized complete block designs, Experiment 1 and 2 used four blocks and Experiment 3 used three blocks. The same plots within the blocks were observed in each of the years. In Experiment 1 transformed response variables were used in the analyses concerning weed number and weed biomass, to achieve approximate normality and equal variance. The transformation used was ln(y\u0026thinsp;+\u0026thinsp;1) where y is the original response variable and ln(∙) is the natural logarithm function. For yield no transformation was used. In Experiment 2 and 3 the original response variables were used in the analyses without any transformation. All response variables were modelled using mixed linear models. For most response variables treatment and year were fixed factors, and significant interactions were also included in the model. The exception was the response variables \u003cem\u003eE. repens\u003c/em\u003e and \u003cem\u003eC. arvense\u003c/em\u003e number in Experiment 2 where time, with four levels, was used instead of year because there were two observation times in both 2018 and 2019. Several potential covariates were used, and depending on their significance the final models contain different covariates, in some situations no covariates. In all the models block was a random factor. To take into account that observations from the same plot can be correlated, an AR(1) covariance structure was used, except for the sum of \u003cem\u003eE. repens\u003c/em\u003e and \u003cem\u003eC. arvense\u003c/em\u003e biomass in Experiment 2 where a compound-symmetry covariance structure was used. To compare and order the least squares means of the levels of fixed effects Tukey-Kramer\u0026apos;s multiple comparison method was used. The calculations were done using proc glimmix in SAS 9.4 (Sas Institute Inc., Cary. NC. USA.).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Initial perennial weed abundance and yearly variation\u003c/h2\u003e\n \u003cp\u003eIn the pre-treatment sampling, there were on average 160 \u003cem\u003eE. repens\u003c/em\u003e and 168 \u003cem\u003eS. arvensis\u003c/em\u003e shoots m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e in Experiment 1, and 108 \u003cem\u003eE. repens\u003c/em\u003e, 5.4 \u003cem\u003eC. arvense\u003c/em\u003e and 7.1 \u003cem\u003eC. album\u003c/em\u003e plants m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e in Experiment 2. \u003cem\u003eStachys palustris\u003c/em\u003e, \u003cem\u003eV. cracca\u003c/em\u003e and \u003cem\u003eC. arvense\u003c/em\u003e in Experiment 1 were not counted pre-treatment.\u003c/p\u003e\n \u003cp\u003eThere was a great deal of yearly variation within the data, but almost no interactions between year and treatment (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). One major factor for the yearly variation was the 2018 summer drought, which resulted in a much lower yield (1983 vs. 3005 kg/ha) in 2018 than 2019 in Experiment 2, but not Experiment 1, and resulted in a lower perennial weed biomass for all species in both Experiment 1 and 2, except \u003cem\u003eC. arvense\u003c/em\u003e. Perennial weed shoot numbers were not likewise affected by the 2018 drought, instead the pattern varied depending on species, for example in Experiment 1 the number of \u003cem\u003eS. arvensis\u003c/em\u003e shoots increased on average each year, while \u003cem\u003eE. repens\u003c/em\u003e had on average significantly more shoots in 2017 than in 2018 and 2019.\u003c/p\u003e\n \u003cp\u003eIn Experiment 3, the surface runoff, soil erosion and P leaching were all greatest in the 2017\u0026ndash;2018 period, for example 862 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e soil was lost on average in 2017\u0026ndash;2018 compared to 369 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2016\u0026ndash;2017 and 135 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in 2018\u0026ndash;2019.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Effect of tillage treatments on crop yield and perennial weed abundance\u003c/h2\u003e\n \u003cp\u003eIn Experiment 1, the rotary tiller resulted in the highest cereal yield, 40% higher than the untreated control and approximately 27% higher than the mowed or mowing\u0026thinsp;+\u0026thinsp;VRC treatments (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA; Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The disc harrow (+\u0026thinsp;30%), HRC at 7 cm depth (+\u0026thinsp;28%), VRC\u0026thinsp;+\u0026thinsp;HRC (+\u0026thinsp;33%) and VRC\u0026thinsp;+\u0026thinsp;disc harrow (+\u0026thinsp;36%) treatments also resulted in significantly higher cereal yields then the untreated control (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). In Experiment 2 there was no significant difference in crop yield between treatments (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB; Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn Experiment 1, the rotary tiller had the greatest effect on the total perennial weed biomass (-67%), compared to the untreated control, but the effect was not significantly greater than the VRC\u0026thinsp;+\u0026thinsp;HRC (-66%), VRC\u0026thinsp;+\u0026thinsp;disc harrow (-66%), HRC 15 cm (-51%), disc harrow (-51%) or HRC 7 cm (46%) treatments (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Similar to crop yield, the stubble harrow, mowing and mowing\u0026thinsp;+\u0026thinsp;VRC treatments did not significantly reduce total perennial weed biomass. In Experiment 2 there was no significant difference between treatments for total perennial weed biomass, only for shoot numbers (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eOn a species level, in Experiment 1, the rotary tiller was the most suppressive of \u003cem\u003eE. repens\u003c/em\u003e and \u003cem\u003eS. arvensis\u003c/em\u003e, reducing \u003cem\u003eE. repens\u003c/em\u003e biomass by 94% and shoot numbers by 88%, and \u003cem\u003eS. arvensis\u003c/em\u003e biomass by 65% and shoot numbers by 45%, compared to the untreated control (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e); though the VRC\u0026thinsp;+\u0026thinsp;disc harrow and VRC\u0026thinsp;+\u0026thinsp;HRC treatments had an almost identical effect on \u003cem\u003eE. repens\u003c/em\u003e and \u003cem\u003eS. arvensis\u003c/em\u003e except that they did not quite significantly reduce the number of \u003cem\u003eE. repens\u003c/em\u003e shoots compared to the untreated control (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). On its own, the disc harrow did not quite significantly reduce \u003cem\u003eE. repens\u003c/em\u003e biomass or shoots numbers, but reduced \u003cem\u003eS. arvensis\u003c/em\u003e biomass by 56% and shoot numbers by 36%, compared to the untreated control. The HRC treatments (7 cm and 15 cm depth) hovered around significantly reducing both \u003cem\u003eE. repens\u003c/em\u003e and \u003cem\u003eS. arvensis\u003c/em\u003e, with the 7 cm treatment significantly reducing \u003cem\u003eS. arvensis\u003c/em\u003e biomass by 52% and the 15 cm treatment significantly reducing \u003cem\u003eE. repens\u003c/em\u003e biomass by 80%, and while neither significantly reduced their shoot numbers on their own, the contrast between the two HRC treatments and the untreated control showed a significant reduction in both \u003cem\u003eE. repens\u003c/em\u003e (-71%; P\u0026thinsp;=\u0026thinsp;0.01) and \u003cem\u003eS. arvensis\u003c/em\u003e (-26%; P\u0026thinsp;=\u0026thinsp;0.004) shoot numbers. In Experiment 2, the disc harrow reduced the number of \u003cem\u003eE. repens\u003c/em\u003e shoots by 75% compared to the untreated control, while the HRC 7 cm reduced the number of \u003cem\u003eC. arvense\u003c/em\u003e shoots by 71% (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB); the same pattern could be seen for the perennial weed biomass, but without significant differences (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). In Experiment 1, \u003cem\u003eS. palustris\u003c/em\u003e appears to have increased in most treatments that suppressed \u003cem\u003eE. repens\u003c/em\u003e and \u003cem\u003eS. arvensis\u003c/em\u003e \u0026ndash; albeit only significantly for the rotary tiller, which had a higher number of shoots than the mowed treatment (9.1 vs. 2.3 plants/m\u003csup\u003e2\u003c/sup\u003e, respectively; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA; Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). There are not many studies on \u003cem\u003eS. palustris\u003c/em\u003e, but it has sometimes been reported as being especially tolerant of tillage (e.g. Korsmo \u003cspan class=\"CitationRef\"\u003e1954\u003c/span\u003e) which, in combination with the decrease in its competitors, could explain its increase in the tilled treatments that reduced \u003cem\u003eE. repens\u003c/em\u003e and \u003cem\u003eS. arvensis\u003c/em\u003e. There was no significant treatment effect on \u003cem\u003eV. cracca\u003c/em\u003e in Experiment 1 (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e), which may be explained by relatively patchy occurrence of \u003cem\u003eV. cracca\u003c/em\u003e in the field compared to \u003cem\u003eE. repens\u003c/em\u003e and \u003cem\u003eS. arvensis\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eHypothesis, which stated the RCs would result in significantly less perennial weed biomass and shoot numbers, and higher crop yield, than the untreated control and mowing, but would not have a significantly worse effect that more intensive tillage methods (i.e. disc harrows, stubble harrows and rotary tillers), was mostly supported for the HRC, but only partly for the VRC. Overall, the HRC treatments reduced the weed biomass and/or shoot numbers of \u003cem\u003eE. repens\u003c/em\u003e, \u003cem\u003eS. arvensis\u003c/em\u003e and \u003cem\u003eC. arvense\u003c/em\u003e compared to mowing and the untreated control and was almost never significantly worse than the more intensive tillage treatments, with the clearest exception being the failure to control \u003cem\u003eE. repens\u003c/em\u003e in Experiment 2 despite an 80% reduction of \u003cem\u003eE. repens\u003c/em\u003e shoot biomass in Experiment 1. One reason for this failure could be because autumn plowing was used in Experiment 2 (compared to spring plowing in Experiment 1), which means that the plowing was conducted relatively soon after the HRC was used \u0026ndash; potentially reducing the impact of the HRC treatment. A not yet published experiment testing different combinations of HRC and plowing at different depths showed that using both HRC and plowing at the same depth had no additive effects on \u003cem\u003eC. arvense\u003c/em\u003e, most likely because the effect was too similar (Brands\u0026aelig;ter et al. Unpublished). Previous studies have shown that plowing time has a relatively minor importance on \u003cem\u003eE. repens\u003c/em\u003e, while spring plowing is more effective than autumn plowing on \u003cem\u003eC. arvense\u003c/em\u003e and \u003cem\u003eS. arvensis\u003c/em\u003e (Brands\u0026aelig;ter et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e); but this was only investigated with or without disc harrowing, so it is possible that the HRC followed by autumn plowing is less effective against \u003cem\u003eE. repens\u003c/em\u003e than when combined with spring plowing. Despite the failure to control \u003cem\u003eE. repens\u003c/em\u003e in Experiment 2, however, the HRC showed that it can have a strong reductive effect on \u003cem\u003eE. repens\u003c/em\u003e, \u003cem\u003eS. arvensis\u003c/em\u003e and \u003cem\u003eC. arvense\u003c/em\u003e, supporting previous studies that fragmenting the roots/rhizomes of perennial weeds has a negative effect on their growth and propagation (e.g. Thomsen et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ringselle et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). One limitation of this study is that it only studied the aboveground biomass, which may differ significantly from the effect on the belowground biomass (cf. Ringselle et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), but this is compensated by the 2\u0026ndash;3 year duration of the experiments.\u003c/p\u003e\n \u003cp\u003eNo treatment with VRC (Mow\u0026thinsp;+\u0026thinsp;VRC, HRC\u0026thinsp;+\u0026thinsp;VCR, disc harrow\u0026thinsp;+\u0026thinsp;VCR) in Experiment 1 reduced \u003cem\u003eE. repens\u003c/em\u003e or \u003cem\u003eS. arvensis\u003c/em\u003e biomass or numbers significantly more than the corresponding treatment without VRC (mowing, HRC 7 cm, HRC 15 cm or disc harrow). However, contrasts between the two treatment groups showed that treatments with VRC reduced total perennial weed biomass by 21% (P\u0026thinsp;=\u0026thinsp;0.005), \u003cem\u003eS. arvensis\u003c/em\u003e shoot numbers by 19% (P\u0026thinsp;=\u0026thinsp;0.03) and shoot biomass by 22% (P\u0026thinsp;=\u0026thinsp;0.03), and \u003cem\u003eE. repens\u003c/em\u003e shoot biomass by 40% (P\u0026thinsp;=\u0026thinsp;0.04) (though not \u003cem\u003eE. repens\u003c/em\u003e shoot numbers (P\u0026thinsp;=\u0026thinsp;0.2)), compared to treatments without VRC. These results support Hypothesis 2, which stated that integrating the VRC with other control methods would increase the efficacy against perennial weeds. This is the first time that the VRC has been shown to have a reductive effect on \u003cem\u003eS. arvensis\u003c/em\u003e, as previous studies have focused on \u003cem\u003eE. repens\u003c/em\u003e; and can, together with the effect of the HRC, be contrasted with previous work that show a relatively limited effect of root fragmentation on \u003cem\u003eS. arvensis\u003c/em\u003e growth and reproduction (e.g. Anbari et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2016a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003eb\u003c/span\u003e). A reduction of 40% shoot biomass of \u003cem\u003eE. repens\u003c/em\u003e corresponds well with the 38% shoot biomass reduction achieved with VRC in Ringselle et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), even though the treatments in Experiment 1 were performed in autumn and only in one direction rather than, as in Ringselle et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), being performed in early summer in a crisscross pattern. Bergkvist et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) found that, at least for \u003cem\u003eE. repens\u003c/em\u003e, rhizome fragmentation in a crisscross pattern is much more effective in early summer than in autumn. Brands\u0026aelig;ter et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that the VRC did not significantly reduce \u003cem\u003eE. repens\u003c/em\u003e shoot biomass when performed in one direction in autumn.\u003c/p\u003e\n \u003cp\u003eThe results provided little to no support to Hypothesis 3, which stated that shallow root/rhizome fragmentation would be more effective against perennial weed species with relatively shallow roots/rhizomes, and deeper fragmentations more effective against those with relatively deep roots. There was little to no difference between the two HRC treatments (7 and 15 cm) in either Experiment 1 and 2 in their effect on perennial weed biomass or shoot numbers, or crop yield. However, the VRC had a higher effect on \u003cem\u003eE. repens\u003c/em\u003e than on \u003cem\u003eS. arvensis\u003c/em\u003e, but this could either be because fewer \u003cem\u003eS. arvensis\u003c/em\u003e roots were fragmented, or that the fragmentation had a lesser effect on \u003cem\u003eS. arvensis\u003c/em\u003e. Two factors may have contributed to the minimal difference between the HRC treatments of different depths: 1) the experiments were conducted in a plowed system, and 2) even the deeper treatment was not that deep. In untilled systems \u003cem\u003eE. repens\u003c/em\u003e rhizomes grow relatively close to the soil surface, while they are distributed down to the plowing depth in plowed systems (Lemieux et al. \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e). Thus, in a plowed system a 15 cm HRC treatment would still affect many \u003cem\u003eE. repens\u003c/em\u003e and \u003cem\u003eS. arvensis\u003c/em\u003e roots/rhizomes but in a plowless system it might fragment fewer roots/rhizomes, especially for \u003cem\u003eE. repens\u003c/em\u003e. A deeper HRC treatment, for example 25 cm depth, might have resulted in a greater contrast in treatment effects as, even in a plowed system, it would likely affect fewer \u003cem\u003eE. repens\u003c/em\u003e and \u003cem\u003eS. arvensis\u003c/em\u003e roots/rhizomes but still be likely to be effective against the more deeper-rooted \u003cem\u003eC. arvense\u003c/em\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Effects on soil erosion and leaching\u003c/h2\u003e\n \u003cp\u003eThe results showed clear support for Hypothesis 4, which stated that using the HRC in the cereal stubble will not increase the water, soil and nutrient losses compared to an untreated control, but will result in significant lower losses than using a disc harrow. In Experiment 3, the HRC 15 cm treatment did not result in a higher level of water surface runoff, soil loss or nutrient leaching of P, N or Po4_p compared to the untreated control, and resulted in 60% less soil loss and a tendency (p\u0026thinsp;=\u0026thinsp;0.06) towards 52% lower P leaching, than the disc harrow (Tables \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). The results are limited to one site and its soil type/environment, but the fact that it is a three-year experiment, and the clearness of the results (i.e. no indication of increase in any measure compared to the untreated control), provides a good indication that the results may be generally applicable, but more studies under more soil types and production systems are needed. Another limitation is that only one HRC treatment was tested in Experiment 3, using it once in autumn, while in Experiment 1\u0026ndash;2, the stubble treatments were sometimes repeated \u0026ndash; which is often the case and could potentially increase the risk of soil and nutrient losses. Yet even multiple HRC treatments would most likely have a lower risk than multiple treatments of more intensive tillage such as disc harrowing. For example Aronsson et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that a single duckfoot cultivation in autumn increased N leaching compared to the untreated control (20 vs. 17 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and that two duckfoot cultivations or two disc harrow cultivations increased the N leaching even further to 26 kg N ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Moreover, unlike disc harrowing, the HRC can be performed in a cover crop without killing it, and combining the HRC with a cover crop could further reduce soil and nutrient losses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Implications for management\u003c/h2\u003e\n \u003cp\u003eThe results show that in a plowed system the HRC can provide a control efficacy of multiple perennial weed species (\u003cem\u003eE. repens\u003c/em\u003e, \u003cem\u003eS. arvensis\u003c/em\u003e, \u003cem\u003eC. arvense\u003c/em\u003e) that is comparable to more intensive tillage methods such as disc harrowing, stubble harrowing and rotary tillage, but with a much lower low risk of water, soil and nutrient losses. Thus, the HRC is a clear alternative to more intensive tillage for achieving a more environmentally friendly control of perennial weeds in plowed systems, for example in organic farming. The results from these experiments indicate that in a plowed system the HRC depth does not need to be adapted to suit different perennial weed species, but further studies on HRC depth are needed. The influence of autumn vs. spring plowing may also need to be studied further, as plowing time may affect the HRC differently than other tillage methods.\u003c/p\u003e\n \u003cp\u003eWith its minimal disturbance of the soil and soil cover, and possibility to combine with cover crops, the HRC is likely to be very relevant for conservation agriculture, regenerative agriculture and other farming systems that eliminate or strongly reduce the use of tillage, especially plowing. For these systems there are still many questions that need answering, such as how effective the HRC is against perennial weeds when it is not followed by plowing, how effective the HRC is as a part of integrated strategies such as combining it with cover crops, mowing and the VRC, and how the treatments must be adapted to a plowless system. For instance, with \u003cem\u003eE. repens\u003c/em\u003e rhizomes growing closer to the surface in untilled systems (Lemieux et al. \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e) it may be necessary to use shallow HRC treatments to control \u003cem\u003eE. repens\u003c/em\u003e in these systems, but this could be far more damaging to a cover crop than a deeper HRC treatment. The fragmentation effect may also be less on roots/rhizomes growing closer to the soil as less energy is needed to reach the soil surface (H\u0026aring;kansson \u003cspan class=\"CitationRef\"\u003e1967\u003c/span\u003e), but this may be compensated for by the use of a cover crop and/or mowing (Kolberg et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e); and it is possible that since less tilled soils can have a greater soil health with a higher degree of soil organic carbon, higher microbial and fungal activity and a different microbial community (Krauss et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) that fragmented roots/rhizomes would break down faster than in plowed soils. Moreover, the shallow rhizomes could also make \u003cem\u003eE. repens\u003c/em\u003e more susceptible to VRC treatments, increasing the synergy from applying both HRC and VRC. Some of these questions have been studied in the AC/DC-weeds-project (\u003cem\u003eApplying and Combining Disturbance and Competition for an agro-ecological management of creeping perennial weeds\u003c/em\u003e) that ran 2019\u0026ndash;2022 and/or will be studied in the SUSWECO-project (\u003cem\u003eSustainable weed control in cereals by combining subsidiary crops and minimal soil disturbances\u003c/em\u003e) that will run 2023\u0026ndash;2027. The results that have been published from AC/DC-weeds so far show that, without plowing, up to six HRC treatments may be needed to achieve sufficient control of \u003cem\u003eC. arvense\u003c/em\u003e (Weigel and Gerowitt \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), yet only two HRC treatments combined with a cover crop was sufficient to reduce \u003cem\u003eC. arvense\u003c/em\u003e expansion to the same degree as plowing (Weigel et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe results show that even though the VRC did not provide very high suppression of perennial weeds (which is congruent with previous results), when combined with other tillage methods (e.g. HRC, disc harrow), it increased the efficacy \u0026ndash; with the VRC\u0026thinsp;+\u0026thinsp;HRC and VRC\u0026thinsp;+\u0026thinsp;disc harrow treatments being almost indistinguishable from the far more intensive rotary tiller in their effect. That the effect was achieved when only running in one direction, rather than as a crisscross patterns (as in e.g. Ringselle et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) increases the potential of the VRC as a control method as running it twice to achieve a crisscross pattern is very labor intensive and disturbs the soil more. More experiments where the VRC is used, preferably in the spring, as a component of an integrated strategy, would be desirable. For example, a spring VRC application in a cereal crop followed by post-harvest HRC applications in a cover crop could potentially be a very potent strategy to control multiple perennial weed species with a very low risk of soil erosion and nutrient leaching.\u003c/p\u003e\n \u003cp\u003eThe experiments have shown that the RCs can be effective against multiple perennial weed species with different traits. It can be hypothesized that they would be effective against other perennial weed species that are negatively affected by rhizome/root fragmentation, such as \u003cem\u003eCalystegia sepium\u003c/em\u003e (L.) R.Br. (hedge bindweed) (Rask and Andreasen \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e), and \u003cem\u003eCyperus aromaticus\u003c/em\u003e (L.) (Navua sedge) (Chadha et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), but it would have to be tested to be confirmed. It would be especially interesting to test the RCs against perennial weed species with other types of underground storage organs such as \u003cem\u003eCyperus esculentus\u003c/em\u003e L. (Yellow nutsedge), which has rhizomes and tubers (Feys et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), \u003cem\u003eCynodon dactylon\u003c/em\u003e (L.) Pers (Bermudagrass), which has rhizomes and stolons (Soares et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), and against \u003cem\u003eRumex obtusifolius\u003c/em\u003e L. (Broad-leaved dock) which has a large taproot (Ringselle et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe HRC can significantly reduce the biomass and/or shoot numbers of multiple perennial weed species (\u003cem\u003eE. repens\u003c/em\u003e, \u003cem\u003eS. arvensis\u003c/em\u003e, \u003cem\u003eC. arvense\u003c/em\u003e) and increase the crop yield compared to an untreated or mowed control. Overall, the effect of the HRC is not worse than more intensive tillage methods such as disc harrow, stubble harrow or rotary tiller. Moreover, the HRC does not increase soil, water and nutrient losses compared to an untreated control, and results in less soil loss and P leaching than the use of a disc harrow. In this study, using a HRC treatment depth of 7 or 15 cm did not differ for \u003cem\u003eE. repens\u003c/em\u003e, \u003cem\u003eS. arvensis\u003c/em\u003e or \u003cem\u003eC. arvense\u003c/em\u003e control. Overall, treatments that integrated the VRC reduced \u003cem\u003eE. repens\u003c/em\u003e and \u003cem\u003eS. arvensis\u003c/em\u003e shoot biomass and shoot numbers more than treatments without VRC but was more effective against \u003cem\u003eE. repens\u003c/em\u003e than \u003cem\u003eS. arvensis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eNovelly, the results show that the HRC can provide a much-needed soil friendly alternative to more intensive tillage to control perennial weeds in plowed systems, for example in organic agriculture. The results also show the VRC\u0026rsquo;s potential to increase the efficacy of integrated strategies against multiple perennial weed species, and that it can seemingly be effective even if run only in one direction rather than in a crisscross pattern. In addition to organic farming, the RCs show great potential to be used for perennial weed control in plowless systems to reduce these systems\u0026rsquo; reliance on herbicides, but more studies are needed in such systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMajority of the funding was provided by the project \u0026ldquo;Rootcutter \u0026ndash; Innovative Technology for Weed Control\u0026rdquo; (project no. 256441/E50) which was funded by the Norwegian Research Council through the BION\u0026AElig;R-Bion\u0026aelig;ring program. The HRC and VRC prototypes were provided by the Kverneland Group as in-kind. Additional minor funding was provided by the C.F. Lundstr\u0026ouml;m foundation (project no. CF2023-0010) and the Swedish Research Council FORMAS to work on the scientific publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest, however for the sake of transparency, please note that Bj\u0026ouml;rn Ringselle is an associate editor at the journal of Agronomy for Sustainable Development. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be provided upon request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLars Olav Brands\u0026aelig;ter (LOB), Trond B\u0026oslash;rresen (TB), Kjell Mangerud (KJ), Anneli Lundkvist (AL) and Theo Verwijst (TW) acquired the major funding and contributed to the study conception and design. Bj\u0026ouml;rn Ringselle (BR) acquired the minor funding. LOB, TB and KJ had the main responsibility for Experiment 1 and 3 and AL and TW the main responsibility for Experiment 2. Torfinn Torp (TT) conducted all statistical analyses. \u0026Oslash;ystein Skagestad (\u0026Oslash;S) collated the management information and wrote a first draft of the introduction and M\u0026amp;M, while BR completed the manuscript and produced all figures. All authors except KJ (deceased) commented on the final draft. BR acted as corresponding author. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdeux G, Vieren E, Carlesi S, et al (2019) Mitigating crop yield losses through weed diversity. Nat Sustain 2:1018\u0026ndash;1026. https://doi.org/10.1038/s41893-019-0415-y\u003c/li\u003e\n\u003cli\u003eAnbari S, Lundkvist A, Forkman J, Verwijst T (2016a) Population dynamics and nitrogen allocation of Sonchus arvensis L. in relation to initial root size. 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Agriculture 13:696. https://doi.org/10.3390/agriculture13030696\u003c/li\u003e\n\u003cli\u003eFogliatto S, Ferrero A, Vidotto F (2020) Current and future scenarios of glyphosate use in Europe: Are there alternatives? https://doi.org/10.1016/bs.agron.2020.05.005\u003c/li\u003e\n\u003cli\u003eH\u0026aring;kansson S (2003) Weeds and Weed Management on Arable Land\u0026ndash;An Ecological Approach. CABI Publishing, Wallingford, U.K.\u003c/li\u003e\n\u003cli\u003eH\u0026aring;kansson S (1967) Experiments with \u003cem\u003eAgropyron repens\u003c/em\u003e (L.) Beauv. I. Development and growth, and the response to burial at different developmental stages. Lantbruksh\u0026ouml;gskolans Ann 33:823\u0026ndash;873\u003c/li\u003e\n\u003cli\u003eKlik A, Rosner J (2020) Long-term experience with conservation tillage practices in Austria: Impacts on soil erosion processes. Soil Tillage Res 203:104669. https://doi.org/10.1016/j.still.2020.104669\u003c/li\u003e\n\u003cli\u003eKolberg D, Brands\u0026aelig;ter LO, Bergkvist G, et al (2018) Effect of rhizome fragmentation, clover competition, shoot-cutting frequency, and cutting height on quackgrass (\u003cem\u003eElymus repens\u003c/em\u003e). Weed Sci 66:215\u0026ndash;225. https://doi.org/10.1017/wsc.2017.65\u003c/li\u003e\n\u003cli\u003eKorsmo E (1954) Anatomy of weeds. Grohndal \u0026amp; Sons, Oslo\u003c/li\u003e\n\u003cli\u003eKrauss M, Berner A, Perrochet F, et al (2020) Enhanced soil quality with reduced tillage and solid manures in organic farming \u0026ndash; a synthesis of 15 years. Sci Rep 10:4403. https://doi.org/10.1038/s41598-020-61320-8\u003c/li\u003e\n\u003cli\u003eLemieux C, Cloutier DC, Leroux GD (1993) Distribution and survival of quackgrass (\u003cem\u003eElytrigia repens\u003c/em\u003e) rhizome buds. Weed Sci 41:600\u0026ndash;606. https://doi.org/10.1017/S0043174500076384\u003c/li\u003e\n\u003cli\u003eLiew J, Andersson L, Bostr\u0026ouml;m U, et al (2013) Regeneration capacity from buds on roots and rhizomes in five herbaceous perennials as affected by time of fragmentation. Plant Ecol 214:1199\u0026ndash;1209. https://doi.org/10.1007/s11258-013-0244-4\u003c/li\u003e\n\u003cli\u003eLMT (2023) Landbruksmetrologisk tjeneste (LMT). In: Hent V\u0026aelig;rdata. https://lmt.nibio.no/agrometbase/getweatherdata_new.php?weatherStationId=61. Accessed 12 Dec 2023\u003c/li\u003e\n\u003cli\u003eMelander B, Rasmussen IA, Olesen JE (2016) Incompatibility between fertility building measures and the management of perennial weeds in organic cropping systems. Agric Ecosyst Environ 220:184\u0026ndash;192. https://doi.org/10.1016/j.agee.2016.01.016\u003c/li\u003e\n\u003cli\u003eMigliorini P, Wezel A (2017) Converging and diverging principles and practices of organic agriculture regulations and agroecology. A review. Agron Sustain Dev 37:1\u0026ndash;18. https://doi.org/10.1007/s13593-017-0472-4\u003c/li\u003e\n\u003cli\u003eNCCS (2023) Norwegian Center for Climate Services. In: Obs. Weather Stat. https://seklima.met.no/observations/. Accessed 12 Dec 2023\u003c/li\u003e\n\u003cli\u003eNewton P, Civita N, Frankel-Goldwater L, et al (2020) What is regenerative agriculture? A review of scholar and practitioner definitions based on processes and outcomes. Front Sustain Food Syst 4:194. https://doi.org/10.3389/fsufs.2020.577723\u003c/li\u003e\n\u003cli\u003eNichols V, Verhulst N, Cox R, Govaerts B (2015) Weed dynamics and conservation agriculture principles: A review. Field Crops Res 183:56\u0026ndash;68. https://doi.org/10.1016/j.fcr.2015.07.012\u003c/li\u003e\n\u003cli\u003eNj\u0026oslash;s A, Hove P (1986) Erosjonsunders\u0026oslash;kelser. NLVF Sluttrapport. No 655. Norwegian Research Council, Oslo\u003c/li\u003e\n\u003cli\u003eOerke E (2006) Crop losses to pests. J Agric Sci 144:31. https://doi.org/10.1017/S0021859605005708\u003c/li\u003e\n\u003cli\u003eRask AM, Andreasen C (2007) Influence of mechanical rhizome cutting, rhizome drying and burial at different developmental stages on the regrowth of Calystegia sepium. Weed Res 47:84\u0026ndash;93. https://doi.org/10.1111/j.1365-3180.2007.00539.x\u003c/li\u003e\n\u003cli\u003eRiemens M, S\u0026oslash;nderskov M, Moonen A-C, et al (2022) An integrated weed management framework: a pan-European perspective. Eur J Agron 133:126443. https://doi.org/10.1016/j.eja.2021.126443\u003c/li\u003e\n\u003cli\u003eRingselle B, Berge TW, Stout D, et al (2019) Effects of renewal time, taproot cutting, ploughing practice, false seedbed and companion crop on docks (\u003cem\u003eRumex\u003c/em\u003e spp.) when renewing grassland. Eur J Agron 103:54\u0026ndash;62. https://doi.org/10.1016/j.eja.2018.11.005\u003c/li\u003e\n\u003cli\u003eRingselle B, Bergkvist G, Aronsson H, Andersson L (2015) Under‐sown cover crops and post‐harvest mowing as measures to control \u003cem\u003eElymus repens\u003c/em\u003e. Weed Res 55:309\u0026ndash;319. https://doi.org/10.1111/wre.12144\u003c/li\u003e\n\u003cli\u003eRingselle B, Bertholtz E, Magnuski E, et al (2018) Rhizome Fragmentation by Vertical Disks Reduces \u003cem\u003eElymus repens\u003c/em\u003e Growth and Benefits Italian Ryegrass-White Clover Crops. Front Plant Sci 8:2243. https://doi.org/10.3389/fpls.2017.02243\u003c/li\u003e\n\u003cli\u003eRingselle B, Brands\u0026aelig;ter LO, Mangerud K, Bergkvist G (2023) Vertical rhizome disking to reduce Elymus repens (quackgrass) abundance in grass-clover leys. Crop Prot 172:106301. https://doi.org/10.1016/j.cropro.2023.106301\u003c/li\u003e\n\u003cli\u003eRingselle B, De Cauwer B, Salonen J, Soukup J (2020) A Review of Non-Chemical Management of Couch Grass (\u003cem\u003eElymus repens\u003c/em\u003e). Agronomy 10:1178. https://doi.org/10.3390/agronomy10081178\u003c/li\u003e\n\u003cli\u003eRingselle B, Oliver BW, Berge TW, et al (2021) Dry weight minimum in the underground storage and proliferation organs of six creeping perennial weeds. Weed Res 61:231\u0026ndash;241. https://doi.org/10.1111/wre.12476\u003c/li\u003e\n\u003cli\u003eSMHI (2023) Normal periods 1991-2020. In: Swed. Meteorol. Hydrol. Inst. SMHI. https://www.smhi.se/kunskapsbanken/klimat/normaler/normalperioden-1991-2020-1.166930. Accessed 4 Dec 2023\u003c/li\u003e\n\u003cli\u003eSoares PR, Galhano C, Gabriel R (2023) Alternative methods to synthetic chemical control of Cynodon dactylon (L.) Pers. A systematic review. Agron Sustain Dev 43:51. https://doi.org/10.1007/s13593-023-00904-w\u003c/li\u003e\n\u003cli\u003eThomsen MG, Brands\u0026aelig;ter L-O, Fykse H (2013) Regeneration of Canada Thistle (Cirsium arvense) from Intact Roots and Root Fragments at Different Soil Depths. Weed Sci 61:277\u0026ndash;282. https://doi.org/10.1614/WS-D-12-00095.1\u003c/li\u003e\n\u003cli\u003eThomsen MG, Mangerud K, Riley H, Brands\u0026aelig;ter LO (2015) Method, timing and duration of bare fallow for the control of \u003cem\u003eCirsium arvense\u003c/em\u003e and other creeping perennials. Crop Prot 77:31\u0026ndash;37. https://doi.org/10.1016/j.cropro.2015.05.020\u003c/li\u003e\n\u003cli\u003eVan Bruggen AH, He M, Shin K, et al (2018) Environmental and health effects of the herbicide glyphosate. Sci Total Environ 616:255\u0026ndash;268. https://doi.org/10.1016/j.scitotenv.2017.10.309\u003c/li\u003e\n\u003cli\u003eWeigel M, Gerowitt B (2022) Mechanical disturbance of \u003cem\u003eCirsium arvense\u003c/em\u003e-Results from a multi-year field study. Julius-K\u0026uuml;hn-Arch 27:78. https://doi.org/10.5073/20220117-073804\u003c/li\u003e\n\u003cli\u003eWeigel MM, Andert S, Gerowitt B (2023) Monitoring Patch Expansion Amends to Evaluate the Effects of Non-Chemical Control on the Creeping Perennial \u003cem\u003eCirsium arvense\u003c/em\u003e (L.) Scop. in a Spring Wheat Crop. Agronomy 13:1474. https://doi.org/10.3390/agronomy13061474\u003c/li\u003e\n\u003cli\u003eWesseler J (2022) The EU\u0026rsquo;s farm‐to‐fork strategy: An assessment from the perspective of agricultural economics. Appl Econ Perspect Policy 44:1826\u0026ndash;1843. https://doi.org/10.1002/aepp.13239\u003c/li\u003e\n\u003cli\u003eWorld Reference Base (2006) A framework for international classification, correlation and communication. Food and Agriculture Organization of the United Nations, Rome, World Soil Resources Reports 103\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Dates for management and treatments in Experiment 1, 2 and 3. VRC = vertical root/rhizome cutter, HRC = Horizontal root/rhizome cutter. A second stubble treatment was performed if the perennial weeds had had sufficient time to reach their compensation stage and the soil conditions allowed for tillage (e.g. not too wet, as was the case in Experiment 2 in 2017).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\n \u003cp\u003eExperiment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eOperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eSpring plowing, all plots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e11 May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003e26 May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e22 May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e23 Apr\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eEstablishment (leveling, fertilizing, seedbed preparation, rolling, sowing*), all plots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e12 May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003e27 May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e22 May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e24-25 Apr\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eThreshing, all plots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e27 Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003e20 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e26 Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e2 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eMain registration - Weeds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e24-31 Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003e21-28 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e27 Aug-3 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e9-13 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eFirst stubble treatment (HRC, VRC, disc harrow, stubble harrow, rotary tiller, mowing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e7 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003e28-29 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e6-7 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eSecond stubble treatment (HRC, VRC, disc harrow, stubble harrow, rotary tiller, mowing)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e26-27 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003eNot applied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003eNot applied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eEstablishment (leveling, fertilizing, seedbed preparation, rolling, sowing**), all plots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003e13 May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e13 May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e2 May\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"44.171779141104295%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMain registration - Weed shoots\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003e14-16 Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e2-3 Jul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e8-9 Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eMain registration - Grain / weed biomass / 2\u003csup\u003end\u003c/sup\u003e weed shoots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003e1-2 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e21-28 Aug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e1 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eThreshing, all plots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003e18 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e1 Sep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eFirst stubble treatment (HRC, disc harrow, stubble harrow)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003e4 Oct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e3 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eSecond stubble treatment (HRC, disc harrow, stubble harrow)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003eNot applied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e26-27 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eAutumn plowing, all plots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e15 Oct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003e24 Oct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e16 Oct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eSpring plowing, all plots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e25 Apr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003e23 May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e30 Apr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eEstablishment (fertilizing, seedbed preparation, rolling, sowing***), all plots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e10 May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003e24 May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e11 May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eThreshing, all plots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e28 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003e25 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e21 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.417177914110429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.05521472392638%\" valign=\"top\"\u003e\n \u003cp\u003eStubble treatment (HRC, disc harrow)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e25 Oct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.190184049079754%\" valign=\"top\"\u003e\n \u003cp\u003e20 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.104294478527606%\" valign=\"top\"\u003e\n \u003cp\u003e28 Sep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.116564417177914%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Spring barley (2016: cv. \u0026ldquo;Thule\u0026rdquo; 210 kg/ha, 2017: cv. \u0026ldquo;Brage\u0026rdquo; 200 kg/ha), Oats (2018: cv. \u0026ldquo;Niklas\u0026rdquo; 220 kg/ha, 2019: cv. \u0026ldquo;Niklas\u0026rdquo; 230 - 240 kg/ha)\u003c/p\u003e\n\u003cp\u003e**Spring barley 400 kernels/plot, all years\u003c/p\u003e\n\u003cp\u003e***Spring oat 210 kg/ha, all years\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Records of monthly precipitation, temperature [taken from NMBU weather station SN17850 (NCCS 2023)] and radiation [taken from weather station \u0026Aring;s (LMT 2023)] for 2016\u0026ndash;2019 in \u0026Aring;s, Norway, where both Experiment 1 and 3 took place.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"658\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003eMean temperature (\u0026deg;C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003eRadiation (\u0026sum; MJ m\u003csup\u003e-2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003ePrecipitation (\u0026sum; mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFeb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eApr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNov\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e-1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*means for the period 1991\u0026ndash;2020\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eRecords of monthly precipitation, temperature, and radiation for 2017\u0026ndash;2019 at Ultuna, Sweden, where Experiment 2 was conducted, compared with means for the period 1991\u0026ndash;2020\u0026nbsp;(Anonymous 2019; SMHI 2023).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.051282051282051%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.92948717948718%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eMean temperature (\u0026deg;C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.92948717948718%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eRadiation (\u0026sum; MJ m\u003csup\u003e-2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.08974358974359%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003ePrecipitation (\u0026sum; mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.051282051282051%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814102564102564%\" valign=\"top\"\u003e\n \u003cp\u003eMean*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814102564102564%\" valign=\"top\"\u003e\n \u003cp\u003eMean*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.974358974358974%\" valign=\"top\"\u003e\n \u003cp\u003eMean*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.051282051282051%\" valign=\"top\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814102564102564%\" valign=\"top\"\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814102564102564%\" valign=\"top\"\u003e\n \u003cp\u003e586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.974358974358974%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.051282051282051%\" valign=\"top\"\u003e\n \u003cp\u003eJune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814102564102564%\" valign=\"top\"\u003e\n \u003cp\u003e14.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814102564102564%\" valign=\"top\"\u003e\n \u003cp\u003e616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.974358974358974%\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.051282051282051%\" valign=\"top\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814102564102564%\" valign=\"top\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814102564102564%\" valign=\"top\"\u003e\n \u003cp\u003e607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.974358974358974%\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.051282051282051%\" valign=\"top\"\u003e\n \u003cp\u003eAug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814102564102564%\" valign=\"top\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814102564102564%\" valign=\"top\"\u003e\n \u003cp\u003e473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.974358974358974%\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.051282051282051%\" valign=\"top\"\u003e\n \u003cp\u003eSept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814102564102564%\" valign=\"top\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814102564102564%\" valign=\"top\"\u003e\n \u003cp\u003e290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.974358974358974%\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.051282051282051%\" valign=\"top\"\u003e\n \u003cp\u003eOct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814102564102564%\" valign=\"top\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.814102564102564%\" valign=\"top\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.371794871794871%\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.974358974358974%\" valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*means for the period 1991\u0026ndash;2020\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Details of the machines and implements used in Experiments 1-3\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.589413447782546%\" valign=\"bottom\"\u003e\n \u003cp\u003eExperiment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.603719599427755%\" valign=\"bottom\"\u003e\n \u003cp\u003eModel name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.204577968526465%\" valign=\"bottom\"\u003e\n \u003cp\u003eTilling implement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.723891273247497%\" valign=\"bottom\"\u003e\n \u003cp\u003ePTO (rpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.878397711015737%\" valign=\"bottom\"\u003e\n \u003cp\u003eForward Speed (km \u003csup\u003eh-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.589413447782546%\" valign=\"top\"\u003e\n \u003cp\u003e1-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.603719599427755%\" valign=\"top\"\u003e\n \u003cp\u003eKverneland Horizontal Root/Rhizome Cutter (HRC) (prototype) (2.5 m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.204577968526465%\" valign=\"top\"\u003e\n \u003cp\u003e54 cm wide flat shares like a goosefoot share, cuts the roots/rhizomes to an even depth throughout the whole width.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.723891273247497%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.878397711015737%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.589413447782546%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.603719599427755%\" valign=\"top\"\u003e\n \u003cp\u003eKverneland Vertical Root/Rhizome Cutter (VRC) (prototype) 1.5 m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.204577968526465%\" valign=\"top\"\u003e\n \u003cp\u003eThe 36 cm diameter discs make cuts 10 cm apart.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.723891273247497%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.878397711015737%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.589413447782546%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.603719599427755%\" valign=\"top\"\u003e\n \u003cp\u003eRotary tiller\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.204577968526465%\" valign=\"top\"\u003e\n \u003cp\u003eHoward L-tine Cultivator. Vertically aligned PTO-driven L-tines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.723891273247497%\" valign=\"top\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.878397711015737%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.589413447782546%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.603719599427755%\" valign=\"top\"\u003e\n \u003cp\u003eKverneland FH 180 Chopper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.204577968526465%\" valign=\"top\"\u003e\n \u003cp\u003eStubble and pasture mower.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.723891273247497%\" valign=\"top\"\u003e\n \u003cp\u003e540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.878397711015737%\" valign=\"top\"\u003e\n \u003cp\u003e5-7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.589413447782546%\" valign=\"top\"\u003e\n \u003cp\u003e1 \u0026amp; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.603719599427755%\" valign=\"top\"\u003e\n \u003cp\u003eKverneland Disc Harrow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.204577968526465%\" valign=\"top\"\u003e\n \u003cp\u003eDisc diameter 35 cm. Kverneland, Norway.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.723891273247497%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.878397711015737%\" valign=\"top\"\u003e\n \u003cp\u003e5-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.589413447782546%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.603719599427755%\" valign=\"top\"\u003e\n \u003cp\u003eStubble Harrow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.204577968526465%\" valign=\"top\"\u003e\n \u003cp\u003eGoosefoot shares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.723891273247497%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.878397711015737%\" valign=\"top\"\u003e\n \u003cp\u003e5-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.589413447782546%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.603719599427755%\" valign=\"top\"\u003e\n \u003cp\u003eV\u0026auml;derstad Carrier disc cultivator (4.25 m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.204577968526465%\" valign=\"top\"\u003e\n \u003cp\u003eDisc diameter 45 cm.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.723891273247497%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.878397711015737%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.589413447782546%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.603719599427755%\" valign=\"top\"\u003e\n \u003cp\u003eV\u0026auml;derstad Swift Cultivator (4 m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.204577968526465%\" valign=\"top\"\u003e\n \u003cp\u003eGoosefoot shares, width 24 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.723891273247497%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.878397711015737%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.589413447782546%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.603719599427755%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.204577968526465%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.723891273247497%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.878397711015737%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e. ANOVA-table of the effects of treatment and year on crop yield in Experiment 1 and 2, and surface runoff, soil loss, phosphorous (P), Po4_p (phosphate) and nitrogen (N) leaching in Experiment 3. Initial number (No) of \u003cem\u003eElymus repens\u003c/em\u003e shoots was used as a covariate for crop yield as it was significant. LN = natural logarithm.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExperiment 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExperiment 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExperiment 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCrop yield (kg ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCrop yield (kg ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSurface runoff (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSoil loss (kg ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP loss (kg ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePo4_p (kg ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal N (kg ha\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYear*Treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eElymusNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTransformation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e. ANOVA-table of the effects of treatment and year on the shoot biomass of \u003cem\u003eElymus repens\u003c/em\u003e, \u003cem\u003eSonchus arvensis\u003c/em\u003e, \u003cem\u003eStachys palustris\u003c/em\u003e, \u003cem\u003eVicia cracca\u003c/em\u003e and the total perennial weed shoot biomass (all four species plus \u003cem\u003eC. arvense\u003c/em\u003e) in Experiment 1, and on the shoot biomass of \u003cem\u003eE. repens\u003c/em\u003e, \u003cem\u003eC. arvense\u003c/em\u003e and the total perennial weed shoot biomass in Experiment 2. Different covariates of initial dry weight (DW) or shoot numbers (No) of different perennial weed species were used depending on whether they had a significant effect. LN = natural logarithm.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"573\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.45993031358885%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eShoot biomass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.233449477351916%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.369337979094077%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.45993031358885%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eExperiment 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.60278745644599%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eExperiment 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.45993031358885%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eElymus repens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eSonchus arvensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eStachys palustris\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eVicia cracca\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\" valign=\"bottom\"\u003e\n \u003cp\u003eTotal per. weed biomass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.233449477351916%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eElymus repens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.369337979094077%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eCirsium arvense\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\" valign=\"bottom\"\u003e\n \u003cp\u003eTotal per. weed biomass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.45993031358885%\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.233449477351916%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.369337979094077%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.45993031358885%\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.233449477351916%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.369337979094077%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.45993031358885%\"\u003e\n \u003cp\u003eYear*Treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.233449477351916%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.369337979094077%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.45993031358885%\"\u003e\n \u003cp\u003eElymusDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.233449477351916%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.369337979094077%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.45993031358885%\"\u003e\n \u003cp\u003eSonchusNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.233449477351916%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.369337979094077%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.45993031358885%\"\u003e\n \u003cp\u003eElymusNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.233449477351916%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.369337979094077%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.45993031358885%\"\u003e\n \u003cp\u003eCirsiumNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.233449477351916%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.369337979094077%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.45993031358885%\"\u003e\n \u003cp\u003eTransformation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\" valign=\"bottom\"\u003e\n \u003cp\u003eLN+1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\" valign=\"bottom\"\u003e\n \u003cp\u003eLN+1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.975609756097562%\" valign=\"bottom\"\u003e\n \u003cp\u003eLN+1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"bottom\"\u003e\n \u003cp\u003eLN+1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.32404181184669%\" valign=\"bottom\"\u003e\n \u003cp\u003eLN+1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.233449477351916%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.369337979094077%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.452961672473867%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7\u003c/strong\u003e. ANOVA-table of the effects of treatment and year/time on the shoot numbers of \u003cem\u003eElymus repens\u003c/em\u003e, \u003cem\u003eSonchus arvensis\u003c/em\u003e, \u003cem\u003eStachys palustris\u003c/em\u003e, and \u003cem\u003eVicia cracca\u003c/em\u003e in Experiment 1, and on \u003cem\u003eE. repens\u003c/em\u003e and \u003cem\u003eC. arvense\u003c/em\u003e in Experiment 2. For Experiment 2 time was used rather than year as shoot numbers were counting twice each year for a total of four times over two years. Different covariates of initial dry weight (DW) or shoot numbers (No) of different perennial weed species were used depending on whether they had a significant effect. LN = natural logarithm.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"704\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.77304964539007%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003eWeed numbers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02836879432624%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.74468085106383%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.921985815602836%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.879432624113477%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.609929078014185%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.77304964539007%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003eExperiment 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02836879432624%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.74468085106383%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.921985815602836%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.879432624113477%\" valign=\"top\"\u003e\n \u003cp\u003eExperiment 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.609929078014185%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.77304964539007%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eElymus repens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02836879432624%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSonchus arvensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.74468085106383%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eStachys palustris\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.921985815602836%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eVicia cracca\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.879432624113477%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eElymus repens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.609929078014185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCirsium arvense\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.77304964539007%\" valign=\"top\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02836879432624%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.74468085106383%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.921985815602836%\" valign=\"top\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.879432624113477%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.609929078014185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.77304964539007%\" valign=\"top\"\u003e\n \u003cp\u003eYear/Time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02836879432624%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.74468085106383%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.921985815602836%\" valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.879432624113477%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.609929078014185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.77304964539007%\" valign=\"top\"\u003e\n \u003cp\u003eElymusDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02836879432624%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.74468085106383%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.921985815602836%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.879432624113477%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.609929078014185%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.77304964539007%\" valign=\"top\"\u003e\n \u003cp\u003eSonchusNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02836879432624%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.74468085106383%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.921985815602836%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.879432624113477%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.609929078014185%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.77304964539007%\" valign=\"top\"\u003e\n \u003cp\u003eElymusNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.042553191489361%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.02836879432624%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.74468085106383%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.921985815602836%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.879432624113477%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.609929078014185%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.77304964539007%\" valign=\"top\"\u003e\n \u003cp\u003eCirsiumNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.042553191489361%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.02836879432624%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.74468085106383%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.921985815602836%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.879432624113477%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.609929078014185%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.77304964539007%\" valign=\"top\"\u003e\n \u003cp\u003eTransformation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.042553191489361%\" valign=\"top\"\u003e\n \u003cp\u003eLN+1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02836879432624%\" valign=\"top\"\u003e\n \u003cp\u003eLN+1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.74468085106383%\" valign=\"top\"\u003e\n \u003cp\u003eLN+1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.921985815602836%\" valign=\"top\"\u003e\n \u003cp\u003eLN+1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.879432624113477%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.609929078014185%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8\u003c/strong\u003e. The effect of the three different treatments between 2016-2019 in Experiment 3 on surface runoff, soil loss, phosphorous (P), Po4_p (phosphate) and nitrogen (N) leaching. HRC = Horizontal root/rhizome cutter. \u0026plusmn; show the confidence intervals at 95%. \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eSurface runoff (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eSoil loss (kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eP loss (kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ePo4_p (kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eN loss (kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDisc harrow 10 cm depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-341/+1419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.48/+1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.03/+0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.7/+14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUntreated control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-173/+720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.3/+0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.02/+0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.7/+8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHRC 15 cm depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-139/+578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.23/+0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.02/+0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.4/+7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"eebf208e-cd09-4b74-9d63-b24bccbc286b","identifier":"10.13039/501100005416","name":"Norges Forskningsråd","awardNumber":"256441/E50","order_by":0},{"identity":"5c7eff35-3f3b-4d0b-8331-5b097f5aa961","identifier":"10.13039/501100009070","name":"C. F. Lundströms Stiftelse","awardNumber":"CF2023-0010","order_by":1}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Norwegian University of Life Sciences","isAcceptedByJournal":true,"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":"Conservation agriculture, regenerative agriculture, organic agriculture, integrated pest management, Elytrigia repens","lastPublishedDoi":"10.21203/rs.3.rs-3827798/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3827798/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePerennial weeds are a major obstacle for reducing pesticides and tillage. Three multi-year experiments were conducted in Norway and Sweden to determine if a) the horizonal and vertical root/rhizome cutters (HRC and VRC, respectively) can provide effective non-chemical control of multiple perennial weed species comparable to more intensive tillage methods (Experiments 1\u0026ndash;2), b) without increasing the risk of soil erosion and nutrient leaching (Experiment 3), and c) if integrating the VRC with the HRC, mowing or disc harrow can increase the efficacy against perennial weeds (Experiment 1). All treatments were spring plowed in Experiment 1 and 3, and autumn plowed in Experiment 2. In Experiment 1, the rotary tiller was the most suppressive against \u003cem\u003eSonchus arvensis\u003c/em\u003e and \u003cem\u003eElymus repens\u003c/em\u003e but increased \u003cem\u003eStachys palustris\u003c/em\u003e shoot numbers. HRC treatments were not significantly worse than the rotary tiller and increased crop yield by 28%, reduced total perennial shoot biomass by 46\u0026ndash;51% and reduced \u003cem\u003eS. arvensis\u003c/em\u003e and \u003cem\u003eE. repens\u003c/em\u003e shoot biomass by 52% and 80%, respectively, compared to an untreated control. In Experiment 2, HRC treatments reduced \u003cem\u003eCirsium arvense\u003c/em\u003e shoot numbers by 71% compared to the untreated control but failed to control \u003cem\u003eE. repens\u003c/em\u003e. HRC treatment depth (7 vs. 15 cm) did not significantly affect control efficacy. Experiment 3 showed that HRC did not increase soil, water or nutrient losses compared to the untreated control and resulted in 60% less soil and 52% less phosphorous losses than disc harrowing. Treatments with VRC reduced the shoot biomass of \u003cem\u003eE. repens\u003c/em\u003e by 40% and \u003cem\u003eS. arvensis\u003c/em\u003e by 22%, compared to without VRC. Novelly, the results show that in plowed systems, HRC provides control of multiple perennial weed species that is comparable to more intensive tillage methods, but with little risk of soil and nutrient losses; and integrating VRC into control strategies improves perennial weed control efficacy.\u003c/p\u003e","manuscriptTitle":"Root cutters: Novel tillage methods to control creeping perennial weeds with a low risk of soil erosion and nutrient leaching","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-17 04:08:54","doi":"10.21203/rs.3.rs-3827798/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":"23028767-c0c0-4dc7-9f5c-c6ad7c8d4877","owner":[],"postedDate":"January 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":27885234,"name":"Agronomy"}],"tags":[],"updatedAt":"2025-11-07T14:45:16+00:00","versionOfRecord":{"articleIdentity":"rs-3827798","link":"https://doi.org/10.1007/s13593-025-01059-6","journal":{"identity":"agronomy-for-sustainable-development","isVorOnly":false,"title":"Agronomy for Sustainable Development"},"publishedOn":"2025-11-06 00:00:00","publishedOnDateReadable":"November 6th, 2025"},"versionCreatedAt":"2024-01-17 04:08:54","video":"","vorDoi":"10.1007/s13593-025-01059-6","vorDoiUrl":"https://doi.org/10.1007/s13593-025-01059-6","workflowStages":[]},"version":"v1","identity":"rs-3827798","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3827798","identity":"rs-3827798","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}