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32 pages, 13693 KiB  
Article
Hydrological Response to Rewetting of Drained Peatlands—A Case Study of Three Raised Bogs in Norway
by Marta Stachowicz, Anders Lyngstad, Paweł Osuch and Mateusz Grygoruk
Land 2025, 14(1), 142; https://doi.org/10.3390/land14010142 - 11 Jan 2025
Viewed by 671
Abstract
The proper functioning of peatlands depends on maintaining an adequate groundwater table, which is essential for ecosystem services beyond water retention. Most degraded peatlands have been drained for agriculture or forestry primarily through ditch construction. Rewetting through ditch blocking is the most common [...] Read more.
The proper functioning of peatlands depends on maintaining an adequate groundwater table, which is essential for ecosystem services beyond water retention. Most degraded peatlands have been drained for agriculture or forestry primarily through ditch construction. Rewetting through ditch blocking is the most common initial step in peatland restoration. This study analyzed the hydrological response to ditch blocking in three drained raised bogs in Norway (Aurstadmåsan, Midtfjellmåsan and Kaldvassmyra) using a Before–After–Control–Impact (BACI) design. Following rewetting, all sites demonstrated an average increase in groundwater levels of 6 cm across all piezometers affected by ditch blocking. The spatial influence of ditch blocking extended 12.7–24.8 m from the ditch with an average of 17.2 m. Additionally, rewetting increased the duration of favorable groundwater levels for peatland functioning by 27.7%. These findings highlight the effectiveness of ditch blocking in restoring hydrological conditions, although its impact is spatially limited. Future assessments should also address vegetation recovery and greenhouse gas emission reductions to ensure comprehensive restoration success. Full article
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Figure 1

Figure 1
<p>A map of southern Norway showing the location of the study sites Kaldvassmyra, Aurstadmåsan, and Midtfjellmåsan.</p>
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<p>Maps with the locations of piezometers in Aurstadmåsan (<b>A</b>), Kaldvassmyra (<b>B</b>) and Midtfjellmåsan (<b>C</b>) sites (‘*’ indicates a reference point; arrows indicate flow directions in the ditches).</p>
Full article ">Figure 3
<p>Boxplots illustrating groundwater table comparisons across equal time intervals before and after rewetting. Row (<b>A</b>) represents the mean groundwater tables from impact piezometers, while row (<b>B</b>) shows the control piezometers. ‘ns’—<span class="html-italic">p</span> &gt; 0.05; ‘*’—<span class="html-italic">p</span> ≤ 0.05; ‘****’—<span class="html-italic">p</span> ≤ 0.0001.</p>
Full article ">Figure 4
<p>Boxplots of groundwater tables before and after rewetting for each piezometer in Kaldvassmyra (<b>A</b>), Aurstadmåsan (<b>B</b>), and Midtfjellmåsan (<b>C</b>). K.REF, A9P, and M6P represent control piezometers.</p>
Full article ">Figure 5
<p>Monthly mean groundwater tables of the impact piezometers and the monthly mean sum of precipitation from the pre-and post-rewetting period in Kaldvassmyra (<b>A</b>), Aurstadmåsan (<b>B</b>), and Midtfjellmåsan (<b>C</b>). The green areas represent the growing season in Norway (May–October).</p>
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<p>Relationship between the average groundwater level change and the distance from the ditch in Kaldvassmyra, Aurstadmåsan, and Midtfjellmåsan (control piezometers excluded).</p>
Full article ">Figure 7
<p>Groundwater depth duration curves before and after rewetting at Kaldvassmyra, Aurstadmåsan, and Midtfjellmåsan.</p>
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<p>Changes in the occurrence of specific ranges of groundwater tables before and after rewetting in Kaldvassmyra (<b>A</b>), Aurstadmåsan (<b>B</b>), and Midtfjellmåsan (<b>C</b>) (control piezometers: K.REF, A9P, M6P).</p>
Full article ">Figure 8 Cont.
<p>Changes in the occurrence of specific ranges of groundwater tables before and after rewetting in Kaldvassmyra (<b>A</b>), Aurstadmåsan (<b>B</b>), and Midtfjellmåsan (<b>C</b>) (control piezometers: K.REF, A9P, M6P).</p>
Full article ">Figure 9
<p>Boxplots of average monthly sums of precipitation in Kaldvassmyra, Aurstadmåsan, and Midtfjellmåsan. ‘ns’—<span class="html-italic">p</span> &gt; 0.05.</p>
Full article ">Figure A1
<p>Piezometer’s design.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer K.REF. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −0.0722695196313581 + −1.34471724148549 × 10<sup>−5</sup> × x.</p>
Full article ">Figure A3
<p>Groundwater depth and precipitation before and after rewetting in piezometer K1.30P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = 0.295372352980613 + −3.56069167565431 × 10<sup>−5</sup> × x.</p>
Full article ">Figure A4
<p>Groundwater depth and precipitation before and after rewetting in piezometer K1.60P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = 0.500276621282405 + −4.99935728099233 × 10<sup>−5</sup> × x.</p>
Full article ">Figure A5
<p>Groundwater depth and precipitation before and after rewetting in piezometer K2.50P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −4.21537097970746 + 0.000227020587660996 × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer K3.0P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = 0.527673900033183 + −5.11983475251307 × 10<sup>−5</sup> × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer K3.30P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −0.617217750141572 + 2.34234704348353 × 10<sup>−5</sup> × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer K4.20P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −0.438316010368798 + 1.40341469065934 × 10<sup>−5</sup> × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer K4.50P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = 0.391328050472584 + −2.71376863007763 × 10<sup>−5</sup> × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer A9P (control piezometer). Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −0.016332267477855 + −4.4304690170292 × 10<sup>−6</sup> × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer A1P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −1.45232389277605 + 6.96421961149087 × 10<sup>−5</sup> × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer A2P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −0.199593765095243 + −1.1320486546341 × 10<sup>−6</sup> × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer A3P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −0.504720685205623 + 1.71058635569323 × 10<sup>−5</sup> × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer A4P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −0.97242567190085 + 4.34024429775599 × 10<sup>−5</sup> × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer A5P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −1.64729627235141 + 8.50666940383836 × 10<sup>−5</sup> × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer A6P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −0.305726611559842 + 2.07597152667249 × 10<sup>−6</sup> × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer A7P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −2.10986538059255 + 0.000117160946097971 × x.</p>
Full article ">Figure A18
<p>Groundwater depth and precipitation before and after rewetting in piezometer A8P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −0.634947112113461 + 3.00869747950434 × 10<sup>−5</sup> × x.</p>
Full article ">Figure A19
<p>Groundwater depth and precipitation before and after rewetting in piezometer A10P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −0.941198521843798 + 5.08352721298071 × 10<sup>−5</sup> × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer M6P (control piezometer). Red line represents the date of peat dam construction; gray line represents a trend line). Trend line equation: y = −0.686592923503051 + 2.25822604941247 × 10<sup>−5</sup> × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer M1P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −0.644078444823363 + 2.91070802712557 × 10<sup>−5</sup> × x.</p>
Full article ">Figure A22
<p>Groundwater depth and precipitation before and after rewetting in piezometer M2P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −0.0632860575727428 + -3.55878203137512 × 10<sup>−6</sup> × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer M3P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −0.452081832899084 + 1.87555571383385 × 10<sup>−5</sup> × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer M4P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −2.06541350159327 + 0.000107846578587073 × x.</p>
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<p>Groundwater depth and precipitation before and after rewetting in piezometer M5P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −1.10483425625354 + 5.31894780911463 × 10<sup>−5</sup> × x.</p>
Full article ">Figure A26
<p>Groundwater depth and precipitation before and after rewetting in piezometer M7P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −1.74867144833594 + 8.39537419663912 × 10<sup>−5</sup> × x.</p>
Full article ">Figure A27
<p>Groundwater depth and precipitation before and after rewetting in piezometer M8P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −0.632890216671114 + 3.01224454111323 × 10<sup>−5</sup> × x.</p>
Full article ">Figure A28
<p>Groundwater depth and precipitation before and after rewetting in piezometer M9P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −0.990506727499476 + 3.56123765081276 × 10<sup>−5</sup> × x.</p>
Full article ">Figure A29
<p>Groundwater depth and precipitation before and after rewetting in piezometer M10P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −2.40381758748658 + 0.00012206384827724 × x.</p>
Full article ">Figure A30
<p>Groundwater depth and precipitation before and after rewetting in piezometer M11P. Red line represents the date of peat dam construction; gray line represents a trend line. Trend line equation: y = −1.40014199790285 + 6.32920737061399 × 10<sup>−5</sup> × x.</p>
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18 pages, 7698 KiB  
Article
Plant Adaptation and Soil Shear Strength: Unraveling the Drought Legacy in Amorpha fruticosa
by Hao Jiang, Xiaoqing Chen, Gang Xu, Jiangang Chen, Dongri Song, Ming Lv, Hanqing Guo and Jingyi Chen
Plants 2025, 14(2), 179; https://doi.org/10.3390/plants14020179 - 10 Jan 2025
Viewed by 582
Abstract
Climate change has led to an increasing frequency of droughts, potentially undermining soil stability. In such a changing environment, the shallow reinforcement effect of plant roots often fails to meet expectations. This study aims to explore whether this is associated with the alteration [...] Read more.
Climate change has led to an increasing frequency of droughts, potentially undermining soil stability. In such a changing environment, the shallow reinforcement effect of plant roots often fails to meet expectations. This study aims to explore whether this is associated with the alteration of plant traits as a response to environmental change. Focusing on Amorpha fruticosa, a species known for its robust root system that plays a crucial role in soil consolidation and slope stabilization, thereby reducing soil and water erosion, we simulated a drought-rewetting event to assess the legacy effects of drought on the soil shear strength and the mechanical and hydrological traits associated with the reinforcement provided by A. fruticosa. The results show that the legacy effect of drought significantly diminishes the soil shear strength. Pretreated with drought, plant roots undergo morphological alterations such as deeper growth, yet the underground root biomass and diameter decline, thereby influencing mechanical reinforcement. Chemical composition analysis indicates that the plant’s adaptation to drought modifies the intrinsic properties of the roots, with varying impacts on different root types and overall reinforcement. Concurrently, the stomatal conductance and transpiration rate of leaves decrease, weakening the capacity to augment soil matric suction through transpiration and potentially reducing hydrological reinforcement. Although rewetting treatments aid in recovery, drought legacy effects persist and impact plant functional attributes. This study emphasizes that, beyond soil matric suction, plant adaptive mechanisms in response to environmental changes may also contribute significantly to reduced soil shear strength. Consequently, ecological restoration strategies should consider plant trait adaptations to drought, enhancing root systems for soil conservation and climate resilience. Full article
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Figure 1

Figure 1
<p>Cumulative curve of particle size distribution. * According to the Unified Soil Classification System (USCS, ASTM D2487-17), the soil used in the present study was classified as lean clay (CL). <span class="html-italic">Cu</span>, coefficient of uniformity (<span class="html-italic">D</span><sub>60</sub>/<span class="html-italic">D</span><sub>10</sub>); <span class="html-italic">Cc</span>, coefficient of curvature.</p>
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<p>(<b>a</b>) Stress–strain curves obtained for bare and rooted soils. Vegetation significantly enhanced soil shear strength compared to bare soils, while drought-stressed plants develop deeper root systems, but with relatively lower root biomass. (<b>b</b>) Comparison of soil shear stress values measured by direct shear tests and those predicted by the Wu and Waldron Model (WWM). (<b>c</b>) Failure characteristics of the direct-shear root-permeated sample. Non-drought group: the soil was conditioned by <span class="html-italic">Amorpha fruticosa</span> plants assigned to an average rainfall treatment receiving 70 mm of water per month. Previous drought group: the soil was conditioned by <span class="html-italic">A. fruticosa</span> plants assigned to a drought treatment with no irrigation for 60 days followed by a rewetting treatment. ■ and ▲, respectively, represent the soil shear stress values of the non-drought group and previous drought group obtained from direct shear tests; □ and △, respectively, represent the soil shear stress values of the non-drought group and previous drought group predicted by the WWM.</p>
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<p>Legacy effects of drought on (<b>a</b>) root diameter, (<b>b</b>) specific root length, and (<b>c</b>) root tissue density in <span class="html-italic">A. fruticosa</span> plants. The bars with different letters are significantly different from each other (n = 10, <span class="html-italic">p</span> &lt;0.05).</p>
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<p>Legacy effects of drought on (<b>a</b>) stomatal conductance, (<b>b</b>) transpiration rate, and (<b>c</b>) photosynthetic water use efficiency in <span class="html-italic">A. fruticosa</span> plants.</p>
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<p>Legacy effects of drought on the parameters of root chemistry in <span class="html-italic">A. fruticosa</span> plants. (<b>a</b>) root carbon concentration (RCC); (<b>b</b>) root nitrogen concentration (RNC); (<b>c</b>) root C:N ratio; (<b>d</b>) nonstructural carbohydrate concentration (NSC); (<b>e</b>) soluble sugars concentration; (<b>f</b>) starch concentration; (<b>g</b>) cellulose concentration; (<b>h</b>) lignin concentration; and (<b>i</b>) root cellulose:lignin ratio. The bars with different letters are significantly different from each other (n = 5, <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 6
<p>Mantel test correlations represent the relationship between plant traits and soil shear stress under (<b>a</b>) nondrought and (<b>b</b>) previous drought treatment conditions, respectively. The width and color of lines indicate Mantel’s <span class="html-italic">p</span>-value and Mantel’s <span class="html-italic">r</span>-value, respectively. Lines with Mantel’s <span class="html-italic">p</span> &lt; 0.05 indicate significant correlations. <span class="html-italic">g</span><sub>s</sub>, stomatal conductance; <span class="html-italic">E</span>, transpiration rate; WUE, water use efficiency; r:s ratio, root/shoot dry mass ratio; RWR, root weight ratio; Z (F). RD, the diameter of zero-order roots (first-order laterals); Z (F). SRL, specific root length of zero-order roots (first-order laterals); Z (F). RTD, root tissue density of zero-order roots (first-order laterals); Z (F). c:l ratio, cellulose/lignin ratio of zero-order roots (first-order laterals).</p>
Full article ">
21 pages, 6313 KiB  
Article
Contemporary Evolution and Water Quality of Lakes Rewetted After 19th Century Drainage in the Olsztyn Lake District (Poland)
by Andrzej Skwierawski
Water 2024, 16(24), 3661; https://doi.org/10.3390/w16243661 - 19 Dec 2024
Viewed by 743
Abstract
Rewetting of drained wetlands is practiced as a method to enhance biodiversity, improve water and nutrient retention, and counteract climate change. While rewetting efforts are most commonly directed toward various types of wetlands, there are relatively few reports on the restoration of lakes [...] Read more.
Rewetting of drained wetlands is practiced as a method to enhance biodiversity, improve water and nutrient retention, and counteract climate change. While rewetting efforts are most commonly directed toward various types of wetlands, there are relatively few reports on the restoration of lakes drained in the past. The Olsztyn Lake District is a region where extensive, organized drainage works were carried out in the 19th century, leading to the disappearance of numerous lakes. This paper examines the changes that have occurred since the early 19th century in a group of 143 lakes identified as the complete set of fully drained lakes in the region. An analysis of cartographic materials revealed that the total area of these lakes, originally about 3000 hectares, was reduced to nearly zero by the early 20th century. However, a gradual restoration of the former lakes is now being observed, primarily as a result of spontaneous processes but also through planned interventions. The study of water quality and trophic status in 25 fully restored lakes revealed that such water bodies typically exhibit unfavorable conditions, primarily due to excessive phosphorus levels. A similar state was observed in 14 examined wetlands, which were partially rewetted. In the absence of organized restoration programs in the study region, the slow trend of passive rewetting of such water bodies is expected to continue. However, this process may be hindered by adverse hydroclimatic changes associated with ongoing climate warming. Full article
(This article belongs to the Section Water Quality and Contamination)
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Figure 1

Figure 1
<p>The location of the study area and the objects included in the detailed studies in 2011, 2012, and 2021, belonging to the respective types 1, 2, and 3, as well as other drained lakes in the Olsztyn Lake District; the base map is derived from the cartographic database and used under the terms of the open resource of the geoportal.gov.pl website.</p>
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<p>Changes in the total surface area of 143 lakes in the Olsztyn Lake District; 1802–1980 refers to the lake areas marked on topographic maps from various years, while 1990–2021 covers the surface area of open water observed on Landsat satellite images (1990, 2000, and 2021L) and orthophotomaps (2002, 2009, and 2021A).</p>
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<p>Number of objects belonging to each type (1–3) and subtype (1A–3B) of drained lakes in the Olsztyn Lake District; descriptions of types are in <a href="#sec2dot2dot1-water-16-03661" class="html-sec">Section 2.2.1</a>.</p>
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<p>Examples of three distinguished types of objects. Type 1: restored lake (Sawąg); Type 2: wetland object, partially re-watered (former lake Paniany); Type 3: object currently in a fully drained state (former lake Krokowo); photos made by the author.</p>
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<p>Changes in the subtypes of the studied objects (<span class="html-italic">n</span> = 143) from 2002 to 2021. A plus sign indicates a transition to categories with a higher degree of rewetting, while a minus sign indicates an increase in vegetation or drainage levels of the object.</p>
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<p>Average values (middle line), standard error of the mean (box), and extreme values (whiskers) of basic water quality indicators for the three identified types of study objects; average values were from the spring and summer seasons in 2011, 2012, and 2021. The letters a, b, etc., indicate statistically significant differences in the LSD test for specific years and objects; and A, B, etc., refer to the statistical significance of differences in the LSD test between the object types.</p>
Full article ">Figure 7
<p>The average values (represented by the central line), standard error of the mean (box), and extreme values (whiskers) of phosphorus and nitrogen concentrations in the water for the three identified types of study objects; average values were from the spring and summer seasons in the years 2011, 2012, and 2021. The labels a, b, (…) indicate statistically significant groups that differ in the LSD test for individual years and objects; and A, B, (…) refer to the statistical significance of differences in the LSD test between object types.</p>
Full article ">Figure 8
<p>Trophic state of the studied objects of Type 1 (restored lakes) and Type 2 (wetlands, partially rewatered) in the years 2011, 2012, and 2021, determined based on phosphorus concentration, nitrogen concentration, chlorophyll-a, and Secchi depth, along with the overall result. Trophic state classifications: O—oligotrophic, M—mesotrophic, E—eutrophic, P—polytrophic, H—hypertrophic.</p>
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<p>Dependencies between trophic state indicators and selected indicators of Type 1 lakes (fully restored, <span class="html-italic">n</span> = 25).</p>
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<p>Values and trends of climatic indicators: annual average temperature, annual total precipitation, and the values of the annual and summer half-year climatic water balance for Olsztyn in the years 1951–2023.</p>
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17 pages, 3398 KiB  
Article
Combined Effects of Drying–Rewetting and Ammonium Addition on Methanotrophs in Agricultural Soil: A Microcosm Study
by Irina K. Kravchenko, Aleksei O. Zverev, Liana G. Gogmachadze and Aleksey L. Stepanov
Agriculture 2024, 14(12), 2243; https://doi.org/10.3390/agriculture14122243 - 7 Dec 2024
Viewed by 730
Abstract
Oxidation of methane by soil microorganisms is an important mechanism controlling the content of this potent greenhouse gas in the atmosphere. Agricultural soils operate under stressful conditions, and ammonium (N-fertilization) and drying (global warming) may have a significant impact on methane oxidation. In [...] Read more.
Oxidation of methane by soil microorganisms is an important mechanism controlling the content of this potent greenhouse gas in the atmosphere. Agricultural soils operate under stressful conditions, and ammonium (N-fertilization) and drying (global warming) may have a significant impact on methane oxidation. In order to investigate how soil methanotrophs respond to drying–rewetting (DW), ammonium addition (100 mg/g) (A), and their combined action (MS), agricultural soil microcosms were incubated over the three months and methane oxidation was measured before and after perturbations, while community composition was monitoring using 16S rRNA gene sequencing. A significant decline in the methane-oxidation activity after perturbations was found, with subsequent restoration, and the combined treatment was more effective than the sum of individual treatments, indicating a synergistic effect. After rewetting, the structure of the bacterial community returned to pre-dry-down levels, but the application of ammonia and combined action lead to irreversible changes in the structure of soil methanotrophic communities. Methanotroph Methylomicrobium were significantly reduced under disturbances, while there was a significant increase in the representation of Methylobacter accompanied by the facultative methylotroph Methylovorus. We concluded that methanotrophic communities in agricultural soil demonstrated flexibility, and even when the abundance of dominant populations drops, ecosystem functions can recover. Full article
(This article belongs to the Section Agricultural Soils)
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Figure 1

Figure 1
<p>A schematic diagram of microcosm setup and analysis in this study. Red arrows indicate soil drying events. Blue arrows indicate water addition. White arrows indicate ammonium addition. The red dot indicates the events of DNA extraction, while the green dots indicate methane oxidation estimation.</p>
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<p>Methane uptake rates in the undisturbed and disturbed incubations determined for the intervals 0–1, 1–7, 7–14, and 14–28 days after drying–rewetting, ammonium application, and multi-stress treatments. No significant differences at <span class="html-italic">p</span> ≤ 0.05 (*); <span class="html-italic">p</span> ≤ 0.01 (**); <span class="html-italic">p</span> ≤ 0.001 (***); <span class="html-italic">p</span> ≤ 0.0001 (****), respectively.</p>
Full article ">Figure 3
<p>Comparisons of the alpha diversity index’s dynamics across several treated soils. Abbreviations: no impact (C), ammonium addition (A), drying–rewetting (DW), combined action of stressors (MS).</p>
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<p>PCoA ordination of different beta diversity indices of microbial communities. Ellipses indicate 95% confidence intervals.</p>
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<p>Abundances of different prokaryotic taxa, determined as the taxa with different abundance levels across incubation time, based on the 16S rRNA gene sequence analysis.</p>
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<p>Abundances of different prokaryotic taxa, determined as the taxa with different abundance levels across treatment, based on the 16S rRNA gene sequence analysis after incubation at 2, 4, and 12 weeks intervals.</p>
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<p>Relative abundance of the methano- and methylotrophs in control, drying–rewetting, ammonium, and multi-stress treatments in soil samples. Data is based on 16S rRNA gene sequencing data.</p>
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12 pages, 4935 KiB  
Article
Enhanced Pool Boiling Heat Transfer on Hybrid Wettability Downward-Facing Surfaces: Impact of Interfacial Phenomena and Rewetting Characteristics
by Xiaojia Li, Qingyun Long, Jingtao Xue, Zhiguang Liang, Binghuo Yan and Laishun Wang
Energies 2024, 17(23), 5849; https://doi.org/10.3390/en17235849 - 22 Nov 2024
Viewed by 588
Abstract
The nucleation and growth of bubbles on homogeneous wetting surfaces have been extensively studied, but the intricate dynamics on hybrid wetting surfaces remain under-explored. This research aims to elucidate the impact of hybrid wettability on pool boiling heat transfer efficiency, specifically under downward-facing [...] Read more.
The nucleation and growth of bubbles on homogeneous wetting surfaces have been extensively studied, but the intricate dynamics on hybrid wetting surfaces remain under-explored. This research aims to elucidate the impact of hybrid wettability on pool boiling heat transfer efficiency, specifically under downward-facing heating conditions. To this end, a series of hybrid wettability surfaces with varying hydrophilic and hydrophobic configurations are meticulously fabricated and analyzed. The study reveals distinctive interfacial phenomena occurring at the boundary between hydrophilic and hydrophobic regions during the boiling process. Experimental results indicate that surfaces with a higher proportion of hydrophilic to hydrophobic interfaces exhibit reduced superheat requirements and enhanced boiling heat transfer coefficients for equivalent heat flux densities. Furthermore, the rewetting characteristics of hybrid wettability surfaces are identified as pivotal factors in determining their critical heat flux (CHF). This investigation underscores the potential of hybrid wettability surfaces to optimize pool boiling heat transfer, offering valuable insights for the design and en-hancement of heat exchangers and other thermal management systems. Full article
(This article belongs to the Section J: Thermal Management)
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<p>Schematic diagram of pool boiling experimental facility.</p>
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<p>List of the surface types and corresponding parameters.</p>
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<p>Nucleation conditions under different heat flux densities on different surfaces.</p>
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<p>The behavior of bubbles on surface #1.</p>
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<p>Snapshots of rewetting after bubble detachment on different surfaces. (<b>a</b>) Surface #1, (<b>b</b>) #2, (<b>c</b>) #3, (<b>d</b>) #4, and (<b>e</b>) #5.</p>
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<p>Boiling curves on different surfaces. (<b>a</b>) Boiling heat transfer curve; (<b>b</b>) boiling heat transfer coefficient variation curve with superheat degree; and (<b>c</b>) boiling heat transfer coefficient increment curve with superheat degree.</p>
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<p>Direction of liquid replenishment when rewetting different surfaces.</p>
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<p>Comparison between predicted critical heat fluxes and experimental data.</p>
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16 pages, 3980 KiB  
Article
Planting Ages Inhibited Soil Respiration and CO2-C Emissions Attribute to Soil Degradation in Gravel-Mulched Land in Arid Areas
by Bingyao Wang, Yunfei Li, Zhixian Liu, Peiyuan Wang, Zhanjun Wang, Xudong Wu, Yongping Gao, Lichao Liu and Haotian Yang
Land 2024, 13(11), 1923; https://doi.org/10.3390/land13111923 - 15 Nov 2024
Viewed by 660
Abstract
Gravel mulching is a widely employed strategy for water conservation in arid agricultural regions, with potential implications for soil carbon (C) sequestration and greenhouse gas emissions. However, soil respiration and CO2-C emissions remain uncertain owing to less consideration of the influence [...] Read more.
Gravel mulching is a widely employed strategy for water conservation in arid agricultural regions, with potential implications for soil carbon (C) sequestration and greenhouse gas emissions. However, soil respiration and CO2-C emissions remain uncertain owing to less consideration of the influence of precipitation patterns and planting age. In this study, we investigated the soil respiration rate (Rsoil) and cumulative CO2-C emission (Ccum), both measured over a period of 72 h, along with soil properties and enzyme activities under different precipitation conditions based on gravel mulching with different planting ages. We analyzed the effects of planting ages on Rsoil and Ccum and revealed the underlying mechanisms driving changes in environmental factors on Rsoil and Ccum. The results demonstrated that the Rsoil reached the maximum value at about 1 h, 0.5 h, and 0.25 h after rewetting in 1, 10, and 20 years of gravel mulching under the condition with 1 mm, 5 mm, and 10 mm of precipitation, respectively, whereas the Rsoil exhibited its maximum at about 8 h after soil rewetting under precipitation of 30 mm. The Ccum induced by precipitation pulses tends to decrease with increasing years of gravel mulching. The Ccum was 0.0061 t ha−1 in the 20-year gravel-mulched soil, representing a 53.79% reduction compared to the 1-year gravel-mulched soil. Soil organic matter (SOM), planting ages, and alkaline phosphatase (ALP) were the primary factors influencing the Rsoil and Ccum in 0–20 cm, while SOM, planting ages, and soil porosity (AirP) were the key factors affecting the Rsoil and Ccum in 20–40 cm. The Rsoil and Ccum in the 0–20 cm soil were regulated by soil enzyme activities, while those of 20–40 cm soil were controlled by soil properties. This indicates that the decrease in Rsoil and Ccum is caused by soil degradation, characterized by a decrease in SOM and ALP. This study offers a novel insight into the long-term environmental impact of gravel mulching measures in arid areas, which is helpful in providing a theoretical basis for dryland agricultural management. It is imperative to consider the duration of gravel mulching when predicting the potential for C sequestration in arid agricultural areas. Full article
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<p>Elevation map of the study areas and sampling site: (<b>a</b>) provincial administrative (Ningxia Hui Autonomous Region, China) elevation map of the study areas; (<b>b</b>) general view of the research sites; (<b>c</b>) samples gravel-mulched for 1 year; (<b>d</b>) samples gravel-mulched for 10 years; (<b>e</b>) samples gravel-mulched for 20 years.</p>
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<p>Dynamic variation characteristics of soil respiration under different precipitation conditions. Note: Y<sub>1</sub>, gravel-mulched land with 1 year; Y<sub>10</sub>, gravel-mulched land with 10 years; Y<sub>20</sub>, gravel-mulched land with 20 years.</p>
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<p>Cumulative CO<sub>2</sub>-C emission of soil respirable C within 72 h after precipitation. Note: The presence of prominent capital letters indicates statistically significant differences in CO<sub>2</sub>-C emissions across various precipitation conditions, with a significance level of <span class="html-italic">p</span> &lt; 0.05. Similarly, the occurrence of a distinct lowercase letter signifies a statistically significant difference in CO<sub>2</sub>-C emissions between different years of gravel-mulched tillage.</p>
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<p>Correlation of precipitation, planting ages, soil physicochemical properties, and enzyme activities in 0–20 cm (<b>a</b>) and 20–40 cm (<b>b</b>) soils. P, precipitation; PA, planting age; BD, bulk density; Pro, soil porosity; AirP, soil air permeability; SOM, soil organic matter; AN, available nitrogen; AK, available potassium; AP, Available phosphorus; ALP, alkaline phosphatase; CAT, catalase; URE, urease; <span class="html-italic">R<sub>max</sub></span>, maximum rate of CO<sub>2</sub>-C emission; <span class="html-italic">C<sub>cum,</sub></span> the cumulative CO<sub>2</sub>-C emission. Note: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>RDA ordination plots of precipitation, planting ages, soil physicochemical properties, and enzyme activities for 0–20 cm (<b>a</b>) and 20–40 cm (<b>b</b>) soils. Note: PA, planting age; BD, bulk density; Pro, soil porosity; AirP, soil air permeability; SOM, soil organic matter; AN, available nitrogen; AK, available potassium; AP, Available phosphorus; ALP, alkaline phosphatase; CAT, catalase; URE, urease; <span class="html-italic">R<sub>max</sub></span>, the maximum rate of CO<sub>2</sub>-C emission; <span class="html-italic">C<sub>cum</sub></span>, the cumulative CO<sub>2</sub>-C emission.</p>
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<p>Structural equation modeling of soil respiration as a function of planting ages and precipitation variability: (<b>a</b>,<b>b</b>) the 0–20 cm soil layer; (<b>c</b>,<b>d</b>) the 20–40 cm soil layer. Note: Numbers adjoining the arrows indicate standardized path coefficients, and the arrow width is proportional to the strength of the association. The red arrow represents the positive correlation, and the blue arrow is the negative correlation. Asterisks indicate significance (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001), and the absence of any marker shows no significance. Variance inflation factors (VIF), coefficient of determination (R<sup>2</sup>), common factor variance (Q<sup>2</sup>), and goodness of fit (GOF) are shown. PA, planting ages; P, precipitation; ALP, alkaline phosphatase; CAT, catalase; Pro, soil porosity; SOM, soil organic matter; CS, clay and silt; AN, available nitrogen; <span class="html-italic">R<sub>ave</sub></span>, average rate of soil respiration; <span class="html-italic">R<sub>cum</sub></span>, cumulative of maximum rate of soil respiration.</p>
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<p>Standardized total effects of environmental factors, soil properties, and enzyme activity in PLS-PM. Note: <span class="html-italic">R<sub>ave</sub></span>, average rate of soil respiration; <span class="html-italic">C<sub>cum</sub></span>, the cumulative CO<sub>2</sub>-C emission.</p>
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12 pages, 1514 KiB  
Article
Accessing Fungal Contributions to the Birch Effect: Real-Time Respiration from Pore-Scale Microfluidics
by Yi-Syuan Guo, Karl K. Weitz, Aramy Truong, Adam G. Ryan, Leslie M. Shor, Arunima Bhattacharjee and Mary S. Lipton
Microorganisms 2024, 12(11), 2295; https://doi.org/10.3390/microorganisms12112295 - 12 Nov 2024
Viewed by 882
Abstract
Drying and rewetting of soil stimulates soil carbon emission. The Birch effect, driven by these cycles, leads to CO2 efflux, which can be monitored using real-time mass spectrometry (RTMS). Although soil fungi retain water during droughts, their contribution to CO2 release [...] Read more.
Drying and rewetting of soil stimulates soil carbon emission. The Birch effect, driven by these cycles, leads to CO2 efflux, which can be monitored using real-time mass spectrometry (RTMS). Although soil fungi retain water during droughts, their contribution to CO2 release during drying–rewetting cycles remains unclear. In this study, we present the first demonstration of integrating micromodels with RTMS to monitor the Birch effect by simulating drought and rewetting. Micromodels were inoculated with axenic fungal culture and dried to assess moisture retention. After drying, RTMS quantified CO2 release upon rewetting with H218O mixtures. Our results showed that soil fungi released CO2 upon rehydration and immediately utilized the external water source at the pore scale by generating subsequent 46CO2. This work is the first to integrate RTMS with microsystems to investigate pore-scale biogeochemistry and the involvement of fungi in the Birch effect. Full article
(This article belongs to the Section Microbiomes)
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<p>The overall scheme of this work and detailed assembly information. (<b>A</b>) Schematic representation of the experimental approach: integration of real-time mass spectrometry (RTMS) with a microfluidics model to investigate soil microbes at the pore scale. (<b>B</b>) Detailed schematic of the micromodel setup connected to the real-time mass spectrometry (RTMS) system. The diagram illustrates the interface between the microfluidics model and the RTMS. (<b>C</b>) Images of the real device assembly from both a top view (3 channels for liquid flow) and a side view, showcasing the setup and structural components of the microfluidics system integrated with the RTMS.</p>
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<p>Workflow of this study and example images from monitoring. (<b>A</b>) Fungal inoculation into microfluidics. (<b>B</b>) Microfluidics covered with a PDMS square. (<b>C</b>) Incubation at 25 °C for 10 days. (<b>D</b>) Monitoring of drying experiments from microfluidics. (<b>E</b>) RTMS monitoring during rewetting of microfluidics. (<b>F</b>) RTMS data analysis. (<b>G</b>) Example images of fungi inoculated within microfluidics. The completely dark channel indicates full saturation after 10 days of incubation. The circled area highlights fungal movement within the geometry. (<b>H</b>) Example channel image of a 0-week dry sample. (<b>I</b>) Example channel image of a 1-week dry sample. (<b>J</b>) Example channel image of a 2-week dry sample.</p>
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<p>RTMS monitoring in a fungal microfluidics rewetting system. (<b>A</b>) Figure shows the apex (intensity per mg) of <sup>44</sup>CO<sub>2</sub> reported (<span class="html-italic">n</span> = 3). “*NS” indicates no significant difference (<span class="html-italic">p</span> &gt; 0.05) between treatments. (<b>B</b>) Example monitoring of <sup>44</sup>CO<sub>2</sub> and <sup>46</sup>CO<sub>2</sub> upon hydration. Hydration was initiated at the 46th minute. During the first 5 min prior to hydration, both <sup>44</sup>CO<sub>2</sub> and <sup>46</sup>CO<sub>2</sub> showed stable signals. Upon rewetting the fungal microfluidics with a food dye and <sup>18</sup>H<sub>2</sub>O mixture, the <sup>44</sup>CO<sub>2</sub> peak sharply increased. After approximately 10 min, the <sup>46</sup>CO<sub>2</sub> peak began to slowly rise, indicating fungal uptake of the reintroduced water. The experiment was terminated when the microflow was affected by moisture.</p>
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14 pages, 1988 KiB  
Article
Impact of Drying–Wetting Cycles on Nitrification Inhibitors (DMPP and DMPSA) in a Greenhouse Experiment with Two Contrasting Mediterranean Soils
by Laura Sánchez-Martin, Adrián Bozal-Leorri, Janaina M. Rodrigues, Carmen González-Murua, Pedro Aparicio, Sonia García-Marco and Antonio Vallejo
Agronomy 2024, 14(11), 2620; https://doi.org/10.3390/agronomy14112620 - 6 Nov 2024
Viewed by 1012
Abstract
Studies of the impact of nitrification inhibitors (NIs), specifically DMPP and DMPSA, on N2O emissions during “hot moments” have produced conflicting results regarding their effectiveness after rewetting. This study aimed to clarify the effectiveness of NIs in reducing N2O [...] Read more.
Studies of the impact of nitrification inhibitors (NIs), specifically DMPP and DMPSA, on N2O emissions during “hot moments” have produced conflicting results regarding their effectiveness after rewetting. This study aimed to clarify the effectiveness of NIs in reducing N2O emissions by assessing residual DMP concentration and its influence on ammonia-oxidizing bacteria (AOB) in two pot experiments using calcareous (Soil C, Calcic Haploxerept) and acidic soils (Soil A, Dystric Xerochrepts). Fertilizer treatments included urea (U), DMPP, and DMPSA. The experiments were divided into Phase I (water application to dry period, 44 days) and Phase II (rewetting from days 101 to 121). In both phases for Soil C, total N2O emissions were reduced by 88% and 90% for DMPP and DMPSA, respectively, compared with U alone. While in Phase I, the efficacy of NIs was linked to the regulation of AOB populations, in Phase II this group was not affected by NIs, suggesting that nitrification may not be the predominant process after rewetting. In Soil A, higher concentrations of DMP from DMPP were maintained compared to Soil C at the end of each phase. Despite this, NIs had no significant effect due to low nitrification rates and limited amoA gene abundance, indicating unfavorable conditions for nitrifiers. The study highlights the need to optimize NIs to reduce N2O emissions and improve nitrogen efficiency, while understanding their interactions with the soil. This knowledge is necessary in order to design fertilization strategies that improve the sustainability of agriculture under climate change. Full article
(This article belongs to the Section Soil and Plant Nutrition)
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<p>Weekly estimates of water-filled pore space (WFPS) in the calcareous (blue color) and acidic (orange color) soils during both phases: Phase I (from 0 to 44 days) and Phase II, rewetting (from 101 to the end of the experiment). DAF: days after fertilization. The vertical bars indicate standard errors (n = 3).</p>
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<p>Daily (<b>a</b>,<b>c</b>) and cumulative (<b>b</b>,<b>d</b>) N<sub>2</sub>O emissions from the calcareous (<b>a</b>,<b>b</b>) and acidic (<b>b</b>,<b>d</b>) soils. DAF: days after fertilization. Black arrows indicate the rewetting. For cumulative emissions, significant differences (α &lt; 0.05) between treatments within the same soil are indicated with capital and lowercase letters for Phases I and II, respectively. The vertical bars indicate standard errors (n = 3). Significant interactions (α = 0.05) between treatment and soil type in each phase are indicated with a (S).</p>
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<p>Soil mean of NH<sub>4</sub><sup>+</sup> (<b>a</b>,<b>c</b>) and NO<sub>3</sub><sup>−</sup> (<b>b</b>,<b>d</b>) concentrations in the first and second phases in the calcareous (<b>a</b>,<b>b</b>) and acidic (<b>c</b>,<b>d</b>) soils. Significant differences (<span class="html-italic">p</span> &lt; 0.05) between treatments within the same phase are indicated with capital and lowercase letters for Phases I and II, respectively. Asterisks (*) indicate significant differences between the two phases for the same treatment in (α &lt; 0.05). The vertical bars indicate standard errors (n = 3). Significant interactions (α = 0.05) between treatment and soil type in each phase are indicated with a (S).</p>
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<p>DMP (from DMPP) concentrations at different soil depths at the end of each phase in both experiments: calcareous soil (<b>a</b>) and acidic soil (<b>b</b>). Different letters above the bars indicate significant differences (α &lt; 0.05) between depths within the same phase (capital and lowercase letters for Phases I and II, respectively). Asterisks (*) indicate significant differences (α &lt; 0.05) between phases at the same depth. The vertical bars indicate standard errors (n = 3).</p>
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<p>Ammonia oxidizing bacteria (AOB) relative abundance (measured as the relative abundance of the <span class="html-italic">amoA</span> gene), in the calcareous (<b>a</b>) and acidic soils (<b>b</b>), respectively. Significant differences (α &lt; 0.05) between treatments within the same soil are indicated with capital and lowercase letters for Phase I and Phase II, respectively. Asterisks (*) indicate significant differences between the two phases within the same treatment (α &lt; 0.05). The vertical bars indicate standard errors (n = 3). Significant interactions (α = 0.05) between treatment and soil type in each phase are indicated with a (S).</p>
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32 pages, 2391 KiB  
Review
Temperate Soils Exposed to Drought—Key Processes, Impacts, Indicators, and Unknowns
by Sabine Reinsch, David A. Robinson, Maud A. J. van Soest, Aidan M. Keith, Simon Parry and Andrew M. Tye
Land 2024, 13(11), 1759; https://doi.org/10.3390/land13111759 - 26 Oct 2024
Cited by 2 | Viewed by 1587
Abstract
The summer drought in the United Kingdom (UK) in 2022 produced significant speculation concerning how its termination may impact and interact with the soil resource. Whilst knowledge regarding soils and droughts exists in the scientific literature, a coherent understanding of the wider range [...] Read more.
The summer drought in the United Kingdom (UK) in 2022 produced significant speculation concerning how its termination may impact and interact with the soil resource. Whilst knowledge regarding soils and droughts exists in the scientific literature, a coherent understanding of the wider range of impacts on soil properties and functions has not been compiled for temperate soils. Here, we draw together knowledge from studies in the UK and other temperate countries to understand how soils respond to drought, and importantly what and where our knowledge gaps are. First, we define the different types of droughts and their frequency in the UK and provide a brief overview on the likely societal impacts that droughts place on the soil and related ecosystems. Our focus is on ‘agricultural and ecosystem drought’, as this is when soils experience dry periods affecting crops and ecosystem function, followed by rewetting. The behaviour of moisture in soils and the key processes that contribute to its storage and transport are examined. The principal changes in the physical, chemical, and biological properties of soils resulting from drought, and rewetting (i.e., drought termination) are discussed and their extensive interactions are demonstrated. Processes that are involved in the rewetting of soils are explored for soil and catchment-scale soil responses. Lastly, soils’ recovery after drought is considered, knowledge gaps are identified, and areas to improve understanding are highlighted. Full article
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<p>Visual impacts of drought on soils: (<b>a</b>) field moist and (<b>b</b>) dried and shrunk organic soil, (<b>c</b>) field moist and (<b>d</b>) dried and cracked soil, (<b>e</b>) shrunken topsoil (0–5 cm), (<b>d</b>) compared to the cracked subsoil of the same soil core, (<b>f</b>) soil parent materials in England and Wales, UK, which may exhibit shrink–swell characteristics due to smectitic clay concentrations. Pictures (<b>a</b>–<b>e</b>) were taken by I. Lebron (UKCEH). (<b>f</b>) BGS Geology Data © UKRI and OS data © Crown copyright and database right 2022.</p>
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<p>Basic processes in the soil water balance.</p>
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<p>Schematic overview of changes in infiltration rates with time and soil condition. Reprinted from…, 2023, Environment Agency.</p>
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<p>Relationship between water activity and water potential.</p>
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<p>Theoretical framework of how drought affects soil microbes, and how these link to the soil carbon pool (Reprinted from Malik and Bouskill (2022) [<a href="#B70-land-13-01759" class="html-bibr">70</a>] under the CC BY 4.0 license).</p>
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<p>Picture showing water drops on the soil surface visualising soil water repellency. Picture taken by D.A. Robinson (UKCEH).</p>
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<p>Impact of different agricultural management practices on infiltration rates of cropland soils (Reprinted from Basche and DeLonge (2019) [<a href="#B59-land-13-01759" class="html-bibr">59</a>] under the CC BY 4.0 license).</p>
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18 pages, 5341 KiB  
Article
Prevalence and Diversity of Plant Parasitic Nematodes in Irish Peatlands
by Anusha Pulavarty, Tilman Klappauf, Ankit Singh, Patricia Molero Molina, Anique Godjo, Bastiaan Molleman, Douglas McMillan and Thomais Kakouli-Duarte
Diversity 2024, 16(10), 639; https://doi.org/10.3390/d16100639 - 15 Oct 2024
Viewed by 1266
Abstract
The prevalence of plant parasitic nematodes (PPN) in the Irish peatlands was investigated in five different peatland habitats—raised bog, cutover scrub/woodlands, fens and peat grasslands, which were further sub-categorised into fourteen different sub-habitats. Within the raised bog habitat were healthy bog hummock (HBH), [...] Read more.
The prevalence of plant parasitic nematodes (PPN) in the Irish peatlands was investigated in five different peatland habitats—raised bog, cutover scrub/woodlands, fens and peat grasslands, which were further sub-categorised into fourteen different sub-habitats. Within the raised bog habitat were healthy bog hummock (HBH), healthy bog lawn (HBL), degraded bog hummock (DBH) and degraded bog lawn (DBL) and the fen habitats were fen peat (FP) and rich fen peat (R-FP). Cutover scrub or woodland habitat included cutover scrub rewetted (C-RW), cutover scrub non-rewetted (C-NRW), woodlands rewetted (W-RW) and woodlands non-rewetted (W-NRW). Grassland included wasted peat (WP), rough grazing (RG-I) and improved fen peat grassland (IFPG-RW and IFPG-NRW). Soil samples from peatlands were all collected between July and December 2023 when the temperature ranged from 12 to 20 °C. One half of each sample was used for molecular nematode analysis and the other half for morphological identification of nematodes. For the morphological identification, a specific nematode extraction protocol was optimised for peatland soils, and the extracted nematodes were fixed onto slides to be studied under a high-power light microscope. Subsequently, the other part of the soil was processed to isolate total DNA, from which the 18S rRNA gene was sequenced for the identification of nematode taxa. The extracted DNA was also used for randomly amplified polymorphic DNA (RAPD) fingerprinting analysis to determine banding patterns that could classify different bog habitats based on PPN random primers. Compared to that in the climax habitats (HBH, HBL, DBH, DBL, FP, R-FP), PPN prevalence was recorded as being higher in grasslands (WP, RG-I, IFPG-RW and IFPG-NRW) and scrub/woodland ecosystems (C-RW, C-NRW, W-RW, W-NRW). The results indicate that nematode populations are different across the various bog habitats. Emerging and current quarantine PPN belonging to the families Pratylenchidae, Meloidogynidae, Anguinidae and Heteroderidae were noted to be above the threshold limits mentioned under EPPO guidelines, in grassland and wooded peatland habitats. Future actions for PPN management may need to be considered, along with the likelihood that these PPN might impact future paludiculture and other crops and trees growing in nearby agricultural lands. Full article
(This article belongs to the Section Biodiversity Conservation)
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<p>(<b>a</b>) Approximate site location in the Republic of Ireland; (<b>b</b>) Bog sampling location and bog habitats in each location, (i) 53°01′14.2″ N and 7°57′15.5″ W, (ii) 53°05′14.01″ N and 7°87′69.96″ W, (iii) 53°06′08.4″ N and 7°80′08.4″ W; source Google Maps.</p>
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<p>RAPD profile of peat habitats: (<b>a</b>) Healthy bog lawn (HBH), (<b>b</b>) Rich Fen peat (R-FP) obtained with primers A5, A6, A7, A9, A10, A12, A13, A15, A16, A18, A19, A20, A22, A24. M = Molecular weight marker (Promega 1 Kb Ladder (G571A)).</p>
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<p>Dendrogram showing the proximity distance between various peatland habitats based on RAPD index data (constructed using IBM SPSS (version 29.0.1.0 (171)).</p>
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<p>Heat map showing the abundance of different nematode families detected in various peat habitats. The PPN families are highlighted using red ovals.</p>
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<p>Relative abundance of PPN (%) in different peatland habitats (molecular data). Values represented by similar letters are not significantly different from each other in terms of PPN % (<span class="html-italic">p</span> ≤ 0.05).</p>
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20 pages, 5323 KiB  
Article
Comparative Photosynthetic Capacity, Respiration Rates, and Nutrient Content of Micropropagated and Wild-Sourced Sphagnum
by Anna T. Keightley, Chris D. Field, James G. Rowson and Simon J. M. Caporn
Int. J. Plant Biol. 2024, 15(4), 959-978; https://doi.org/10.3390/ijpb15040068 - 2 Oct 2024
Cited by 1 | Viewed by 2017
Abstract
The rapid, effective restoration of degraded peatlands is urgently needed to reduce their current high levels of carbon loss. The re-introduction of Sphagnum moss, along with re-wetting, is key to returning carbon sequestration and retention capabilities to northern degraded bogs. Micropropagated Sphagnum has [...] Read more.
The rapid, effective restoration of degraded peatlands is urgently needed to reduce their current high levels of carbon loss. The re-introduction of Sphagnum moss, along with re-wetting, is key to returning carbon sequestration and retention capabilities to northern degraded bogs. Micropropagated Sphagnum has already been applied in large quantities, and more is planned, for restoration projects in Britain and parts of Europe. A comparison with wild-sourced Sphagnum material is therefore pertinent to demonstrate its safety and suitability for wide-scale application. Six Sphagnum species of both micropropagated and wild-sourced origin were assessed for photosynthetic capacity, nutrient content, form parity, chlorocyst size, and chloroplast numbers. Micropropagated Sphagnum had significantly higher light-saturated photosynthesis (Pmax) rates, little color expression, an open growth habit, greater chloroplast numbers, and more numerous, smaller shoot apices than wild-sourced Sphagnum. Higher Pmax rates were associated with a lower bulk density and higher tissue nutrient concentrations. Potentially, greater chloroplast numbers in micropropagated Sphagnum facilitate higher photosynthesis rates, driving rapid growth in early-stage plants, particularly in optimum moisture conditions. Micropropagated Sphagnum can be used confidently, propagated in large quantities, and will likely establish well on application to sites where re-wetting has already occurred, therefore making it highly beneficial for the restoration of degraded bogs. Full article
(This article belongs to the Section Plant Physiology)
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<p>Examples of <span class="html-italic">Sphagnum</span> species samples for the analysis of the photosynthesis rate. Micropropagated (top row of pair) and wild-sourced <span class="html-italic">Sphagnum</span> species samples show typical visual differences in pigmentation and form.</p>
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<p>Examples of capitula used for microscopic study; arrows indicate typical branch selection.</p>
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<p>Examples of chlorocyst width measurement locations (double-ended arrows) and chloroplasts (single-arrowed) (<b>A</b>) <span class="html-italic">S. palustre</span> and (<b>B</b>) <span class="html-italic">S. squarrosum</span>).</p>
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<p>Comparison of micropropagated and wild-sourced <span class="html-italic">Sphagnum</span> species net photosynthesis (P<sub>n</sub>) response to light. A light intensity range from 0 to 800 µmol (photons) m<sup>−2</sup> s<sup>−1</sup> was used to test each sample. Results are shown on a dry weight basis (<span class="html-italic">n</span> = 5). Crosses indicate the mean, lines indicate the median, and interquartile range is exclusive with maximum and minimum values that are not outliers indicated by the whiskers.</p>
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<p>Comparison of micropropagated and wild-sourced <span class="html-italic">Sphagnum</span> species net photosynthesis (P<sub>n</sub>) response to light. A light intensity range from 0 to 800 µmol (photons) m<sup>−2</sup> s<sup>−1</sup> was used to test each sample. Results are shown on a dry weight basis (<span class="html-italic">n</span> = 5). Crosses indicate the mean, lines indicate the median, and interquartile range is exclusive with maximum and minimum values that are not outliers indicated by the whiskers.</p>
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<p>Inter-species differences in light-saturated photosynthesis (P<sub>max</sub>) and respiration (Resp) rates within <span class="html-italic">Sphagnum</span> types. Statistically significant differences between species on each graph are indicated by shared letters. Crosses indicate the mean, lines indicate the median, and interquartile range is exclusive with maximum and minimum values that are not outliers indicated by the whiskers.</p>
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<p>Inter-species differences in light-saturated photosynthesis (P<sub>max</sub>) and respiration (Resp) rates within <span class="html-italic">Sphagnum</span> types. Statistically significant differences between species on each graph are indicated by shared letters. Crosses indicate the mean, lines indicate the median, and interquartile range is exclusive with maximum and minimum values that are not outliers indicated by the whiskers.</p>
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<p>Comparison of micropropagated and wild-sourced <span class="html-italic">Sphagnum</span> density by fresh weight (FW) and dry weight (DW). Shared letters within types (micropropagated and wild-sourced) indicate significant differences in density between species (one-way ANOVA post hoc Tukey’s HSD). Crosses indicate the mean, lines indicate the median, and interquartile range is exclusive with maximum and minimum values that are not outliers indicated by the whiskers.</p>
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<p>Principal component analysis (PCA: correlation matrix) of <span class="html-italic">Sphagnum</span> element content. PCA graphs show (<b>A</b>) macronutrient and (<b>B</b>) micronutrient and trace element contents of <span class="html-italic">Sphagnum</span> samples with P<sub>max</sub> and respiration by dry weight.</p>
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<p>Examples of <span class="html-italic">Sphagnum</span> samples at 1000× magnification: micropropagated (top of pair) and wild-sourced <span class="html-italic">Sphagnum</span> samples. Scale bar = 50 µm.</p>
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31 pages, 1427 KiB  
Article
Combining Photovoltaics with the Rewetting of Peatlands—A SWOT Analysis of an Innovative Land Use for the Case of North-East Germany
by Melissa Seidel, Sabine Wichmann, Carl Pump and Volker Beckmann
Land 2024, 13(10), 1548; https://doi.org/10.3390/land13101548 - 24 Sep 2024
Cited by 1 | Viewed by 1984
Abstract
Reducing emissions from energy production and enhancing the capacity of land use systems to store carbon are both important pathways towards greenhouse gas neutrality. Expanding photovoltaics (PV) contributes to the former, while the rewetting of drained peatlands preserves the peat soil as long-term [...] Read more.
Reducing emissions from energy production and enhancing the capacity of land use systems to store carbon are both important pathways towards greenhouse gas neutrality. Expanding photovoltaics (PV) contributes to the former, while the rewetting of drained peatlands preserves the peat soil as long-term carbon store, thus contributing to the latter. However, both options are usually considered separately. This study analyses Peatland PV, defined as the combination of open-space PV with the rewetting of peatlands on the same site, and has an explorative and field-defining character. Due to a lack of empirical data, we used expert interviews to identify the strengths and weaknesses, opportunities, and threats of Peatland PV in the sparsely populated and peatland-rich state of Mecklenburg-Western Pomerania in North-East Germany. The material was analysed using a qualitative content analysis and compiled into SWOT and TOWS matrices. Besides the ecological and technological dimensions, this study focuses on the economic and legal framework in Germany. We found that Peatland PV may mitigate land use conflicts by contributing to climate and restoration targets, energy self-sufficiency, and security. Continued value creation can incentivize landowners to agree to peatland rewetting. Technical feasibility has, however, a significant influence on the profitability and thus the prospects of Peatland PV. Although Peatland PV has recently been included in the Renewable Energy Sources Act (EEG), several specialised legal regulations still need to be adapted to ensure legal certainty for all stakeholders. Pilot implementation projects are required to study effects on vegetation cover, soil, peatland ecosystem services, biodiversity, hydrology, and water management, as well as to analyse the feasibility and profitability of Peatland PV. Full article
(This article belongs to the Section Land Socio-Economic and Political Issues)
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<p>Venn diagram to illustrate the different land uses of open-space photovoltaics, agriculture, and peatland rewetting, as well as possible combinations. The position of Peatland PV investigated in this study is highlighted.</p>
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<p>Overall assessment of opportunities and threats of Peatland PV by the 10 interview partners on a scale of 1 (threats very much predominate) to 10 (opportunities very much predominate).</p>
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<p>Venn diagrams of different land uses, based on <a href="#land-13-01548-f001" class="html-fig">Figure 1</a>. Exemplary representation of some SWOT aspects [areas of application (green) and reference (orange)]: (<b>a</b>) Strength of carbon store function and opportunity for climate change mitigation through peatland rewetting compared to drainage-based land use. (<b>b</b>) Weaknesses and threats for species and nature conservation in Peatland PV compared with rewetting without PV. (<b>c</b>) Strength of renewable energy production and opportunity to substitute fossil fuels through land use with PV. (<b>d</b>) Weaknesses for technical project implementation and threat of lacking profitability of PV on rewetted peatlands. (<b>e</b>) Weakness of loss of agricultural biomass production and threat of lacking social acceptance. (<b>f</b>) Strength of synergy effects through land use combinations and opportunity to defuse the land use conflict.</p>
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17 pages, 3225 KiB  
Article
The Effect of Drought on Agronomic and Plant Physiological Characteristics of Cocksfoot (Dactylis glomerata L.) Cultivars
by Lukas Gaier, Erich M. Poetsch, Wilhelm Graiss, Andreas Klingler, Markus Herndl and Bernhard Krautzer
Agriculture 2024, 14(7), 1116; https://doi.org/10.3390/agriculture14071116 - 10 Jul 2024
Cited by 1 | Viewed by 982
Abstract
Cocksfoot (Dactylis glomerata L.) is becoming increasingly important for grassland farming due to climate change, which alters precipitation and increases droughts. Although it is generally considered to be drought-tolerant, little is known about the differences between cultivars. This study aimed to investigate [...] Read more.
Cocksfoot (Dactylis glomerata L.) is becoming increasingly important for grassland farming due to climate change, which alters precipitation and increases droughts. Although it is generally considered to be drought-tolerant, little is known about the differences between cultivars. This study aimed to investigate the effects of four different field capacity (FC) levels (80%, 60%, 40%, and rewetting to 80% after a period of 40% FC) on the yield, crude protein content, water consumption, water use efficiency (WUE), and drought susceptibility index of five European cocksfoot cultivars (cv). A pot experiment was conducted in a greenhouse subjected to the specified irrigation treatments over three growth periods. The results revealed significant differences in the cultivars’ responses to the irrigation treatments. Dry matter yield decreased under simulated drought conditions, while crude protein content and WUE increased. Prolana cv achieved the highest yield under drought conditions, Tandem cv had the highest WUE, and Laban cv exhibited the highest crude protein content. Rewetting to 80% FC in the last growth period resulted in similar dry matter and crude protein yields for all cultivars compared to full irrigation. These findings highlight the importance of selecting and breeding drought-tolerant cocksfoot cultivars to maintain high yields and quality in perennial grassland farming under future climate conditions. Full article
(This article belongs to the Special Issue Responses and Tolerance to Abiotic Stress in Forage and Turf Grasses)
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<p>Experimental design with five cocksfoot cultivars and four irrigation levels: FC<sub>80</sub>, FC<sub>60</sub>, FC<sub>40</sub>, and FC<sub>40rw</sub> in each of six replicates.</p>
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<p>(<b>A</b>) Air temperature, (<b>B</b>) solar radiation, and (<b>C</b>) relative humidity during the experiment.</p>
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<p>Timeline diagram of the experimental course.</p>
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<p>Drought susceptibility index (DSI) of five cocksfoot cultivars over the entire experiment period.</p>
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27 pages, 3555 KiB  
Article
Ecological Groups of Coleoptera (Insecta) as Indicators of Habitat Transformation on Drained and Rewetted Peatlands: A Baseline Study from a Carbon Supersite, Kaliningrad, Russia
by Vitalii Alekseev, Maxim Napreenko and Tatiana Napreenko-Dorokhova
Insects 2024, 15(5), 356; https://doi.org/10.3390/insects15050356 - 15 May 2024
Viewed by 1335
Abstract
A total of 281 coleopteran species from 41 families were recorded from different sites of an abandoned cut-over peatland designated as the Carbon Measurement Supersite in Kaliningrad Oblast. This beetle assemblage is considered a baseline (pre-impact) faunal assemblage for further investigations during the [...] Read more.
A total of 281 coleopteran species from 41 families were recorded from different sites of an abandoned cut-over peatland designated as the Carbon Measurement Supersite in Kaliningrad Oblast. This beetle assemblage is considered a baseline (pre-impact) faunal assemblage for further investigations during the ‘before–after’ (BA) or ‘before–after control-impact’ (BACI) study on a peatland that is planned to be rewetted. The spontaneously revegetated peatland has a less specialised beetle assemblage than at an intact raised bog. Tyrphobiontic species are completely absent from the peatland, while some tyrphophiles (5.3% of the total beetle fauna) are still found as remnants of the former raised bog communities. The predominant coenotic coleopteran group is tyrphoneutral generalists from various non-bog habitats (72.9%). The species composition is associated with the vegetation structure of the disturbed peatland (fragmentary Sphagnum cover, lack of open habitats, and widespread birch coppice or tree stand), which does not correspond to that of a typical European raised bog. The sampled coleopteran assemblage is divided into several relative ecological groups, whose composition and peculiarities are discussed separately. Possible responses to the rewetting measurements in different coleopteran groups are predicted and briefly discussed. A complex assemblage of stenotopic peatland-specialised tyrphophiles (15 spp.) and the most abundant tyrphoneutral generalists (31 spp.) were assigned as indicators for the environmental monitoring of peatland development. Full article
(This article belongs to the Section Insect Ecology, Diversity and Conservation)
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<p>Location of the Vittgirrensky peatland (the ‘Rossyanka’ Carbon Measurement Supersite) and the Zehlau raised bog (mentioned in the text for comparison) in Kaliningrad Oblast.</p>
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<p>Vegetation cover in the Vittgirrensky Peatland (after [<a href="#B28-insects-15-00356" class="html-bibr">28</a>,<a href="#B29-insects-15-00356" class="html-bibr">29</a>]) and location of the sampling points for Coleoptera in 2023: 1—Dry shrublands, 2—Wet shrublands, 3—Dry birch stand, 4—Fen-like communities (<span class="html-italic">Juncus</span>), 5—Birch coppice, 6—Reed beds, 7—pitfall traps set in lines, 8—Bare-peat sites, 9—Dense closed-canopy stand, 10—Fen-like communities (<span class="html-italic">Eriophorum</span>/<span class="html-italic">Carex</span>), 11—Wet forest (birch and aspen), 12—Hydrophilic communities in ditches, 13—Dirt road, 14—Places of sweeping with net in water.</p>
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<p>The main typical habitats of the Vittgirrensky peatland: (<b>A</b>) Birch coppice with <span class="html-italic">Calluna</span> (site of the pitfall line 1), habitat for <span class="html-italic">Micrelus ericae</span>, <span class="html-italic">Curimopsis nigrita</span>, <span class="html-italic">Altica longicollis</span>; (<b>B</b>) Open bare peat (site of the pitfall line 3), habitat for <span class="html-italic">Cymindis vaporariorum</span>, <span class="html-italic">Parabolitobius formosus</span>, <span class="html-italic">Pselaphus heisei</span>, <span class="html-italic">Curimopsis nigrita</span>; (<b>C</b>) Dry birch stand (site of pitfall line 2), habitat for <span class="html-italic">Sciaphilus asperatus</span>, <span class="html-italic">Barypeithes pellucidus</span>, <span class="html-italic">Brachysomus echinatus</span>; (<b>D</b>) <span class="html-italic">Phragmites</span>-dominated birch coppice, habitat for <span class="html-italic">Malthodes pumilus</span>, <span class="html-italic">Scymnus suturalis</span>; (<b>E</b>) The drainage ditch drying up in summer, sampling place of <span class="html-italic">Dytiscus dimidiatus</span>, <span class="html-italic">Graphoderus cinereus</span>, <span class="html-italic">Hydaticus seminiger</span>; (<b>F</b>) non-drying drainage ditch, sampling place of <span class="html-italic">Hydrochus elongatus</span>, <span class="html-italic">Enochrus ochropterus</span>.</p>
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<p>Some of the potential indicator species (tyrphophiles and abundant tyrphoneutrals) that can be used, among others, to monitor environmental impacts in the Vittgirrensky peatland: (<b>A</b>) <span class="html-italic">Altica aenescens</span>; (<b>B</b>) <span class="html-italic">Orchestes jota</span>; (<b>C</b>) <span class="html-italic">Platydracus fulvipes</span>; (<b>D</b>) <span class="html-italic">Scymnus suturalis</span>; (<b>E</b>) <span class="html-italic">Lochmaea caprea</span>; and (<b>F</b>) <span class="html-italic">Polydrusus cervinus</span>.</p>
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17 pages, 10567 KiB  
Article
Optimizing Structural Patterns for 3D Electrodes in Lithium-Ion Batteries for Enhanced Fast-Charging Capability and Reduced Lithium Plating
by Yannic Sterzl and Wilhelm Pfleging
Batteries 2024, 10(5), 160; https://doi.org/10.3390/batteries10050160 - 11 May 2024
Cited by 2 | Viewed by 2854
Abstract
The most common pattern types for anode structuring, in particular the line, grid, and hexagonal-arranged hole pattern were evaluated in a comparable setup in full-cells and symmetrical cells. The cells with structured electrodes were compared to reference cells with unstructured anodes of similar [...] Read more.
The most common pattern types for anode structuring, in particular the line, grid, and hexagonal-arranged hole pattern were evaluated in a comparable setup in full-cells and symmetrical cells. The cells with structured electrodes were compared to reference cells with unstructured anodes of similar areal capacity (4.3 mAh cm−2) and the onset of lithium plating during fast-charging was determined in situ by differential voltage analysis of the voltage relaxation and ex situ by post-mortem analysis. Furthermore, electrochemical impedance spectroscopy measurements on symmetrical cells were used to determine the ionic resistance of structured and unstructured electrodes of similar areal capacity. All cells with structured electrodes showed lower ionic resistances and an onset of lithium plating shifted to higher C-rates compared to cells with unstructured electrodes. The structure patterns with capillary structures, i.e., lines and grids, showed significant reduced lithium plating during fast-charging and a higher rate capability compared to reference cells with unstructured electrodes and cells with hole structured electrodes. The continuous rewetting of the electrode with liquid electrolyte by capillary forces and the reduced ionic resistance of the 3D electrode are identified as key factors in improving overall battery performance. The data of the studied cells were used to calculate the resulting energy and power densities of prospective commercial pouch cells and potential pitfalls in the comparison to cells with unstructured electrodes were identified. Full article
(This article belongs to the Section Battery Processing, Manufacturing and Recycling)
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Figure 1
<p>Top-view SEM images of the laser structured anodes with grid (<b>a</b>,<b>d</b>) line (<b>b</b>,<b>e</b>) and hole (<b>c</b>,<b>f</b>) pattern with indicated structure dimension (<math display="inline"><semantics> <mrow> <mi>w</mi> </mrow> </semantics></math>) and pitch (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>p</mi> </mrow> <mrow> <mi>S</mi> </mrow> </msub> </mrow> </semantics></math>). (SEM: 10 kV accelerating voltage).</p>
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<p>(<b>a</b>) Nyquist plot of EIS data (squares) and related fits (solid lines) on symmetrical cells with structured and unstructured electrodes; (<b>b</b>) the measured ionic resistance; and (<b>c</b>) the used equivalent circuit with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> as resistor and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>W</mi> </mrow> <mrow> <mi>G</mi> <mi>E</mi> </mrow> </msub> </mrow> </semantics></math> as generalized finite Warburg element.</p>
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<p>Specific charge capacity after the CC (<b>a</b>) and CCCV (<b>b</b>) phase (without the fifth cycle at each C-rate). The shading indicates the extent of the standard deviation.</p>
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<p>SOC after CCCV charging (<b>a</b>) and the required charging time to a SOC of 80% (<b>b</b>) as a function of C-rate (fifth cycle of the respective C-rate shown; the star symbols indicate the number of cells—out of 3 cells—that have fulfilled the criterion).</p>
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<p>Differential voltage analysis of the voltage relaxation (solid line) and voltage as a function of time (dashed line) in the first 30 min of the 4 h rest period after charging in the fifth cycle at each C-rate for the cells with unstructured (<b>a</b>), hole structured (<b>b</b>), line structured (<b>c</b>), and grid structured (<b>d</b>) electrodes.</p>
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<p>Post-mortem digital microscope images (<b>a</b>–<b>d</b>) and SEM images (<b>e</b>–<b>f</b>) of the unstructured (<b>a</b>,<b>e</b>), hole structured (<b>b</b>,<b>f</b>), line structured (<b>c</b>,<b>g</b>), and grid structured (<b>d</b>,<b>h</b>) electrodes (same set of cells as in <a href="#batteries-10-00160-f005" class="html-fig">Figure 5</a>). White dashed outline marks the anode area which was covered by the cathode and the red dashed outline marks the cut-out of the respective SEM image.</p>
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<p>Calculation of the energy density (orange) and specific energy (green) as a function of the anode areal capacity in an 80 Ah pouch cell (cell geometry data extracted from [<a href="#B53-batteries-10-00160" class="html-bibr">53</a>]; electrode parameter from this work with a N/P ratio of 1.16) with unstructured (solid symbols) and structured (hollow symbols; 11% mass loss) anodes. The dashed line serves as an aid to orientation for comparing the same energy density or specific energy between cells with structured and unstructured electrodes.</p>
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<p>Calculated power and energy data for an 80 Ah pouch cell: (<b>a</b>) volumetric energy and power density; (<b>b</b>) specific energy and power. The geometry data were extracted from [<a href="#B53-batteries-10-00160" class="html-bibr">53</a>] and electrode parameters were taken from this work.</p>
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