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13 pages, 1966 KiB  
Article
Long-Term Effects of Biochar Application on Soil Heterotrophic Respiration in a Warm–Temperate Oak Forest
by Shinpei Yoshitake, Kakuya Enichi, Yuki Tsukimori, Toshiyuki Ohtsuka, Hiroshi Koizumi and Mitsutoshi Tomotsune
Forests 2025, 16(3), 489; https://doi.org/10.3390/f16030489 (registering DOI) - 11 Mar 2025
Viewed by 29
Abstract
Biochar application as a soil amendment is gaining attention as a stable, long-term carbon sequestration strategy for the mitigation of climate change. However, biochar applied to the soil may increase soil carbon efflux. This study aimed to determine the long-term (8 years) effects [...] Read more.
Biochar application as a soil amendment is gaining attention as a stable, long-term carbon sequestration strategy for the mitigation of climate change. However, biochar applied to the soil may increase soil carbon efflux. This study aimed to determine the long-term (8 years) effects of biochar application to the forest floor on soil carbon effluxes (soil respiration [SR] and heterotrophic respiration [HR]) in a warm–temperate oak forest. Biochar was applied at the rate of 0, 5, or 10 Mg ha−1 to 20 m × 20 m plots (n = 4). The SR and HR rates were determined using the closed chamber method and the trenching method. The annual SR tended to increase over 8 years following biochar application, whereas a significant increase in the annual HR (+31%–37%) was observed in the short term (<3 years). The increased HR likely included CO2 emissions from the decomposition of the labile fraction of biochar carbon and from the microbial decomposition of the original soil organic matter stimulated through changes in the soil physicochemical environment, such as soil moisture and pH. The results suggest that a short-term increase in HR should be considered in the evaluation of carbon sequestration in response to biochar addition to forest ecosystems. Full article
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Figure 1

Figure 1
<p>Temporal changes in (<b>a</b>) soil respiration rate (<span class="html-italic">R</span><sub>S</sub>) and (<b>b</b>) heterotrophic respiration rate (<span class="html-italic">R</span><sub>H</sub>) in the plots with or without biochar. C0, C5, and C10 represent the experimental plots with 0, 5, and 10 Mg ha<sup>−1</sup> biochar application, respectively. Values are means (<span class="html-italic">n</span> = 3–4).</p>
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<p>Temporal changes in (<b>a</b>) soil temperature and (<b>b</b>) soil moisture (volumetric soil water content) recorded during the respiration measurements in plots with or without biochar. C0, C5, and C10 represent experimental plots with 0, 5, and 10 Mg ha<sup>−1</sup> biochar application, respectively. Values are means (<span class="html-italic">n</span> = 3–4).</p>
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<p>Soil pH in plots with or without biochar. C0, C5, and C10 represent experimental plots with 0, 5, and 10 Mg ha<sup>−1</sup> biochar application, respectively. Bars and error bars indicate the mean ± SD (<span class="html-italic">n</span> = 4). Bars labeled with different lowercase letters differ significantly (Tukey’s test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Soil microbial biomass carbon determined with the adenosine triphosphate method in plots with or without biochar. C0, C5, and C10 represent experimental plots with 0, 5, and 10 Mg ha<sup>−1</sup> biochar application, respectively. Bars and error bars represent the mean ± SD (<span class="html-italic">n</span> = 4). Bars labeled with different lowercase letters differ significantly (Tukey’s test, <span class="html-italic">p</span> &lt; 0.05). n.d., not determined.</p>
Full article ">Figure 5
<p>(<b>a</b>) Annual soil respiration (SR) and (<b>b</b>) annual heterotrophic respiration (HR) in the plots with or without biochar from the second to the eighth year. C0, C5, and C10 represent the experimental plots with 0, 5, and 10 Mg ha<sup>−1</sup> biochar application, respectively. Bars and error bars represent the mean ± SD (<span class="html-italic">n</span> = 4). Bars labeled with different lowercase letters differ significantly (Tukey’s test, <span class="html-italic">p</span> &lt; 0.05). n.d., not determined.</p>
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19 pages, 2770 KiB  
Article
Carbon Absorption Potential of Abandoned Rice Paddy Fields in Korea
by Chang-Seok Lee, Bong-Soon Lim and Ji-Eun Seok
Sustainability 2025, 17(5), 2054; https://doi.org/10.3390/su17052054 - 27 Feb 2025
Viewed by 289
Abstract
Over time, the vegetation of abandoned rice paddy fields is succeeded by communities of willow (Salix pierotii Miq.). This study was carried out to confirm the potential for future carbon farming by evaluating the carbon absorption capacity of willow communities restored passively [...] Read more.
Over time, the vegetation of abandoned rice paddy fields is succeeded by communities of willow (Salix pierotii Miq.). This study was carried out to confirm the potential for future carbon farming by evaluating the carbon absorption capacity of willow communities restored passively in abandoned rice paddy fields. The net primary productivity (NPP) of willow communities established in abandoned rice paddy fields in three areas of central Korea (Cheongju, Andong, and Buyeo) was determined. The NPP was obtained by combining the diameter growth of willow individuals and the density of willow stands, yielding 24.36, 19.74, and 38.69 tons·ha−1·yr−1, respectively, and the average NPP of the three sites was 27.62 tons·ha−1·yr−1. The carbon-based NPP calculated from the average NPP at the three sites was 13.81 tons·C·ha−1·yr−1, and the amount of heterotrophic respiration, which is the respiration of microorganisms and animals in the soil, measured in abandoned rice paddy fields in Cheongju was 5.25 tons·C·ha−1·yr−1. As a result, the net ecosystem production (NEP) of the willow communities established in the abandoned rice paddy fields was calculated as 8.56 tons·C·ha−1·yr−1. By substituting this NEP value into the area of abandoned rice paddy fields so far, the carbon dioxide absorption capacity of abandoned rice paddy fields was estimated to exceed 19 million·tons·yr−1. This amount is high enough to account for 77% of the total forecasted carbon absorption capacity in 2050, which is the year Korea aims to achieve carbon neutrality. In this regard, carbon farming using abandoned rice paddy fields is evaluated as a promising project. Full article
(This article belongs to the Special Issue Sustainable Development of Agricultural Systems)
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<p>A map showing the study areas.</p>
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<p>DCA ordination of the stands based on vegetation data collected from abandoned rice paddy fields with different abandonment histories across the central part of the Republic of Korea. Pa: <span class="html-italic">Phragmites australis</span>, Pt: <span class="html-italic">Persicaria thunbergii</span>, To: <span class="html-italic">Typha orientalis</span>, Sp: <span class="html-italic">Salix pierotii</span>, Sp (present): <span class="html-italic">S. pierotii</span> stands selected for this study.</p>
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<p>An allometric equation derived from the correlation between the diameter at breast height (DBH) and the dry weight of the willows excavated at the Cheongju study site [<a href="#B38-sustainability-17-02054" class="html-bibr">38</a>].</p>
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<p>Cumulative diameter growth of <span class="html-italic">S. pierotii</span> at three study sites.</p>
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<p>Changes in annual diameter growth of <span class="html-italic">S. pierotii</span> trees at three study sites.</p>
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<p>Changes in the NPP of <span class="html-italic">S. pierotii</span> trees at three study sites.</p>
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<p>Seasonal variation in monthly mean soil respiration in <span class="html-italic">S. pierotii</span> communities established in the abandoned rice paddy fields of Cheongju. Bars indicate standard errors of mean soil respiration.</p>
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<p>Scatter plots of observed soil respiration vs. temperatures (°C) measured in air (1.5 m height) and soil (0.5 cm depth) in the <span class="html-italic">S. pierotii</span> communities established in the abandoned rice paddy fields of Cheongju.</p>
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<p>A change in the area of rice paddy fields in the Republic of Korea.</p>
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<p>A yearly change in the area of abandoned rice paddy fields in the Republic of Korea.</p>
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18 pages, 6803 KiB  
Article
Vegetation and Precipitation Patterns Define Annual Dynamics of CO2 Efflux from Soil and Its Components
by Dmitriy Khoroshaev, Irina Kurganova, Valentin Lopes de Gerenyu, Dmitry Sapronov, Sergey Kivalov, Abeer S. Aloufi and Yakov Kuzyakov
Land 2024, 13(12), 2152; https://doi.org/10.3390/land13122152 - 11 Dec 2024
Cited by 1 | Viewed by 671
Abstract
Respiration of soil heterotrophs—mainly of bacteria and fungi—is a substantial part of carbon balance in terrestrial ecosystems, which tie up organic matter decomposition with the rise of atmospheric CO2 concentration. Deep understanding and prediction of seasonal and interannual variation of heterotrophic and [...] Read more.
Respiration of soil heterotrophs—mainly of bacteria and fungi—is a substantial part of carbon balance in terrestrial ecosystems, which tie up organic matter decomposition with the rise of atmospheric CO2 concentration. Deep understanding and prediction of seasonal and interannual variation of heterotrophic and autotrophic components of CO2 efflux from soil is limited by the lack of long-term, full-year measurements. To better understand the impact of current climate changes on CO2 emissions from soils in the mixed forest and mowed grassland, we measured CO2 efflux every week for 2 years. Heterotrophic (SOM-derived + leaf litter) and root-associated (root with rhizosphere microorganisms) components were partitioned by the root exclusion method. The total CO2 efflux from soil was averaged 500 g C m−2 yr−1 in the forest and 650 g C m−2 yr−1 in the grassland, with shares of the no-growing cold season (Nov–Mar) of 22% and 14%, respectively. The heterotrophic component of CO2 efflux from the soil averaged 62% in the forest and 28% in the grassland, and it was generally stable across seasons. The redistribution of the annual precipitation amounts as well as their deficit (droughts) reduced soil respiration by 33–81% and heterotrophic respiration by 24–57% during dry periods. This effect was more pronounced in the grassland (with an average decline of 56% compared to 39% in the forest), which is related to lower soil moisture content in the grassland topsoil during dry periods. Full article
(This article belongs to the Section Land–Climate Interactions)
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Figure 1
<p>General view of the vegetation and soils within the native mixed forest (<b>a</b>) and mowed grassland (<b>b</b>) ecosystems, along with chambers for soil respiration measurements during warm (<b>b</b>) and cold (<b>c</b>) periods. The process of installing soil chambers containing root-free soils (<b>d</b>).</p>
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<p>Dynamics of air (Ta) and soil (Ts) temperatures and height of snow cover (<b>a</b>), soil and forest litter moisture content (<b>b</b>) during the measurements of soil respiration (SR) and respiration of SOM−derived microorganisms (HR) in the forest (<b>c</b>) and the grassland (<b>d</b>). The dotted lines for HR show values reconstructed using the regression method for the first half of June 2022. Arrows indicate the decrease in SR and HR values during prolonged dry periods: August 2022, June 2023, and September 2023. Error bars are standard errors of the mean.</p>
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<p>Dynamics of air (Ta) temperatures, day sum precipitation (P), soil moisture content (Ww) during the measurements of soil respiration (SR) and respiration of SOM-derived microorganisms (HR) in the grassland (<b>a</b>,<b>c</b>) and the forest (<b>b</b>,<b>d</b>) during summer–autumn periods of 2022 (<b>a</b>,<b>b</b>) and 2023 (<b>c</b>,<b>d</b>).</p>
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<p>Soil respiration (SR) and soil moisture content (Ww) relationships during summer–autumn periods of 2022 (<b>a</b>,<b>b</b>) and 2023 (<b>c</b>,<b>d</b>) in the forest and the grassland. Data for the whole June–September period (<b>a</b>,<b>c</b>) and for periods with a high amount of dry days: 26 June–8 August 2022 (<b>b</b>) as well as June and 29 August–4 October 2023 (<b>d</b>).</p>
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<p>Dynamics of total monthly soil (SR), SOM-derived heterotrophs (HR), and root-derived (RR) respiration values (<b>a</b>–<b>c</b>) and their distribution over two years (<b>d</b>–<b>f</b>): the median (bar), lower (Q1) and upper (Q3) quartiles (“boxes”); X1 = Q1 − 1.5 IQR (interquartile range, IQR = Q3 − Q1) and X2 = Q3 − 1.5 IQR (“moustaches”); all data are shown as dots.</p>
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<p>Differences in the total monthly soil (SR), SOM-derived heterotrophs (HR), and root-derived (RR) respiration between the grassland and forest ecosystems (<b>a</b>); a positive value means more intensive fluxes in the grassland. Dynamics of the share of monthly HR in SR values in the ecosystems (<b>b</b>). The relationships between increments of HR or RR and SR (<b>c</b>) values presented in (<b>a</b>), as well as the increments of HR shares between the forest and the grassland (<b>d</b>).</p>
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<p>Offset of SOM-derived respiration of heterotrophs (HR) measured in young (installed in June–July 2023) soil chambers relative to mature (installed in May 2022) soil chambers, in terms of absolute (<b>a</b>) and relative (<b>b</b>) values. A cross mark indicates days with no significant differences (<span class="html-italic">t</span>-test with equal variances, n = 4–5, <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure A2
<p>Offset of soil moisture content (SMC) at the depth of 0–6 cm within soil chambers without roots relative to surrounding intact soil, in terms of absolute (<b>a</b>) and relative (<b>b</b>) values. A cross mark indicates days with no significant differences (<span class="html-italic">t</span>-test with equal variances, n = 5–10, <span class="html-italic">p</span> &lt;0.05).</p>
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<p>Respiration of soil (SR) and its SOM-derived (HR) and root-derived components (RR) during 2022–2023 (<b>a</b>) and during 2023–2024 (<b>b</b>): the mean (cross), the median (bar), lower (Q1), and upper (Q3) quartiles (“boxes”); X1 = Q1 − 1.5 IQR (interquartile range, IQR = Q3 − Q1) and X2 = Q3 − 1.5 IQR (“moustaches”); all data are shown as dots. Different letters indicate pairs of average values, the differences of which are detected during the multiple comparison procedure (Tukey test, α = 5%) after two-way ANOVA (Flux component × Ecosystem).</p>
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<p>The regression functions of SOM-derived microorganisms respiration rate (HR) based on soil respiration rate (SR) was developed using data from 20 July 2022 to 29 September 2022, which was then used to reconstruct HR for the period from 1 July 2022 to 14 July 2022.</p>
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19 pages, 6983 KiB  
Article
Impact of Conversion of the Caatinga Forest to Different Land Uses on Soil and Root Respiration Dynamics in the Brazilian Semiarid Region
by Denizard Oresca, Eduardo Soares de Souza, Rodolfo Marcondes Silva Souza, José Raliuson Inácio Silva, Débora Purcina de Moura, Everardo Valadares de Sá Barreto Sampaio, Claude Hammecker, José Romualdo de Sousa Lima, Rômulo Simões Cezar Menezes, Luiz Guilherme Medeiros Pessoa, Natache Gonçalves de Moura Ferrão and Antônio Celso Dantas Antonino
Sustainability 2024, 16(23), 10652; https://doi.org/10.3390/su162310652 - 5 Dec 2024
Viewed by 984
Abstract
The Caatinga biome has been severely devastated over the years due to the replacement of native dry forests with grassland areas in the Brazilian semiarid region. Despite this, variations in key soil quality indicators still need to be fully elucidated. We evaluated soil [...] Read more.
The Caatinga biome has been severely devastated over the years due to the replacement of native dry forests with grassland areas in the Brazilian semiarid region. Despite this, variations in key soil quality indicators still need to be fully elucidated. We evaluated soil and root respiration dynamics in grassland (GR), agroforestry (AS), and Caatinga forest (CA) areas, during dry and rainy seasons. In situ, monthly CO2 flux (total, root, and heterotrophic respirations), soil moisture (θv), and temperature (Tsoil) were measured. Soil samples were collected every 5 cm layer up to 20 cm depth to analyze total organic carbon (TOC) and microbial activities. The highest parameter values occurred during the rainy season. Total soil respiration was highest in AS, followed by CA and then GR, with 19.3, 13.4, and 8.4 ton C ha1 yr1, respectively, and root respiration contributed 33.2 and 32.9% to total soil respiration in CA and AS, respectively. However, TOC concentrations and microbial activity were significantly higher in AS than in GR and similar to CA, more than compensating the C losses by respiration. Therefore, agroforestry systems have a high potential for semiarid lands because they preserve soil carbon and microbial activity comparable to Caatinga forests. Full article
(This article belongs to the Section Soil Conservation and Sustainability)
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Figure 1
<p>Overview of Brazil within South America, semiarid region, São Francisco River, Pajeu River, and experimental sites in Serra Talhada and Triunfo municipalities, Pernambuco state.</p>
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<p>Installation scheme of PVC collars to monitor <math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>h</mi> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics></math> (<b>a</b>), soil <math display="inline"><semantics> <msub> <mi>CO</mi> <mn>2</mn> </msub> </semantics></math> flux system (LI-6400-09, LI-COR, Lincoln, NE, USA) (<b>b</b>), and collar at 30 cm depth with installation details. Components include (<b>c</b>), 1. collar with lateral windows lined with 0.5 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m nylon mesh; 2. PVC slip coupling with rubber ring joint gaskets for sealing; 3. polyamide nylon billet; 4. single bevel-cutting ring with a flat outer edge for sharp ground cutting; and 5. hammer.</p>
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<p>Dynamics of soil total (<math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> </semantics></math>) (<b>D</b>) and heterotrophic (<math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>h</mi> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics></math>) (<b>E</b>) respirations, moisture (<span class="html-italic">θ</span>v) (<b>B</b>), and soil temperature (<math display="inline"><semantics> <msub> <mi mathvariant="normal">T</mi> <mrow> <mi>s</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msub> </semantics></math>) (<b>C</b>) in grassland (GR), agroforests (AS), and native Caatinga vegetation (CA) systems in the semiarid region of Brazil. Subfigure (<b>A</b>) shows the average monthly rainfall in the region.</p>
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<p>Seasonality of the root respiration rate (<math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>r</mi> <mi>o</mi> <mi>o</mi> <mi>t</mi> </mrow> </msub> </semantics></math>) and its contribution to total soil respiration (RC) in grassland, agroforestry, and native Caatinga vegetation systems in the semiarid region of Brazil. Bars ± standard deviation represent <math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>r</mi> <mi>o</mi> <mi>o</mi> <mi>t</mi> </mrow> </msub> </semantics></math>, while lines with points represent RC.</p>
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<p>Quadratic relationships between soil respiration (<math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>h</mi> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>r</mi> <mi>o</mi> <mi>o</mi> <mi>t</mi> </mrow> </msub> </semantics></math>) and soil moisture (<math display="inline"><semantics> <mi>θ</mi> </semantics></math>v) from March 2019 to May 2021 in grassland (GR), agroforestry (AS), and native Caatinga vegetation (CA) systems in the semiarid region of Brazil. Subfigures (<b>A</b>,<b>D</b>,<b>G</b>) illustrate the relationship between (<math display="inline"><semantics> <mi>θ</mi> </semantics></math>v) and (<math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> </semantics></math>, while subfigures (<b>B</b>,<b>E</b>,<b>H</b>), as well as (<b>C</b>,<b>F</b>,<b>I</b>), depict the relationships between (<math display="inline"><semantics> <mi>θ</mi> </semantics></math>v) and <math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>h</mi> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>r</mi> <mi>o</mi> <mi>o</mi> <mi>t</mi> </mrow> </msub> </semantics></math>), respectively.</p>
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<p>Relationship between total, heterotrophic, and root soil respirations (<math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>h</mi> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>r</mi> <mi>o</mi> <mi>o</mi> <mi>t</mi> </mrow> </msub> </semantics></math>) and soil surface temperature (<math display="inline"><semantics> <msub> <mi mathvariant="normal">T</mi> <mrow> <mi>s</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msub> </semantics></math>) from March 2019 to May 2021 in grassland (GR), agroforestry (AS), and native Caatinga vegetation (CA) systems in the semiarid region of Brazil. Subfigures (<b>A</b>,<b>D</b>,<b>G</b>) illustrate the relationship between (<math display="inline"><semantics> <msub> <mi mathvariant="normal">T</mi> <mrow> <mi>s</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msub> </semantics></math>) and (<math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> </semantics></math>, while subfigures (<b>B</b>,<b>E</b>,<b>H</b>), as well as (<b>C</b>,<b>F</b>,<b>I</b>), depict the relationships between (<math display="inline"><semantics> <msub> <mi mathvariant="normal">T</mi> <mrow> <mi>s</mi> <mi>o</mi> <mi>i</mi> <mi>l</mi> </mrow> </msub> </semantics></math>) and <math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>h</mi> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>r</mi> <mi>o</mi> <mi>o</mi> <mi>t</mi> </mrow> </msub> </semantics></math>), respectively.</p>
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<p>Diurnal variation in soil surface temperature and soil respiration (<math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>h</mi> <mi>e</mi> <mi>t</mi> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mi mathvariant="normal">R</mi> <mrow> <mi>r</mi> <mi>o</mi> <mi>o</mi> <mi>t</mi> </mrow> </msub> </semantics></math>) from three different land uses (grassland, GR; agroforestry system, AS; and native Caatinga, CA) in the semiarid region of Brazil, during the dry (sugfigures <b>A</b>–<b>C</b>) and rainy seasons (subfigures <b>D</b>–<b>F</b>).</p>
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20 pages, 21022 KiB  
Article
Decoupling the Impacts of Climate Change and Human Activities on Terrestrial Vegetation Carbon Sink
by Shuheng Dong, Wanxia Ren, Xiaobin Dong, Fan Lei, Xue-Chao Wang, Linglin Xie and Xiafei Zhou
Remote Sens. 2024, 16(23), 4417; https://doi.org/10.3390/rs16234417 - 26 Nov 2024
Cited by 1 | Viewed by 801
Abstract
Net ecosystem productivity (NEP) plays a vital role in quantifying the carbon exchange between the atmosphere and terrestrial ecosystems. Understanding the effects of dominant driving forces and their respective contribution rates on NEP can aid in the effective management of terrestrial carbon sinks, [...] Read more.
Net ecosystem productivity (NEP) plays a vital role in quantifying the carbon exchange between the atmosphere and terrestrial ecosystems. Understanding the effects of dominant driving forces and their respective contribution rates on NEP can aid in the effective management of terrestrial carbon sinks, especially in rapidly urbanizing coastal areas where climate change (CC) and human activities (HA) occur frequently. Combining MODIS NPP products and meteorological data from 2000 to 2020, this paper established a Modis NPP-Soil heterotrophic respiration (Rh) model to estimate the magnitude of NEP in China’s coastal zone (CCZ). Hotspot analysis, variation trend, partial correlation, and residual analysis were applied to explore the spatiotemporal patterns of NEP and the contributions of CC and HA to the dynamics of NEP. We also explored the changes in NEP in different land use types. It was found that there is a clear north–south difference in the spatial pattern of NEP in CCZ, with Zhejiang Province serving as the main watershed for this difference. In addition, NEP in most regions showed an improvement trend, especially in the Beijing–Tianjin–Hebei region and Shandong Province, but the pixel values of NEP here were generally not as high as that in most southern provinces. According to the types of driving forces, the improvement of NEP in these regions primarily results from the synergistic effects of CC and HA. NEP changes in provinces south of Zhejiang are mainly dominated by single-factor-driven degradation. The area where HA contributes to the increase in NEP is much larger than that of CC. From the perspective of land use types, forests and farmland are the dominant contributors to the magnitude of NEP in CCZ. Full article
(This article belongs to the Special Issue Mapping Essential Elements of Agricultural Land Using Remote Sensing)
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<p>Location, topography, and main land use types of CCZ.</p>
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<p>Spatiotemporal dynamics of NEP in CCZ.</p>
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<p>Average NEP changes in the main provincial-level administrative regions from 2000 to 2020.</p>
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<p>Hotspot cluster map of average NEP in CCZ.</p>
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<p>Spatial distribution of NEP trend types and area proportion in different provinces.</p>
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<p>Partial correlation types of NEP and meteorological factors: (<b>a</b>) NEP-Temperature; (<b>b</b>) NET-Precipitation.</p>
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<p>Partial correlation coefficients of NEP and meteorological factors: (<b>a</b>) NEP-Temperature; (<b>b</b>) NET-Precipitation.</p>
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<p>Spatial distribution of NEP driver types and their area proportion.</p>
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<p>Contribution rate distribution of CC and HA: (<b>a</b>) Climate change; (<b>b</b>) Human activities.</p>
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<p>Relative contribution ratio of CC and HA.</p>
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<p>Area proportion of NEP trend types.</p>
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<p>Area proportion of NEP diver types.</p>
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16 pages, 6839 KiB  
Article
Global Spatial Projections of Forest Soil Respiration and Associated Uncertainties
by Lingxia Feng, Junjie Jiang, Junguo Hu, Chao Zhu, Zhiwei Wu, Guangliang Li and Taolve Chen
Forests 2024, 15(11), 1982; https://doi.org/10.3390/f15111982 - 10 Nov 2024
Cited by 2 | Viewed by 1125
Abstract
The accurate prediction of global forest soil respiration (Rs) is critical for climate change research. Rs consists of autotrophic (Ra) and heterotrophic (Rh) respiration, which respond differently to environmental factors. Predicting Rs as a single flux can [...] Read more.
The accurate prediction of global forest soil respiration (Rs) is critical for climate change research. Rs consists of autotrophic (Ra) and heterotrophic (Rh) respiration, which respond differently to environmental factors. Predicting Rs as a single flux can be biased; therefore, Ra and Rh should be predicted separately to improve prediction accuracy. In this study, we used the SRDB_V5 database and the random forest model to analyze the uncertainty in predicting Rs using a single global model (SGM) and Ra/Rh using a specific categorical model (SCM) and predicted the spatial dynamics of the distribution pattern of forest Ra, Rh, and Rs in the future under the two different climate patterns. The results show that Rs is higher under tropical and inland climatic conditions, while Rh fluctuates less than Ra and Rs. In addition, the SCM predictions better capture key environmental factors and are more consistent with actual data. In the SSP585 (high emissions) scenario, Rs is projected to increase by 19.59 percent, while in the SSP126 (low emissions) scenario, Rs increases by only 3.76 percent over 80 years, which underlines the need for SCM in future projections. Full article
(This article belongs to the Section Forest Soil)
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<p>(<b>a</b>) Spatial distribution of <b><span class="html-italic">Ra</span></b> and <b><span class="html-italic">Rh</span></b> contributions to forest soil respiration in different climatic zones; stations from the Global Soil Respiration Database (SRDB). (<b>b</b>–<b>d</b>) Statistical map of global forest <b><span class="html-italic">Ra</span></b>, <b><span class="html-italic">Rh</span></b>, and <b><span class="html-italic">Rs</span></b> observations.</p>
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<p>(<b>a</b>–<b>c</b>) Ordering of the importance of factors affecting soil respiration and the interactions. (<b>a</b>) Result of soil autotrophic respiration (<b><span class="html-italic">Ra</span></b>), (<b>e</b>) result of soil heterotrophic respiration (<b><span class="html-italic">Rh</span></b>), and (<b>i</b>) result of total soil respiration (<b><span class="html-italic">Rs</span></b>). The dots represent the importance of the factors, the lines represent the interaction between the factors, “Vint” represents the interaction of the variables, and “Vimp” represents the importance of the variables. (<b>d</b>–<b>l</b>) A partial dependence plot of the top three important variables affecting <b><span class="html-italic">Ra</span></b>, <b><span class="html-italic">Rh</span></b>, and <b><span class="html-italic">Rs</span></b>. The yellow and green regions indicate the density of the data distribution, with yellow representing low density and green representing high density. The green dashed line indicates the generalized linear regression fit, where (<b>d</b>–<b>f</b>) are partial dependence plots for <b><span class="html-italic">Ra</span></b>, (<b>g</b>–<b>i</b>) are partial dependence plots for <b><span class="html-italic">Rh</span></b>, and (<b>j</b>–<b>l</b>) are partial dependence plots for <b><span class="html-italic">Rs</span></b>.</p>
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<p>Global forest <b><span class="html-italic">Ra</span></b>, <b><span class="html-italic">Rh</span></b>, <b><span class="html-italic">Rs</span></b>, and <b><span class="html-italic">Rah</span></b> distributions for 2019 predicted by random forests with a spatial resolution of 0.5°. (<b>a</b>) Prediction results of forest <b><span class="html-italic">Ra</span></b>, (<b>b</b>) prediction results of forest <b><span class="html-italic">Rh</span></b>, (<b>c</b>) prediction results of forest <b><span class="html-italic">Rs</span></b>, and (<b>d</b>) prediction results of forest <b><span class="html-italic">Rah</span></b>.</p>
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<p>Density distribution and statistical analysis of <b><span class="html-italic">Ra</span></b>, <b><span class="html-italic">Rh</span></b>, and <b><span class="html-italic">Rs</span></b> components. The insets show the <b><span class="html-italic">Ra</span></b>, <b><span class="html-italic">Rh</span></b>, and <b><span class="html-italic">Rs</span></b> standard errors and the variability of the data. Significant differences between components are indicated by asterisks: “*” indicates <span class="html-italic">p</span> &lt; 0.05, “**” indicates <span class="html-italic">p</span> &lt; 0.01, and “***” indicates <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>(<b>a</b>,<b>b</b>) Global distribution of global soil respiration coefficient of variation (CV) predicted by SCM and SGM, respectively. (<b>c</b>) Difference ratio between <b><span class="html-italic">Rah</span></b> and <b><span class="html-italic">Rs</span></b> calculated from Equation (2), with a spatial resolution of 0.5°.</p>
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<p>Emissions from forests, (<b>a</b>) <b><span class="html-italic">Ra</span></b>, (<b>b</b>) <b><span class="html-italic">Rh</span></b>, and (<b>c</b>) <b><span class="html-italic">Rs</span></b>, in five different climatic zones, A (tropical), B (arid), C (temperate), D (continental) and E (polar) in the SSP126 and SSP585 climate models (<a href="#app1-forests-15-01982" class="html-app">Figures S6–S8</a>).</p>
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<p>Changes in global forest <b><span class="html-italic">Rs</span></b>, <b><span class="html-italic">Rh</span></b>, and <b><span class="html-italic">Ra</span></b> emissions over the next 80 years in the SSP126 (<b>a</b>) and SSP585 (<b>b</b>) climate models (<a href="#app1-forests-15-01982" class="html-app">Figure S9</a>).</p>
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19 pages, 2364 KiB  
Article
No-Tillage Treatment with Total Green Manure Mulching Reduces Soil Respiration by Regulating Soil Water Content Affecting Heterotrophic Respiration
by Yongpan Shang, Aizhong Yu, Yulong Wang, Pengfei Wang, Hanqiang Lyu, Feng Wang, Xuehui Yang, Yalong Liu, Bo Yin and Dongling Zhang
Agronomy 2024, 14(11), 2551; https://doi.org/10.3390/agronomy14112551 - 30 Oct 2024
Viewed by 675
Abstract
Green manure is widely applied in agricultural production due to its beneficial soil modification and fertilization effects. However, the mechanisms underlying the effects of green manure return methods on soil respiration (Rs) and its components remain unclear. This study aimed to investigate the [...] Read more.
Green manure is widely applied in agricultural production due to its beneficial soil modification and fertilization effects. However, the mechanisms underlying the effects of green manure return methods on soil respiration (Rs) and its components remain unclear. This study aimed to investigate the effects of green manure return methods on Rs in maize fields by quantifying Rs levels. A field experiment was conducted from 2021 to 2023 in the inland river oasis irrigation area of Gansu, with five treatment conditions: tillage with a full quantity of green manure incorporated into the soil (TG), no tillage with a full quantity of green manure mulched on the soil surface (NTG), tillage with roots incorporated into the soil and above-ground green manure removed (T), no tillage with above-ground manure removed (NT), and conventional tillage and leisure (CT). The results showed that, compared with CT, the NTG treatment increased the maize grain yield while reducing the soil heterotrophic respiration rate (Rh) by 8.5–9.8% and Rs by 6.7–8.7%, but did not significantly affect the soil autotrophic respiration rate (Ra), and decreased the carbon emission efficiency (CEE) by 20.8–25.6%. The increase in the soil water content (SWC) significantly reduced Rh during all growth periods, which was the primary factor in the reduction of Rs. Additionally, the net ecosystem productivity carbon sequestration (NEP-C) of the farmland ecosystem was positive under this system, indicating that the soil acts as a carbon “sink”. Therefore, a no-tillage treatment with a full quantity of green manure mulched on the soil surface can be used as a reasonable green manure return method to reduce carbon emissions from farmland in arid oasis irrigation regions. Full article
(This article belongs to the Section Innovative Cropping Systems)
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<p>Crop rotation sequence figure. Crop rotation sequence Ⅰ is spring wheat—green manure→spring maize and rotation sequence Ⅱ is spring maize→spring wheat—green manure.</p>
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<p>The characterization of changes in Rs and its components under different treatments. (<b>A</b>–<b>C</b>) are seasonal changes in Rs, Rh, and Ra, and (<b>D</b>–<b>F</b>) are annual means of Rs, Rh, and Ra. The data in the figure are the average of 2021, 2022, and 2023. The error bar indicates the value of LSD in the figure. Different lowercase letters indicate significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05). ** means significantly different at the 0.01 level. The same below. TG: tillage with the full quantity of green manure incorporated into the soil, NTG: no tillage with the full quantity of green manure mulched on the soil surface, T: tillage with roots incorporated into the soil and above-ground green manure removed, NT: no tillage with above-ground manure removed, CT: conventional tillage and leisure.</p>
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<p>The cumulative CO<sub>2</sub> emissions of Rs components under different treatments ((<b>A</b>–<b>C</b>) are the cumulative emissions of Rs, Rh, and Ra, respectively) and the ratio of each respiration component to the Rs (<b>D</b>). TG: tillage with the full quantity of green manure incorporated into the soil, NTG: no tillage with the full quantity of green manure mulched on the soil surface, T: tillage with roots incorporated into the soil and above-ground green manure removed, NT: no tillage with above-ground manure removed, CT: conventional tillage and leisure.</p>
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<p>Changes in soil temperature (ST) in 5 cm soil layer ((<b>A</b>–<b>C</b>) are ST changes for 2021, 2022, and 2023) and soil water content (SWC) in 0–30 cm soil layer under different treatments ((<b>D</b>–<b>F</b>) are SWC changes for 2021, 2022, and 2023). TG: tillage with the full quantity of green manure incorporated into the soil, NTG: no tillage with the full quantity of green manure mulched on the soil surface, T: tillage with roots incorporated into the soil and above-ground green manure removed, NT: no tillage with above-ground manure removed, CT: conventional tillage and leisure.</p>
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<p>Changes in SOC (<b>A</b>), MBC (<b>B</b>), and SBD (<b>C</b>) in 0–30 cm soil layer under different treatments. The data in the figure are the average of 2021, 2022, and 2023. *, ** means significantly different at the 0.05, 0.01 level. TG: tillage with the full quantity of green manure incorporated into the soil, NTG: no tillage with the full quantity of green manure mulched on the soil surface, T: tillage with roots incorporated into the soil and above-ground green manure removed, NT: no tillage with above-ground manure removed, CT: conventional tillage and leisure.</p>
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<p>Relationship between ST, SWC, and Rs components under different treatments. (<b>A</b>–<b>C</b>) are the correlations of Rs, Rh, and Ra with ST. (<b>D</b>–<b>F</b>) are the correlations of Rs, Rh, and Ra with SWC. TG: tillage with the full quantity of green manure incorporated into the soil, NTG: no tillage with the full quantity of green manure mulched on the soil surface, T: tillage with roots incorporated into the soil and above-ground green manure removed, NT: no tillage with above-ground manure removed, CT: conventional tillage and leisure.</p>
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<p>A structural equation model of the influence of soil environmental factors on Rs (<b>A</b>) and standardized total effects from SEM (<b>B</b>). The thickness of each arrow and the number on the arrow represent the standardized path coefficients (SPC) and their magnitudes, respectively, with the orange line representing a positive correlation, the blue line representing a negative correlation, and the dashed line representing no correlation. (*** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05; model parameter: CMIN/DF = 0.914, DF = 4, <span class="html-italic">p</span> = 0.455, CFI = 1.000, RFI = 0.933, RMSEA = 0.000).</p>
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17 pages, 20945 KiB  
Article
Responses of Soil Respiration and Ecological Environmental Factors to Warming and Thermokarst in River Source Wetlands of the Qinghai Lake Basin
by Yanli Yang, Ni Zhang, Zhiyun Zhou, Lin Li, Kelong Chen, Wei Ji and Xia Zhao
Biology 2024, 13(11), 863; https://doi.org/10.3390/biology13110863 - 24 Oct 2024
Viewed by 5663
Abstract
Global climate warming has led to the deepening of the active layer of permafrost on the Tibetan Plateau, further triggering thermal subsidence phenomena, which have profound effects on the carbon cycle of regional ecosystems. This study conducted warming (W) and thermal subsidence (RR) [...] Read more.
Global climate warming has led to the deepening of the active layer of permafrost on the Tibetan Plateau, further triggering thermal subsidence phenomena, which have profound effects on the carbon cycle of regional ecosystems. This study conducted warming (W) and thermal subsidence (RR) control experiments using an Open-Top Chamber (OTC) device in the river source wetlands of the Qinghai Lake basin. The aim was to assess the impacts of warming and thermal subsidence on soil temperature, volumetric water content, biomass, microbial diversity, and soil respiration (both autotrophic and heterotrophic respiration). The results indicate that warming significantly increased soil temperature, especially during the colder seasons, and thermal subsidence treatment further exacerbated this effect. Soil volumetric water content significantly decreased under thermal subsidence, with the RRW treatment having the most pronounced impact on moisture. Additionally, a microbial diversity analysis revealed that warming promoted bacterial richness in the surface soil, while thermal subsidence suppressed fungal community diversity. Soil respiration rates exhibited a unimodal curve during the growing season. Warming treatment significantly reduced autotrophic respiration rates, while thermal subsidence inhibited heterotrophic respiration. Further analysis indicated that under thermal subsidence treatment, soil respiration was most sensitive to temperature changes, with a Q10 value reaching 7.39, reflecting a strong response to climate warming. In summary, this study provides new scientific evidence for understanding the response mechanisms of soil carbon cycling in Tibetan Plateau wetlands to climate warming. Full article
(This article belongs to the Special Issue Wetland Ecosystems (2nd Edition))
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<p>The effects of warming and thermal subsidence on soil respiration in the Qinghai Lake basin experiment. (<b>A</b>) Qinghai Lake Basin and the location of the Wayan Mountain experimental research site. (<b>B</b>) Experimental sites for warming and thermal subsidence. (<b>C</b>) Measuring the soil respiration rate in thermokarst subsidence areas. (<b>D</b>) Measuring the soil respiration rate under warming treatment. (<b>E</b>) The interaction between OTC warming and thermal subsidence.</p>
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<p>The monthly average temperature and humidity, and the variation curves of air temperature and humidity in the experimental area for 2023. (<b>A</b>) Monthly average temperature for 2023. (<b>B</b>) Monthly average humidity for 2023. (<b>C</b>) Variation curve of air temperature and humidity in the experimental area for 2023.</p>
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<p>Changes in soil temperature, volumetric water content, and aboveground and belowground biomass under different treatments. (<b>A</b>) Soil temperature variation from June to September 2023; (<b>B</b>) Soil volumetric water content variation from June to September 2023; (<b>C</b>) Changes in aboveground and belowground biomass. Note: CK: control; W: warming treatment; RR: thaw settlement area; RRW: warming treatment in thaw settlement area. Different letters indicate significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of warming and thermokarst on soil respiration rate. (<b>A</b>) Soil respiration rate; (<b>B</b>) autotrophic respiration rate; (<b>C</b>) heterotrophic respiration rate. Note: CK: control; W: warming; RR: thermokarst; RRW: warming treatment of thermokarst. Different letters indicate significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The correlations between soil respiration and its environmental factors across the four treatments. (<b>A</b>) Control treatment; (<b>B</b>) warming treatment; (<b>C</b>) thermokarst treatment; (<b>D</b>) combined thermokarst and warming treatment. Note: VWC represents soil volumetric water content, EC represents soil electrical conductivity, pH represents acidity, ST represents soil temperature, AT represents air temperature, AGB represents aboveground biomass, BGB represents underground biomass, RH represents air relative humidity, RS represents soil respiration rate, Ra represents autotrophic respiration rate, and Rh represents heterotrophic respiration rate. * indicates <span class="html-italic">p</span> &lt; 0.05; ** indicates <span class="html-italic">p</span> &lt; 0.01; *** indicates <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Nonlinear fitting of soil respiration rates and their environmental factors with soil temperature under different treatments; (<b>A</b>) soil respiration rate; (<b>B</b>) soil autotrophic respiration rate; (<b>C</b>) soil heterotrophic respiration rate.</p>
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17 pages, 1593 KiB  
Article
Impact of Top-Down Regulation on the Growth Efficiency of Freshwater Bacterioplankton
by Angia Sriram Pradeep Ram, Hermine Billard, Fanny Perriere, Olivier Voldoire and Jonathan Colombet
Microorganisms 2024, 12(10), 2061; https://doi.org/10.3390/microorganisms12102061 - 15 Oct 2024
Viewed by 972
Abstract
To investigate the hypothesis of top-down control by viruses and heterotrophic nanoflagellates on bacterial-mediated carbon fluxes in freshwater systems, a year-long study (2023–2024) was conducted in the pelagic zone of Lake Saint-Gervais (France). The variability in BGE (9.9% to 45.5%) was attributed to [...] Read more.
To investigate the hypothesis of top-down control by viruses and heterotrophic nanoflagellates on bacterial-mediated carbon fluxes in freshwater systems, a year-long study (2023–2024) was conducted in the pelagic zone of Lake Saint-Gervais (France). The variability in BGE (9.9% to 45.5%) was attributed to the decoupling of production and respiration, providing bacterioplankton communities with a competitive advantage in adapting to fluctuating environmental disturbances in freshwater systems. The high nucleic acid (HNA) bacterial community, the active fraction, contributed the most to bacterial production and was linked to BGE estimates. Weak bottom-up controls (nutrient concentrations and stoichiometry) on BGE suggested a stronger role for mortality forces. Among viral subgroups (VLP1–VLP4) identified via flow cytometry, the dominant low-fluorescence DNA VLP1 subgroup (range = 0.7 to 3.1 × 108 VLP mL−1) accounting for the majority of viral production was closely linked to the HNA population. Both top-down forces exerted antagonistic effects on BGE at the community level. The preferential lysis and grazing of the susceptible HNA population, which stimulated bacterial community respiration more than production in the non-target population, resulted in reduced BGE. These results underscore the key role of top-down processes in shaping carbon flux through bacterioplankton in this freshwater system. Full article
(This article belongs to the Special Issue Microbial Communities in Aquatic Environments)
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<p>Seasonal variations in total viral abundances (VA), expressed as viral-like particles (VLP), alongside the distribution of viral subgroups (VLP1, VLP2, VLP3, and VLP4) distinguished by their fluorescence characteristics using flow cytometry. The data presented represent the averages of triplicate measurements.</p>
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<p>Seasonal variations in total heterotrophic bacteria (BA) and their subgroups, categorized as low nucleic acid (LNA) and high nucleic acid (HNA) bacteria, identified using flow cytometry. The data shown represent the averages of triplicate measurements.</p>
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<p>Seasonal dynamics of bacterial metabolic parameters, including bacterial production (BP) and respiration (BR), along with the calculated bacterial growth efficiency (BGE) in the pelagic zone of Lake Saint-Gervais. Error bars represent the standard deviation (SD) from triplicate measurements (<span class="html-italic">n</span> = 3).</p>
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<p>A regression plot showing the relationship between high nucleic acid bacteria (HNA) and the low-fluorescent viral DNA subgroup VLP1 in Lake Saint-Gervais (y = 0.31x + 0.39, r<sup>2</sup> = 0.73, <span class="html-italic">p</span> &lt; 0.001, <span class="html-italic">n</span> = 24).</p>
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<p>Relationship of viral lysis with bacterial growth efficiency ((<b>A</b>), y = 94.35x<sup>−0.75</sup>, r = −0.75, <span class="html-italic">p</span> &lt; 0.001, <span class="html-italic">n</span> = 24) and carbon demand ((<b>B</b>), y = 4.59x + 63.48, r = 0.65, <span class="html-italic">p</span> &lt; 0.001, <span class="html-italic">n</span> = 24) in Lake Saint-Gervais.</p>
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15 pages, 2892 KiB  
Review
Exploring the Factors Affecting Terrestrial Soil Respiration in Global Warming Manipulation Experiments Based on Meta-Analysis
by Xue Chen, Haibo Hu, Qi Wang, Xia Wang and Bing Ma
Agriculture 2024, 14(9), 1581; https://doi.org/10.3390/agriculture14091581 - 11 Sep 2024
Cited by 1 | Viewed by 1330
Abstract
Warming significantly impacts soil respiration in terrestrial ecosystems, thereby altering global carbon cycle processes. Numerous field experiments have investigated the effects of warming on soil respiration (Rs), but the results have been inconsistent due to various factors such as ecosystem type, soil warming [...] Read more.
Warming significantly impacts soil respiration in terrestrial ecosystems, thereby altering global carbon cycle processes. Numerous field experiments have investigated the effects of warming on soil respiration (Rs), but the results have been inconsistent due to various factors such as ecosystem type, soil warming amplitude, duration, and environmental conditions. In this study, we conducted a meta-analysis of 1339 cases from 70 studies in terrestrial ecosystems to evaluate the response of Rs, heterotrophic respiration (Rh), and autotrophic respiration (Ra) to global warming. The results indicated that Rs, Rh, and Ra increased by 13.88%, 15.03%, and 19.72%, respectively, with a significant rise observed across different ecosystems. Generally, Rs increased with rising temperatures within a specific range (0–4 °C), whereas higher temperatures (>4 °C) did not significantly affect Rs. Moreover, Rs, Rh, and Ra exhibited an initial increase followed by a decrease with prolonged duration, indicating an adaptive response to climate warming. Additionally, Rs and Rh exhibit significant seasonal variations, with levels in winter being markedly higher than in summer. Furthermore, environmental factors exerted direct or indirect effects on soil respiration components. The factors’ importance for Rs was ranked as microbial biomass carbon (MBC) > mean annual temperature (MAT) > mean annual precipitation (MAP), for Rh as soil organic carbon (SOC) > MBC > MAT > MAP, and for Ra as belowground biomass (BGB) > aboveground biomass (AGB) > SOC. Future research should focus on the interactions among explanatory factors to elucidate the response mechanisms of soil respiration under global warming conditions. Full article
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)
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<p>Global distribution of soil respiration experiments used in this analysis.</p>
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<p>Responses of Rs, Rh, and Ra to the top-down effects of global warming. Effect sizes (log response ratio) and 95% confidence intervals (CI) for each sample are given in order. lnRR = 0, dashed blue line.</p>
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<p>Orchard plot showing number of cases, <span class="html-italic">p</span>-values, mean estimate, confidence interval, and individual effect sizes and their precision (inverse variance) of soil respiration (Rs, (<b>a</b>)), heterotrophic respiration (Rh, (<b>b</b>)), and autotrophic respiration (Ra, (<b>c</b>)) in different ecosystems. lnRR = 0, dashed blue line.</p>
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<p>Orchard plot showing number of cases, <span class="html-italic">p</span>-values, mean estimate, confidence interval, and individual effect sizes and their precision (inverse variance) of soil respiration (Rs, (<b>a</b>,<b>d</b>,<b>g</b>)), heterotrophic respiration (Rh, (<b>b</b>,<b>e</b>,<b>h</b>)), and autotrophic respiration (Ra, (<b>c</b>,<b>f</b>,<b>i</b>)) in different warming amplitude, warming duration, and warming season. lnRR = 0, dashed blue line.</p>
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<p>Relationships between soil respiration (Rs, (<b>a</b>,<b>d</b>)), heterotrophic respiration (Rh, (<b>b</b>,<b>e</b>)), and autotrophic respiration (Ra, (<b>c</b>,<b>f</b>)) responses to climate warming treatment for mean annual temperature (MAT) and mean annual precipitation (MAP).</p>
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<p>The response of Rs, Rh, and Ra to experimental warming with the changes of plant carbon pool and soil properties. * indicates statistical significance (<span class="html-italic">p</span> &lt; 0.05). Numbers indicate the effect size (percentage change).</p>
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<p>The importance of the factors of significance (<span class="html-italic">p</span> &lt; 0.05) for Rs, Rh, and Ra.</p>
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13 pages, 3285 KiB  
Article
Minor Effects of Canopy and Understory Nitrogen Addition on Soil Organic Carbon Turnover Time in Moso Bamboo Forests
by Changli Zeng, Shurui He, Boyin Long, Zhihang Zhou, Jie Hong, Huan Cao, Zhihan Yang and Xiaolu Tang
Forests 2024, 15(7), 1144; https://doi.org/10.3390/f15071144 - 1 Jul 2024
Viewed by 1098
Abstract
Increased atmospheric nitrogen (N) deposition has greatly influenced soil organic carbon (SOC) dynamics. Currently, the response of SOC to atmospheric N deposition is generally detected through understory N addition, while canopy processes have been largely ignored. In the present study, canopy N addition [...] Read more.
Increased atmospheric nitrogen (N) deposition has greatly influenced soil organic carbon (SOC) dynamics. Currently, the response of SOC to atmospheric N deposition is generally detected through understory N addition, while canopy processes have been largely ignored. In the present study, canopy N addition (CN) and understory N addition (UN, 50 and 100 kg N ha−1 year−1) were performed in a Moso bamboo forest to compare whether CN and UN addition have consistent effects on SOC and SOC turnover times (τsoil: defined as the ratio of SOC stock and soil heterotrophic respiration) with a local NHx:NOy ratio of 2.08:1. The experimental results showed that after five years, the SOC content of canopy water addition without N addition (CN0) was 82.9 g C kg−1, while it was 79.3, 70.7, 79.5 and 74.5 g C kg−1 for CN50, CN100, UN50 and UN100, respectively, and no significant difference was found for the SOC content between CN and UN. Five-year N addition did not significantly change τsoil, which was 34.5 ± 7.4 (mean ± standard error) for CN0, and it was 24.9 ± 4.8, 22.4 ± 4.9, 30.5 ± 4.0 and 22.1 ± 6.5 years for CN0, CN50, CN100, UN50 and UN100, respectively. Partial least squares structural equation modeling explained 93% of the variance in τsoil, and the results showed that soil enzyme activity was the most important positive factor controlling τsoil. These findings contradicted the previous assumption that UN may overestimate the impacts of N deposition on SOC. Our findings were mainly related to the high N deposition background in the study area, the special forest type of Moso bamboo and the short duration of the experiment. Therefore, our study had significant implications for modeling SOC dynamics to N deposition for high N deposition areas. Full article
(This article belongs to the Special Issue Soil Organic Carbon and Nutrient Cycling in the Forest Ecosystems)
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<p>Geographic location of the study area and map of the sample plots. CN0: canopy water addition without N addition; CN50 and CN100: canopy N addition with 50 and 100 kg N ha<sup>−1</sup> year<sup>−1</sup>; UN50 and UN100: understory N addition with 50 and 100 kg N ha<sup>−1</sup> year<sup>−1</sup>. The same below.</p>
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<p>Changes in SOC content to canopy and understory N addition. The error bars indicate the standard error (n = 3). The abbreviations can be found in <a href="#sec2dot2-forests-15-01144" class="html-sec">Section 2.2</a>.</p>
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<p>Changes in topsoil (0–10 cm) (<b>a</b>) recalcitrant organic carbon (ROC, mg kg<sup>−1</sup>), (<b>b</b>) easily oxidized carbon (EOC, mg kg<sup>−1</sup>), (<b>c</b>) dissolved organic carbon (DOC, mg kg<sup>−1</sup>) and (<b>d</b>) microbial biomass carbon (MBC, mg kg<sup>−1</sup>) contents to canopy and understory N addition. The error bars indicate the standard error (n = 3). ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Changes in (<b>a</b>) SOC stocks (Mg C ha<sup>−1</sup>) and (<b>b</b>) SOC turnover time (τ<sub>soil</sub>, years) of canopy and understory N addition. The error bars indicate the standard error (n = 3).</p>
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<p>The PLS-SEM model diagram shows the relationship between each environment variable and mean turnover time. SPCP: soil chemical properties; SWC: soil water content; BD: bulk density; HTP: hair tube porosity; NCP: non-capillary porosity; SWS: soil water storage; MWHC: maximum water-holding capacity; HTWHC: hair tube water-holding capacity; SVWC: soil volume water content. Lines and arrows represent relationships between variables. The blue and red lines represent positive and negative effects, respectively. The number represents the path coefficient. *** <span class="html-italic">p</span> &lt; 0.001.</p>
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18 pages, 2325 KiB  
Article
Wildfire Effects on the Soil Respiration and Bacterial Microbiota Composition in Mediterranean-Type Ecosystems
by Panagiotis Dalias, Eleftherios Hadjisterkotis, Michalis Omirou, Ourania Michaelidou, Ioannis M. Ioannides, Damianos Neocleous and Anastasis Christou
Fire 2024, 7(7), 213; https://doi.org/10.3390/fire7070213 - 26 Jun 2024
Viewed by 1777
Abstract
This work provides insights into the effect of fire on soil processes in Mediterranean-type ecosystems in Cyprus. Soil samples from mountainous sites that were subjected to a summer wildfire and adjacent control samples were collected. Incubations were used to estimate basal respiration and [...] Read more.
This work provides insights into the effect of fire on soil processes in Mediterranean-type ecosystems in Cyprus. Soil samples from mountainous sites that were subjected to a summer wildfire and adjacent control samples were collected. Incubations were used to estimate basal respiration and isolate soil CO2 release of heterotrophic microorganisms from autotrophic root respiration and heterotrophic respiration from litter decomposition. Physicochemical property changes, bacteria community changes using DNA extraction and 16S rRNA gene analysis, and the effects of ash and fresh litter addition were studied to reveal the microbial composition and the post-fire soil function. Laboratory incubation showed that burned soils constantly showed higher microbial respiration rates compared with control unburned areas, even six months after a fire. Adding ash to unburned samples increased microbial respiration, suggesting that increased nutrient availability positively corelates with the increased release of CO2 from fire-affected soil. Elevated temperatures due to the wildfire exerted significant effects on the composition of soil bacterial microbiota. Nevertheless, the wildfire did not affect the alpha-diversity of soil bacteria. New communities of microorganisms are still able to decompose fresh plant material after a fire, but at a slower rate than natural pre-fire populations. Full article
(This article belongs to the Special Issue Effects of Fires on Forest Ecosystems)
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<p>Cumulative microbial respiration in mg of C-CO<sub>2</sub> release per g of dry soil during incubation (first incubation) of soil samples coming from burned (B) and unburned (control-C) sites on either side of the fire front line. OG: Olive Grove, PF: Pine Forest, MV: Macchia Vegetation.</p>
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<p>Cumulative microbial respiration in mg of C-CO<sub>2</sub> release per g of dry soil during incubation (fourth incubation) of soil samples coming from burned (B) and unburned (control-C) sites. The results in each graph illustrate differences in respiration of a soil after mixing with lucerne (luc) litter.</p>
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<p>(<b>A</b>) Relative abundance of predominant bacterial phyla (&gt;1%) in the soils sampled from burned and unburned sites in three different ecosystems, namely, Pine forest, Olive grove, and Macchia, as obtained by 16S rRNA gene metabarcoding. (<b>B</b>) Dominant bacteria phyla of burned and unburned soils in different ecosystems were screened out by the Kruskal–Wallis test corrected with the Benjamini–Hochberg algorithm. No sign was considered insignificant; * <span class="html-italic">p</span> &lt; 0.05 was considered as significant difference.</p>
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<p>Comparison (Kruskal–Wallis test) of alpha-diversity metrics (Fisher, Gini-Gimpson, Inverse-Simpson, and Shannon) between the bacterial communities of burned and unburned soil samples from three different vegetation type systems. No letters indicate no statistically significant differences.</p>
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<p>Non-metric Multidimensional Scaling (NMDS) Plot visualization of beta diversity (PERMANOVA: R<sup>2</sup> = 12.34, <span class="html-italic">p = 0.023</span>), using the Bray–Curtis distances, separating soil samples from burned and unburned sampling sites. Plot ellipses represent the 95% confidence regions for the grouped sampling sites (nMDS stress = 0.17).</p>
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<p>Venn diagrams showing the number of bacterial ASVs shared between burned and unburned soils.</p>
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16 pages, 4944 KiB  
Article
Characteristics, Sources, and Mechanisms of Soil Respiration under Simulated Rainfall in a Native Karst Forest in Southwestern China
by Wenqiang Lv, Xiuming Liu and Hu Ding
Forests 2024, 15(6), 945; https://doi.org/10.3390/f15060945 - 30 May 2024
Viewed by 871
Abstract
Rainfall significantly affects soil respiration rates by altering microbial activity and organic matter decomposition. In karst regions, it also impacts carbonate dissolution and precipitation, further influencing soil CO2 flux. Investigating the mechanism of rainfall’s impact on soil respiration is essential for accurately [...] Read more.
Rainfall significantly affects soil respiration rates by altering microbial activity and organic matter decomposition. In karst regions, it also impacts carbonate dissolution and precipitation, further influencing soil CO2 flux. Investigating the mechanism of rainfall’s impact on soil respiration is essential for accurately evaluating and predicting changes in terrestrial ecosystems. However, our understanding of the interaction between rainfall and soil respiration in the extensive karst ecosystems of southwestern China remains limited. This study conducted field-based simulated rainfall experiments to examine variations in soil respiration rates and elucidate the associated control mechanisms through stable carbon isotope composition analysis. Simulated rainfall significantly increased the CO2 release via soil respiration. We observed significant differences in the δ13C value of soil-respired CO2 before and after simulated rainfall. Following the rain, the δ13C of soil-respired CO2 was enriched compared to that before the rain. Through isotope data analysis, we found that the increased soil CO2 emissions were primarily driven by heterotrophic respiration, likely stimulated via changes in soil moisture, affecting microbial growth conditions. Furthermore, the variation in soil moisture affected carbonate dissolution and precipitation, potentially increasing the soil CO2 release after rainfall. In conclusion, these findings expand our understanding of rainfall’s effects on soil respiration in the native karst forests of southwestern China, contributing to the prediction of carbon cycling processes in such ecosystems. The data from this study have significant implications for addressing the release of greenhouse gases in efforts to combat climate change. Full article
(This article belongs to the Section Forest Soil)
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<p>Location of sampling points.</p>
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<p>Topographic map of sampling points.</p>
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<p>Changes in soil water content before and after simulated rainfall. (<b>a</b>) 8 mm simulated rainfall. (<b>b</b>) 25 mm simulated rainfall in April. (<b>c</b>) 25 mm simulated rainfall in June.</p>
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<p>Changes in soil temperature before and after simulated rainfall. (<b>a</b>) 8 mm simulated rainfall. (<b>b</b>) 25 mm simulated rainfall in April. (<b>c</b>) 25 mm simulated rainfall in June.</p>
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<p>Soil respiration rates before and after simulated rainfall. (<b>a</b>) 8 mm simulated rainfall. (<b>b</b>) 25 mm simulated rainfall in April. (<b>c</b>) 25 mm simulated rainfall in June.</p>
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<p>Variation in δ<sup>13</sup>C of soil-respired CO<sub>2</sub> before and after simulated rainfall. (<b>a</b>) 8 mm simulated rainfall. (<b>b</b>) 25 mm simulated rainfall in April. (<b>c</b>) 25 mm simulated rainfall in June.</p>
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14 pages, 1849 KiB  
Article
Nitrogen Addition Decreased Respiration and Heterotrophic Respiration but Increased Autotrophic Respiration in a Cabbage (Brassica pekinensis Rupr) Experiment in the Northeast Plains
by Xinming Jiang, Xu Yan, Shuyan Liu, Lili Fu, Xiaomei Gao and Dongyan Huang
Agriculture 2024, 14(4), 596; https://doi.org/10.3390/agriculture14040596 - 9 Apr 2024
Cited by 1 | Viewed by 1102
Abstract
Farmland soil respiration (Rs) significantly impacts the global carbon (C) cycle. Although nitrogen (N) can promote crop growth and increase yields, its relationship with Rs and its constituents, including autotrophic respiration (Ra) and heterotrophic respiration (Rh), remains [...] Read more.
Farmland soil respiration (Rs) significantly impacts the global carbon (C) cycle. Although nitrogen (N) can promote crop growth and increase yields, its relationship with Rs and its constituents, including autotrophic respiration (Ra) and heterotrophic respiration (Rh), remains unclear. Therefore, a field study was carried out in a cabbage (Brassica pekinensis Rupr) system to probe the impact of N addition on Rs, Ra, and Rh. Five levels of N addition, including 0 kg N hm−2·yr−1 (N0), 50 kg N hm−2·yr−1 (N50), 100 kg N hm−2·yr−1 (N100), 150 kg N hm−2·yr−1 (N150), and 200 kg N hm−2·yr−1 (N200), started in March 2022. The Rs (Ra and Rh) and soil samples were measured and collected twice a month. The findings revealed the following: (1) N fertilizer enhanced Ra while reducing Rs and Rh; (2) soil temperature (ST), belowground net primary productivity (BNPP), soil inorganic N (SIN), and soil total C/total N (C/N) were the significant elements influencing Ra, and microbial biomass carbon (MBC), SIN, and microbial diversity (MD) were the primary factors influencing Rh; (3) partial least squares-path models (PLS-PM) showed that ST and SIN directly impacted Rh, while ST and BNPP tangentially influenced Ra; (4) 150 kg N hm−2·yr−1 was the ideal N addition rate for the cabbage in the region. In summary, the reactions of Ra and Rh to N fertilizer in the Northeast Plains are distinct. To comprehend the underlying processes of Rs, Ra, and Rh, further long-term trials involving various amounts of N addition are required, particularly concerning worsening N deposition. Full article
(This article belongs to the Section Agricultural Soils)
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<p><span class="html-italic">Rs</span> under different N addition. Under various N treatments (N0 to N200), CO<sub>2</sub> fluxes displayed irregular patterns. Minimal N fertilization had negligible impacts on <span class="html-italic">Rs</span> and its components, but substantial changes occurred with higher N levels (&gt;150 kg N hm<sup>−2</sup>·yr<sup>−1</sup>). N0 consistently yielded the highest <span class="html-italic">Rs</span>, while N200 the lowest. Compared to N0, <span class="html-italic">Rs</span> decreased by 6.68–9.34% for N50–N200. <span class="html-italic">Rh</span> declined by 4.56–16.42%, but <span class="html-italic">Ra</span> rose by 16.67–41.07%. Note: (<b>a</b>–<b>c</b>) stand for <span class="html-italic">Rs</span>, <span class="html-italic">Ra</span>, and <span class="html-italic">Rh</span>, respectively.</p>
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<p>Effect of N fertilization on soil biotic and abiotic factors. As N application increased, ST decreased to a minimum under N50 then rose. Both SWC and SOC peaked under N150 before declining, pH and C/N decreased while SIN increased. Compared to N0, SOC was elevated by 1.61–32.26% across treatments, with SIN showing a 1.22–46.88% increase. Notes: ST stands for soil temperature, SWC for soil moisture content, SOC for soil organic matter carbon, C/N for soil total carbon/total nitrogen, and SIN for soil inorganic nitrogen.</p>
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<p>Effect of N addition on soil microbial activities. Nitrogen fertilization had a profound impact on soil microbial parameters, causing a notable surge in microbial biomass carbon (MBC) and nitrogen (MBN), which peaked at N150 before declining. Conversely, microbial diversity (MD) and richness (MR) diminished with escalating N levels. This altered the microbial composition significantly. Notes: MD stands for microbial diversity (bacterial diversity plus fungal diversity), MR stands for microbial richness (bacterial richness adds fungus richness), MBC for microbial biomass carbon, and MBN for microbial biomass nitrogen.</p>
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<p>Effects of N fertilizer on ANPP and BNPP. Under sustained nitrogen fertilization, both aboveground (ANPP) and belowground (BNPP) net primary productivity exhibited an initial rise, peaking at the N50 level before showing a decline. Specifically, in comparison to the control (N0), BNPP experienced notable increases of 21.00%, 27.40%, 68.04%, and 38.81% across the N50, N100, N150, and N200 treatments, respectively. Notes: The ANPP and BNPP stand for aboveground net primary productivity and belowground net primary productivity, respectively.</p>
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<p>Pearson correlation analysis for <span class="html-italic">Ra</span> and <span class="html-italic">Rh</span>. (<b>a</b>) Represents the correlations among <span class="html-italic">Ra</span> and MBC, MBN, BNPP, SIN, ANPP, SOC, MD, SWC, pH, MR, C/N, and ST. (<b>b</b>) Represents the correlations among <span class="html-italic">Rh</span> and pH, SINM MD, MR, ANPP, SOC, C/N, MBN, SWC, ST, BNPP, and MBC.</p>
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<p>Box plots of the relative contributions of driving factors on <span class="html-italic">Ra</span> and <span class="html-italic">Rh</span>. The most significant factors influencing <span class="html-italic">Ra</span> were ST, BNPP, SIN, and C/N, whereas <span class="html-italic">Rh</span> was most accurately predicted by MBC, SIN, and MD, all with <span class="html-italic">p</span>-values less than 0.01. This study explained 56% and 80% of the variance in <span class="html-italic">Ra</span> and <span class="html-italic">Rh</span>, respectively. Note: (<b>a</b>,<b>b</b>) represent <span class="html-italic">Ra</span> and <span class="html-italic">Rh</span>, respectively. The importance of predictor variables is assessed based on the percentage increased mean square error (lnMSE) from 100 runs of the random forest model, with ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>PLS-PM for the effect of N addition on the soil-microbial-biomass system. ST and BNPP are the primary direct regulators of <span class="html-italic">Ra</span>, while ST and MBC are the main drivers of <span class="html-italic">Rh</span>. SIN indirectly influenced <span class="html-italic">Ra</span>, with a weak direct impact on <span class="html-italic">Rh</span> (<span class="html-italic">p</span> &lt; 0.1). Changes in <span class="html-italic">Ra</span> are predominantly governed by decreased ST and elevated BNPP, mediated by various factors. Both BNPP and SIN positively influenced <span class="html-italic">Ra</span>, whereas <span class="html-italic">Rh</span> variations were primarily attributed to MBC, SIN, and MD. Note: The letters a and b represent <span class="html-italic">Ra</span> and <span class="html-italic">Rh</span>, respectively.</p>
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18 pages, 6351 KiB  
Article
Impact of Soil Organic Layer Thickness on Soil-to-Atmosphere GHG Fluxes in Grassland in Latvia
by Dana Purviņa, Zaiga Anna Zvaigzne, Ilona Skranda, Raitis Normunds Meļņiks, Guna Petaja, Ieva Līcīte, Aldis Butlers and Arta Bārdule
Agriculture 2024, 14(3), 387; https://doi.org/10.3390/agriculture14030387 - 28 Feb 2024
Viewed by 1315
Abstract
Drained organic soils in agricultural land are considered significant contributors to total greenhouse gas (GHG) emissions, although the temporal and spatial variation of GHG emissions is high. Here, we present results of the study on soil-to-atmosphere fluxes of carbon dioxide (CO2), [...] Read more.
Drained organic soils in agricultural land are considered significant contributors to total greenhouse gas (GHG) emissions, although the temporal and spatial variation of GHG emissions is high. Here, we present results of the study on soil-to-atmosphere fluxes of carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) from drained organic (fen) soils in grassland. A two-year study (from July 2021 to June 2023) was conducted in three research sites in Latvia (Europe’s hemiboreal zone). Soil total respiration (Rtot), CH4 and N2O fluxes were determined using a manual opaque chamber technique in combination with gas chromatography, while soil heterotrophic respiration (Rhet) was measured with a portable spectrometer. Among research sites, the thickness of the soil organic layer ranged from 10 to 70 cm and mean groundwater level ranged from 27 to 99 cm below the soil surface. Drained organic soil in all research sites was a net source of CO2 emissions (mean 3.48 ± 0.33 t CO2-C ha−1 yr−1). No evidence was obtained that the thickness of the soil organic layer (ranging from 10 to 70 cm) and OC stock in soil can be considered one of the main affecting factors of magnitude of net CO2 emissions from drained organic soil. Drained organic soil in grassland was mostly a source of N2O emissions (mean 2.39 ± 0.70 kg N2O-N ha−1 yr−1), while the soil both emitted and consumed atmospheric CH4 depending on the thickness of the soil organic layer (ranging from −3.26 ± 1.33 to 0.96 ± 0.10 kg CH4-C ha−1 yr−1). Full article
(This article belongs to the Section Agricultural Soils)
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<p>Geographical distribution of research sites in Latvia belonging to the Europe’s hemiboreal zone.</p>
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<p>Soil total respiration (R<sub>tot</sub>) depending on thickness of soil organic layer (<b>A</b>) and carbon (C) content at 0–20 cm soil layer (<b>B</b>). In the box plots (<b>A</b>), the medians are shown as bold horizontal lines in the boxes, the mean values are shown as red dots, and the black dots denote outliers of the datasets; groups with the same letter (a) are not statistically different from each other (<span class="html-italic">p</span> &gt; 0.05). In (<b>B</b>), this study’s results (black dots) are supplemented by data (yellow points) from a previous study in Latvia conducted in drained grasslands with deep peat (&gt;40 cm) soils [<a href="#B41-agriculture-14-00387" class="html-bibr">41</a>]; grey area reflects confidence interval of linear regression.</p>
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<p>Soil total respiration (R<sub>tot</sub>) depending on air temperature, soil temperature at 10 cm depth and soil moisture (linear regressions). Grey area reflects confidence interval of regression.</p>
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<p>Soil heterotrophic respiration (R<sub>het</sub>) depending on air temperature and soil moisture. Grey area in left graph shows confidence interval of regression.</p>
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<p>Soil-to-atmosphere CH<sub>4</sub> fluxes depending on thickness of soil organic layer (<b>A</b>) and carbon (C) content at 0–20 cm soil layer (<b>B</b>). In the box plots (<b>A</b>), the medians are shown as bold horizontal lines in the boxes, the mean values are shown as red dots, and the black dots denote outliers of the datasets; groups with different letters (a–c) are statistically different from each other (<span class="html-italic">p</span> &lt; 0.05). In (<b>B</b>), this study’s results (black dots) are supplemented by data (yellow points) from a previous study in Latvia conducted in drained grasslands with deep peat (&gt;40 cm) soils [<a href="#B41-agriculture-14-00387" class="html-bibr">41</a>]; grey area reflects confidence interval of regression.</p>
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<p>Soil-to-atmosphere CH<sub>4</sub> fluxes depending on air temperature, soil temperature at 10 cm depth and soil moisture. Grey area reflects confidence interval of linear regression.</p>
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<p>Soil-to-atmosphere N<sub>2</sub>O fluxes depending on thickness of soil organic layer (<b>A</b>) and carbon (C) content at 0–20 cm soil layer (<b>B</b>). In the box plots (<b>A</b>), the medians are shown as bold horizontal lines in the boxes, the mean values are shown as red dots, and the black dots denote outliers of the datasets; groups with the same letter (a) are not statistically different from each other (<span class="html-italic">p</span> &gt; 0.05). In (<b>B</b>), this study’s results (black dots) are supplemented by data (yellow points) from a previous study in Latvia conducted in drained grasslands with deep peat (&gt;40 cm) soils [<a href="#B41-agriculture-14-00387" class="html-bibr">41</a>]; grey area reflects confidence interval of linear regression.</p>
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<p>Soil-to-atmosphere N<sub>2</sub>O fluxes depending on air temperature, soil temperature at 10 cm depth and soil moisture. Grey area reflects confidence interval of linear regression.</p>
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<p>Variation in soil temperature (at 10 cm depth), groundwater level and soil moisture in the research sites during GHG sampling (field surveys within the study). In the box plots, the medians are shown as bold horizontal lines in the boxes, the mean values are shown as red dots, and the black dots denote outliers of the datasets.</p>
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