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15 pages, 3264 KiB  
Article
Successions of Bacterial and Fungal Communities in Biological Soil Crust under Sand-Fixation Plantation in Horqin Sandy Land, Northeast China
by Chengyou Cao, Ying Zhang and Zhenbo Cui
Forests 2024, 15(9), 1631; https://doi.org/10.3390/f15091631 (registering DOI) - 15 Sep 2024
Abstract
Biological soil crusts (BSCs) serve important functions in conserving biodiversity and ecological service in arid and semi-arid regions. Afforestation on shifting sand dunes can induce the formation of BSC on topsoil, which can accelerate the restoration of a degraded ecosystem. However, the studies [...] Read more.
Biological soil crusts (BSCs) serve important functions in conserving biodiversity and ecological service in arid and semi-arid regions. Afforestation on shifting sand dunes can induce the formation of BSC on topsoil, which can accelerate the restoration of a degraded ecosystem. However, the studies on microbial community succession along BSC development under sand-fixation plantations in desertification areas are limited. This paper investigated the soil properties, enzymatic activities, and bacterial and fungal community structures across an age sequence (0-, 10-, 22-, and 37-year-old) of BSCs under Caragana microphylla sand-fixation plantations in Horqin Sandy Land, Northeast China. The dynamics in the diversities and structures of soil bacterial and fungal communities were detected via the high-throughput sequencing of the 16S and ITS rRNA genes, respectively. The soil nutrients and enzymatic activities all linearly increased with the development of BSC; furthermore, soil enzymatic activity was more sensitive to BSC development than soil nutrients. The diversities of the bacterial and fungal communities gradually increased along BSC development. There was a significant difference in the structure of the bacterial/fungal communities of the moving sand dune and BSC sites, and similar microbial compositions among different BSC sites were found. The successions of microbial communities in the BSC were characterized as a sequential process consisting of an initial phase of the faster recoveries of dominant taxa, a subsequent slower development phase, and a final stable phase. The quantitative response to BSC development varied with the dominant taxa. The secondary successions of the microbial communities of the BSC were affected by soil factors, and soil moisture, available nutrients, nitrate reductase, and polyphenol oxidase were the main influencing factors. Full article
(This article belongs to the Section Forest Soil)
Show Figures

Figure 1

Figure 1
<p>Cluster analysis of the structures of soil bacterial (<b>a</b>) and fungal (<b>b</b>) communities. MSD: moving sand dune; SC-10, SC-22, and SC-37: 10-, 22-, and 37-year biological soil crust, respectively.</p>
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<p>Relative abundances of dominant taxa in different sites. (<b>a</b>): bacterial phylum; (<b>b</b>): bacterial genus; (<b>c</b>): fungal phylum; (<b>d</b>): fungal genus. MSD: moving sand dune; SC-10, SC-22, and SC-37: 10-, 22-, and 37-year biological soil crust, respectively.</p>
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<p>Linear responses of the relative abundances of dominant bacterial phyla to biological soil crust age. (<b>a</b>): Proteobacteria; (<b>b</b>): Actinobacteria; (<b>c</b>): Chloroflexi; (<b>d</b>): Bacteroidetes.</p>
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<p>Linear responses of the relative abundances of dominant bacterial genera to BSC age. (<b>a</b>): <span class="html-italic">Sphingomonas</span>; (<b>b</b>): RB41; (<b>c</b>): <span class="html-italic">Ambiguous</span>; (<b>d</b>): <span class="html-italic">Segetibacter</span>; (<b>e</b>): <span class="html-italic">Flavisolibacter</span>; (<b>f</b>): <span class="html-italic">Haliangium</span>; (<b>g</b>): <span class="html-italic">Pseudarthrobacter</span>; (<b>h</b>): <span class="html-italic">Roseiflexus</span>.</p>
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<p>RDA between bacterial (<b>a</b>)/fungal (<b>b</b>) community structure and soil properties. SM: soil moisture; SOM: soil organic matter; TN: total N; AN: NH<sub>4</sub>-N; TP: total P; AP: available P; AK: available K. MSD: moving sand dune (0 yr); SC10, SC22, and SC37: 10, 22, and 37 yr biological soil crust, respectively.</p>
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21 pages, 32879 KiB  
Article
Soil and Water Bioengineering in Fire-Prone Lands: Detecting Erosive Areas Using RUSLE and Remote Sensing Methods
by Melanie Maxwald, Ronald Correa, Edwin Japón, Federico Preti, Hans Peter Rauch and Markus Immitzer
Fire 2024, 7(9), 319; https://doi.org/10.3390/fire7090319 - 13 Sep 2024
Viewed by 370
Abstract
Soil and water bioengineering (SWBE) measures in fire-prone areas are essential for erosion mitigation, revegetation, as well as protection of settlements against inundations and landslides. This study’s aim was to detect erosive areas at the basin scale for SWBE implementation in pre- and [...] Read more.
Soil and water bioengineering (SWBE) measures in fire-prone areas are essential for erosion mitigation, revegetation, as well as protection of settlements against inundations and landslides. This study’s aim was to detect erosive areas at the basin scale for SWBE implementation in pre- and post-fire conditions based on a wildfire event in 2019 in southern Ecuador. The Revised Universal Soil Loss Equation (RUSLE) was used in combination with earth observation data to detect the fire-induced change in erosion behavior by adapting the cover management factor (C-factor). To understand the spatial accuracy of the predicted erosion-prone areas, high-resolution data from an Unmanned Aerial Vehicle (UAV) served for comparison and visual interpretation at the sub-basin level. As a result, the mean erosion at the basin was estimated to be 4.08 t ha−1 yr−1 in pre-fire conditions and 4.06 t ha−1 yr−1 in post-fire conditions. The decrease of 0.44% is due to the high autonomous vegetation recovery capacity of grassland in the first post-fire year. Extreme values increased by a factor of 4 in post-fire conditions, indicating the importance of post-fire erosion measures such as SWBE in vulnerable areas. The correct spatial location of highly erosive areas detected by the RUSLE was successfully verified by the UAV data. This confirms the effectivity of combining the RUSLE with very-high-resolution data in identifying areas of high erosion, suggesting potential scalability to other fire-prone regions. Full article
(This article belongs to the Special Issue Remote Sensing of Wildfire: Regime Change and Disaster Response)
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Figure 1

Figure 1
<p>Soil and water bioengineering post-fire measures for erosion mitigation at a wildfire site in Italy: (<b>a</b>) contour-felled logs and (<b>b</b>) pile wall with deposited sediment after first rainfall events.</p>
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<p>(<b>a</b>) Visible effects of the wildfire on the vegetation at the El Saco basin (<b>b</b>) Burned shrub layer one month after the event in 2019, Quilanga/Ecuador.</p>
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<p>Location (red dot) of the El Saco basin (green) and the sub-basin (orange) in Quilanga, Ecuador; Background contour map of elevation and river network derived from DEMs (credit: Marc Souris, IRD). Road network: Google Traffic.</p>
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<p>Workflow of the C-factor development for pre- and post-fire conditions, as well as implementation in the RUSLE.</p>
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<p>RUSLE input parameters at the El Saco basin: (<b>a</b>) rainfall erosivity R, (<b>b</b>) soil erodibility K, (<b>c</b>) slope length and steepness LS, and (<b>d</b>) support practices P.</p>
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<p>RUSLE input parameters at the El Saco basin: (<b>a</b>) rainfall erosivity R, (<b>b</b>) soil erodibility K, (<b>c</b>) slope length and steepness LS, and (<b>d</b>) support practices P.</p>
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<p>NDVI-derived C-factors at different dates in pre- and post-fire conditions at the El Saco basin: S2 Pair 1: (<b>a</b>) 24 October 2018 and (<b>b</b>) 18 November 2019; S2 Pair 2: (<b>c</b>) 17 May 2019 and (<b>d</b>) 11 May 2020; S2 Pair 3: (<b>e</b>) 25 August 2019 and (<b>f</b>) 9 August 2020.</p>
Full article ">Figure 6 Cont.
<p>NDVI-derived C-factors at different dates in pre- and post-fire conditions at the El Saco basin: S2 Pair 1: (<b>a</b>) 24 October 2018 and (<b>b</b>) 18 November 2019; S2 Pair 2: (<b>c</b>) 17 May 2019 and (<b>d</b>) 11 May 2020; S2 Pair 3: (<b>e</b>) 25 August 2019 and (<b>f</b>) 9 August 2020.</p>
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<p>Estimated erosion using the RUSLE with C-factor values derived from different NDVI dates in pre- and post-fire conditions at the El Saco basin: S2 Pair 1: (<b>a</b>) 24 October 2018 and (<b>b</b>) 18 November 2019; S2 Pair 2: (<b>c</b>) 17 May 2019 and (<b>d</b>) 11 May 2020; S2 Pair 3: (<b>e</b>) 25 August 2019 and (<b>f</b>) 9 August 2020; Pair 4 S2-Time-Series: (<b>g</b>) pre-fire mean C and (<b>h</b>) post-fire mean C.</p>
Full article ">Figure 7 Cont.
<p>Estimated erosion using the RUSLE with C-factor values derived from different NDVI dates in pre- and post-fire conditions at the El Saco basin: S2 Pair 1: (<b>a</b>) 24 October 2018 and (<b>b</b>) 18 November 2019; S2 Pair 2: (<b>c</b>) 17 May 2019 and (<b>d</b>) 11 May 2020; S2 Pair 3: (<b>e</b>) 25 August 2019 and (<b>f</b>) 9 August 2020; Pair 4 S2-Time-Series: (<b>g</b>) pre-fire mean C and (<b>h</b>) post-fire mean C.</p>
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<p>Estimated erosion using the RUSLE with C factor values derived from different NDVI dates at a confidence interval of 95%: (<b>a</b>) pre-fire conditions and (<b>b</b>) post-fire conditions, as well as the associated frequency distribution (<b>c</b>,<b>d</b>).</p>
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<p>Estimated erosion at the sub-basin above 6 t ha<sup>−1</sup> yr<sup>−1</sup>: (<b>a</b>) pre-fire conditions and (<b>b</b>) post-fire conditions, as well as the associated frequency distribution (<b>c</b>,<b>d</b>).</p>
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<p>Optical erosion detection using high-resolution RGB images from the UAV survey: (<b>a</b>) image of the El Saco sub-basin in October 2021; (<b>b</b>) detail of the 3D model based on UAV survey: Detected erosion at the lower area.</p>
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<p>Comparison of highly erosive areas at the sub-basin with high-resolution UAV data: (<b>a</b>) outlet: pre-fire; (<b>b</b>) outlet: post-fire; (<b>c</b>) lower area: pre-fire; (<b>d</b>) lower area: post-fire; (<b>e</b>) higher area: pre-fire; (<b>f</b>) higher area: post-fire.</p>
Full article ">Figure 11 Cont.
<p>Comparison of highly erosive areas at the sub-basin with high-resolution UAV data: (<b>a</b>) outlet: pre-fire; (<b>b</b>) outlet: post-fire; (<b>c</b>) lower area: pre-fire; (<b>d</b>) lower area: post-fire; (<b>e</b>) higher area: pre-fire; (<b>f</b>) higher area: post-fire.</p>
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<p>S2-derived NDVIs at different dates in pre- and post-fire conditions at the El Saco basin: S2 Pair 1: (<b>a</b>) 24 October 2018 and (<b>b</b>) 18 November 2019; S2 Pair 2 (<b>c</b>) 17 May 2019 and (<b>d</b>) 11 May 2020; S2 Pair 3 (<b>e</b>) 25 August 2019 and (<b>f</b>) 9 August 2020.</p>
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20 pages, 27493 KiB  
Article
Development and Application of an Environmental Vulnerability Index (EVI) for Identifying Priority Restoration Areas in the São Francisco River Basin, Brazil
by Clívia Dias Coelho, Demetrius David da Silva, Ricardo Santos Silva Amorim, Bruno Nery Fernandes Vasconcelos, Ernani Lopes Possato, Elpídio Inácio Fernandes Filho, Pedro Christo Brandão, José Ambrósio Ferreira Neto and Lucas Vieira Silva
Land 2024, 13(9), 1475; https://doi.org/10.3390/land13091475 - 12 Sep 2024
Viewed by 210
Abstract
The environmental vulnerability diagnosis of a river basin depends on a holistic analysis of its environmental aspects and degradation factors. Based on this diagnosis, the definition of priority areas where interventions for environmental recovery should be carried out is fundamental, since financial and [...] Read more.
The environmental vulnerability diagnosis of a river basin depends on a holistic analysis of its environmental aspects and degradation factors. Based on this diagnosis, the definition of priority areas where interventions for environmental recovery should be carried out is fundamental, since financial and natural resources are limited. In this study, we developed a methodology to assess these fragilities using an environmental vulnerability index (EVI) that combines physical and environmental indicators related to the natural sensitivity of ecosystems and their exposure to anthropogenic factors. The developed EVI was applied to the headwater region of the São Francisco River Basin (SFRB), Brazil. The proposed index was based on the AHP multicriteria analysis and was adapted to include four variables representative of the study area: Land Use Adequacy, Burned Area, Erosion Susceptibility, and quantitative water balance. The EVI analysis highlighted that the presence of easily erodible soils, associated with sloping areas and land use above their capacity, generate the most vulnerable areas in the headwaters of the SFRB. The highest EVI values are primarily linked to regions with shallow, easily erodible soils like Leptosols and Cambisols, found in steep areas predominantly used for pasture. In the SFBR, the greatest vulnerability was observed within a 5 km buffer around conservation units, covering approximately 32.4% of the total area. The results of this study indicate where resources should be applied for environmental preservation in the basin under study, directing the allocation of efforts to areas with lower resilience to maintain ecosystem services. Full article
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Figure 1

Figure 1
<p>Study area: selected sub-basins in the headwaters of the São Francisco River Basin.</p>
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<p>Steps to create the environmental vulnerability index (EVI).</p>
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<p>(<b>a</b>) Land use capability and (<b>b</b>) land use intensity in the headwaters of São Francisco.</p>
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<p>Number of exceeding classes in the headwaters of the São Francisco River Basin.</p>
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<p>Recurrence of fires in the headwaters of the São Francisco River Basin.</p>
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<p>(<b>a</b>) Rainfall erosivity, (<b>b</b>) soil erodibility, and (<b>c</b>) slope of the headwaters of the São Francisco River Basin.</p>
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<p>Erosion susceptibility in the headwaters of the São Francisco River Basin.</p>
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<p>Quantitative water balance in the headwaters of the São Francisco River Basin.</p>
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<p>Environmental Vulnerability Index (EVI) in the headwaters of the São Francisco River Basin.</p>
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23 pages, 6157 KiB  
Article
Stomatal and Non-Stomatal Leaf Responses during Two Sequential Water Stress Cycles in Young Coffea canephora Plants
by Danilo F. Baroni, Guilherme A. R. de Souza, Wallace de P. Bernado, Anne R. Santos, Larissa C. de S. Barcellos, Letícia F. T. Barcelos, Laísa Z. Correia, Claudio M. de Almeida, Abraão C. Verdin Filho, Weverton P. Rodrigues, José C. Ramalho, Miroslava Rakočević and Eliemar Campostrini
Stresses 2024, 4(3), 575-597; https://doi.org/10.3390/stresses4030037 - 9 Sep 2024
Viewed by 440
Abstract
Understanding the dynamics of physiological changes involved in the acclimation responses of plants after their exposure to repeated cycles of water stress is crucial to selecting resilient genotypes for regions with recurrent drought episodes. Under such background, we tried to respond to questions [...] Read more.
Understanding the dynamics of physiological changes involved in the acclimation responses of plants after their exposure to repeated cycles of water stress is crucial to selecting resilient genotypes for regions with recurrent drought episodes. Under such background, we tried to respond to questions as: (1) Are there differences in the stomatal-related and non-stomatal responses during water stress cycles in different clones of Coffea canephora Pierre ex A. Froehner? (2) Do these C. canephora clones show a different response in each of the two sequential water stress events? (3) Is one previous drought stress event sufficient to induce a kind of “memory” in C. canephora? Seven-month-old plants of two clones (’3V’ and ‘A1’, previously characterized as deeper and lesser deep root growth, respectively) were maintained well-watered (WW) or fully withholding the irrigation, inducing soil water stress (WS) until the soil matric water potential (Ψmsoil) reached ≅ −0.5 MPa (−500 kPa) at a soil depth of 500 mm. Two sequential drought events (drought-1 and drought-2) attained this Ψmsoil after 19 days and were followed by soil rewatering until a complete recovery of leaf net CO2 assimilation rate (Anet) during the recovery-1 and recovery-2 events. The leaf gas exchange, chlorophyll a fluorescence, and leaf reflectance parameters were measured in six-day frequency, while the leaf anatomy was examined only at the end of the second drought cycle. In both drought events, the WS plants showed reduction in stomatal conductance and leaf transpiration. The reduction in internal CO2 diffusion was observed in the second drought cycle, expressed by increased thickness of spongy parenchyma in both clones. Those stomatal and anatomical traits impacted decreasing the Anet in both drought events. The ‘3V’ was less influenced by water stress than the ‘A1’ genotype in Anet, effective quantum yield in PSII photochemistry, photochemical quenching, linear electron transport rate, and photochemical reflectance index during the drought-1, but during the drought-2 event such an advantage disappeared. Such physiological genotype differences were supported by the medium xylem vessel area diminished only in ‘3V’ under WS. In both drought cycles, the recovery of all observed stomatal and non-stomatal responses was usually complete after 12 days of rewatering. The absence of photochemical impacts, namely in the maximum quantum yield of primary photochemical reactions, photosynthetic performance index, and density of reaction centers capable of QA reduction during the drought-2 event, might result from an acclimation response of the clones to WS. In the second drought cycle, the plants showed some improved responses to stress, suggesting “memory” effects as drought acclimation at a recurrent drought. Full article
(This article belongs to the Topic Plant Responses to Environmental Stress)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Soil matric water potential (Ψ<sub>msoil</sub>) at 100 mm cm and 500 mm from the soil surface in the pots of the <span class="html-italic">C. canephora</span> var. Robusta genotypes of (<b>A</b>) ‘3V’ and (<b>B</b>) ‘A1’ under well-watered (WW) and water stressed (WS) conditions. The water restriction was imposed during the drought-1 and drought-2 events, after which the soil was rewatered (and recovery-1 and recovery-2 events).</p>
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<p>Leaf gas exchanges of two genotypes (Gen) of <span class="html-italic">C. canephora</span> var. Robusta (‘3V’ and ‘A1’) grown under two water availability conditions [Wat, well-watered (WW) and water stress (WS)], over 12 time-points of six-day intervals (Day) during drought-1 and drought-2 and respective recovery events: (<b>A</b>) net CO<sub>2</sub> assimilation rate (<span class="html-italic">A</span><sub>net</sub>), (<b>B</b>) stomatal conductance to water (<span class="html-italic">g</span><sub>s</sub>), (<b>C</b>) transpiration rate (<span class="html-italic">E</span>), and (<b>D</b>) leaf-to-air vapor pressure deficit (VPD<sub>leaf-air</sub>). Inside the figures, the different lowercase letters indicate the significant difference among the time-points for each water regime (blue for WW and olive green for WS); different uppercase letters indicate the comparison between water availabilities for each time-point of observation (blue for WW and olive green for WS); and different superscript black ■ signs indicate that ‘3V’ was statistically superior to ‘A1’ at that time-point. Mean ± SE and ANOVA <span class="html-italic">p</span>-values (n = 7) for effects of three factors (water availability, genotype, and day of observation) and their interactions are shown. The significant <span class="html-italic">p</span>-values were marked in bold in the upper part of each graph.</p>
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<p>Instantaneous water-use efficiency (WUE, <span class="html-italic">A</span><sub>net</sub>/<span class="html-italic">E</span>) of two genotypes (Gen) of <span class="html-italic">C. canephora</span> var. Robusta (‘3V’ and ‘A1’) grown under two water availability conditions [Wat, well-watered (WW) and water stress (WS)], over 12 time-points of six-day intervals (Day) during drought-1 and drought-2 and respective recovery events. Inside the figure, different lowercase letters indicate the significant difference among the day-time points for each water regime (blue for WW and olive green for WS); different uppercase letters indicate the comparison between water availabilities for each time-point of observation (blue for WW and olive green for WS). Mean ± SE and ANOVA <span class="html-italic">p</span>-values (n = 7) for effects of three factors (water availability, genotype, and day of observation) and their interactions are shown. The significant <span class="html-italic">p</span>-values were marked in bold in the upper part of each graph.</p>
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<p>Variation of OJIP indexes of two genotypes (Gen) of <span class="html-italic">C. canephora</span> var. Robusta (‘3V’ and ‘A1’) grown under two water availability conditions [Wat, well-watered (WW) and water stress (WS)] over 12 time-points of six-day intervals (Day) during drought-1 and drought-2 and respective recovery events: (<b>A</b>) maximum quantum yield of primary photochemical reactions (ΦP<sub>0</sub>), (<b>B</b>) probability of electron transfer from Q<sub>A</sub>-to-electron transport chain beyond Q<sub>A</sub> (ΨE<sub>0</sub>), (<b>C</b>) photosynthetic performance index (PI<sub>ABS</sub>), and (<b>D</b>) density of reaction centers capable of Q<sub>A</sub> reduction (RC/CS<sub>0</sub>). Inside the figures, the different lowercase letters indicate the significant difference among the time-points for each water regime (blue for WW and olive green for WS); different uppercase letters indicate the comparison between water availabilities for each day of observation (blue for WW and olive green for WS); superscript black ■ signs indicate that ‘3V’ was statistically superior to ‘A1’, while superscript black ● signs indicate that ‘A1’ clone was statistically superior to ‘3V’ clone at that time-point. Mean ± SE and ANOVA <span class="html-italic">p</span>-values (n = 7) for effects of three factors (water availability, genotype, and day of observation) and their interactions are shown. The significant <span class="html-italic">p</span>-values were marked in bold.</p>
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<p>Variation of modulated chlorophyll <span class="html-italic">a</span> fluorescence indexes of two genotypes (Gen) of <span class="html-italic">C. canephora</span> var. Robusta (‘3V’ and ‘A1’) grown under two water availability conditions [Wat, well-watered (WW) and water stress (WS)] over 12 time-points of six-day intervals (Day) during drought-1 and drought-2 and respective recovery events: (<b>A</b>) effective quantum yield in PSII photochemistry (Φ<sub>PSII</sub>), (<b>B</b>) photochemical quenching (qP), (<b>C</b>) non-photochemical quenching (NPQ), and (<b>D</b>) linear electron transport rate (ETR). Inside the figures, the different lowercase letters indicate the significant difference among the time-points for each water regime (blue for WW and olive green for WS); different uppercase letters indicate the comparison between water availabilities for each day of observation (blue for WW and olive green for WS); different superscript black ■ signs indicate that ‘3V’ was statistically superior to ‘A1’, while superscript black ● signs indicate that ‘A1’ clone was statistically superior to ‘3V’ clone at that time-point. Mean ± SE and ANOVA <span class="html-italic">P</span>-values (n = 7) for effects of three factors (water availability, genotype, and day of observation) and their interactions are shown. The significant <span class="html-italic">P</span>-values were marked in bold.</p>
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<p>Variation of spectral reflectance indices of leaf adaxial surface of two genotypes (Gen) of C. canephora var. Robusta (‘3V’ and ‘A1’) grown under two water availability conditions [Wat, well-watered (WW) and water stress (WS)] over 12 time-points of six-day intervals (Day) during drought-1 and drought-2 and respective recovery events: (<b>A</b>) green chlorophyll index (GCI), (<b>B</b>) carotenoid reflectance index (CRI), (<b>C</b>) photochemical reflectance index (PRI), and (<b>D</b>) structure intensive reflectance index (SIPI). Inside the figures, the different lowercase letters indicate the significant difference among the time-points for each water regime (blue for WW and olive green for WS); different uppercase letters indicate the comparison between water availabilities for each day of observation (blue for WW and olive green for WS); different superscript black ■ signs indicate that ‘3V’ was statistically superior to ‘A1’, while superscript black ● signs indicate that ‘A1’ clone was statistically superior to ‘3V’ clone at that time-point. Mean ± SE and ANOVA <span class="html-italic">p</span>-values (n = 7) for effects of three factors (water availability, genotype, and day of observation) and their interactions are shown. The significant <span class="html-italic">p</span>-values were marked in bold.</p>
Full article ">Figure 7
<p>Representative area of leaf xylem vessel (µm<sup>2</sup>) measured in <span class="html-italic">C. canephora</span> var. Robusta clones (‘3V’ and ‘A1’) under well-watered (WW) and water stress (WS) conditions: (<b>A</b>) A1-WW, (<b>B</b>) 3V-WW, (<b>C</b>) A1-WS, and (<b>D</b>) 3V-WS, evaluated at the end of the second drought cycle. A scale of 100 µm is shown.</p>
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<p>Diagram of the two drought cycles. Transplant followed by drought-1 event last for 19 days (until −500 kPa of Ψ<sub>msoil</sub> was reached), followed by a 31-day period for a whole plant recovery (including 12-day period of recovery-1 event). The 2nd drought cycle was then applied, similarly to the 1st drought cycle, by withholding irrigation until the −500 kPa of Ψ<sub>msoil</sub> was reached (drought-2 event) and followed by another 12 days of recovery-2 event.</p>
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21 pages, 3481 KiB  
Article
Does Nitrogen Fertilization Improve Nitrogen-Use Efficiency in Spring Wheat?
by Aixia Xu, Yafei Chen, Xuexue Wei, Zechariah Effah, Lingling Li, Junhong Xie, Chang Liu and Sumera Anwar
Agronomy 2024, 14(9), 2049; https://doi.org/10.3390/agronomy14092049 - 7 Sep 2024
Viewed by 362
Abstract
To investigate the effects and mechanism of prolonged inorganic nitrogen (N) fertilization on the N-use efficiency of spring wheat (Triticum aestivum L.), a long-term study initiated in 2003 was conducted. The study analyzed how N fertilization affects dry matter translocation, N translocation, [...] Read more.
To investigate the effects and mechanism of prolonged inorganic nitrogen (N) fertilization on the N-use efficiency of spring wheat (Triticum aestivum L.), a long-term study initiated in 2003 was conducted. The study analyzed how N fertilization affects dry matter translocation, N translocation, soil NO3-N, and N-use efficiency. Five different N-fertilizer rate treatments were tested: N0, N52.5, N105, N157.5, and N210, corresponding to annual N fertilizer doses of 0, 52.5, 105.0, 157.5, and 210.0 kg N ha−1, respectively. Results showed that increasing N-fertilizer rates significantly enhanced the two-year average dry matter accumulation amount (DMA) at maturity by 22.97–56.25% and pre-flowering crop growth rate (CGR) by 17.11–92.85%, with no significant increase beyond 105 kg N ha−1. However, no significant correlation was observed between the dry matter translocation efficiency (DTE) and wheat grain yield. Both insufficient and excessive N applications resulted in an imbalanced N distribution favoring vegetative growth over reproductive growth, thus negatively impacting N-use efficiency. At maturity, the N-fertilized treatments significantly increased the two-year average N accumulation amount (NAA) by 52.04–129.98%, with no further increase beyond 105 kg N ha−1. N fertilization also improved the two-year average N translocation efficiency (NTE) by 56.89–63.80% and the N contribution proportion (NCP) of wheat vegetative organs by 27.79–57.83%, peaking in the lower-N treatment (N52.5). However, high-N treatment (N210) led to an increase in NO3-N accumulation in the 0–100 cm soil layer, with an increase of 26.27% in 2018 and 122.44% in 2019. This higher soil NO3-N accumulation in the 0–100 cm layer decreased NHI, NUE, NAE, NPFP, and NMB. Additionally, N fertilization significantly reduced the two-year average N harvest index (NHI) by 9.89–12.85% and N utilization efficiency (NUE) by 11.14–20.79%, both decreasing with higher N application rates. The NAA followed the trend of anthesis > maturity > jointing. At the 105 kg N ha−1 rate, the highest N agronomic efficiency (NAE) (9.31 kg kg−1), N recovery efficiency (NRE) (38.32%), and N marginal benefit (NMB) (10.67 kg kg−1) were observed. Higher dry matter translocation amount (DTA) and N translocation amount (NTA) reduced NHI and NUE, whereas higher NTE improved NHI, NUE, and N partial factor productivity (NPFP). Overall, N fertilization enhanced N-use efficiency in spring wheat by improving N translocation rather than dry matter translocation. Full article
(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)
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<p>Monthly rainfall and temperature for the experimental years.</p>
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<p>The dry matter translocation amount (DTA) in total aboveground (<b>A</b>) and different tissues (<b>D</b>), dry matter translocation efficiency (DTE) of total aboveground (<b>B</b>) and different tissues (<b>E</b>), and dry matter contribution proportion (DCP) of total aboveground (<b>C</b>) and different tissues (<b>F</b>) of wheat according to N fertilizer supply (N0, N52.5, N105, N157.5, and N210 represent annual N-fertilizer rates of 0, 52.5, 105.0, 157.5, and 210.0 kg N ha<sup>−1</sup>, respectively). Vertical bars represent standard errors, and columns with different letters indicate statistically significant differences according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Nitrogen (N) accumulation (mg stem<sup>−1</sup>) in various wheat tissues according to N fertilizer supply (N0, N52.5, N105, N157.5, and N210 represent annual N-fertilizer rate of 0, 52.5, 105.0, 157.5, and 210.0 kg N ha<sup>−1</sup>, respectively) at (<b>A</b>) anthesis in 2018, (<b>B</b>) anthesis in 2019, (<b>C</b>) anthesis as a two-year average, (<b>D</b>) maturity in 2018, (<b>E</b>) maturity in 2019, and (<b>F</b>) maturity as a two-year average. Vertical bars represent standard errors, and columns with different letters indicate statistically significant differences according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Nitrogen translocation and contribution to grain with different nitrogen (N) treatments (mg stem<sup>−1</sup>). The N translocation amount of wheat vegetative organs (<b>A</b>) and various tissues (<b>D</b>), N translocation efficiency of vegetative organs (<b>B</b>) and various tissues (<b>E</b>), and N contribution proportion of wheat vegetative organs (<b>C</b>) and different tissues (<b>F</b>) according to N fertilizer supply (N0, N52.5, N105, N157.5, and N210 represent annual N-fertilizer rates of 0, 52.5, 105.0, 157.5, and 210.0 kg N ha<sup>−1</sup>, respectively). Vertical bars represent standard errors, and columns with different letters indicate statistically significant differences according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>NO<sub>3</sub>–N accumulation (kg ha<sup>−1</sup>) in the 0–100 cm soil layers at maturity of wheat in 2018 (<b>A</b>) and 2019 (<b>B</b>) (N0, N52.5, N105, N157.5, and N210 represent annual N-fertilizer rates of 0, 52.5, 105.0, 157.5, and 210.0 kg N ha<sup>−1</sup>, respectively). Vertical bars represent standard errors, and columns with different letters indicate statistically significant differences according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>(<b>A</b>) Nitrogen (N) harvest index (NHI), (<b>B</b>) N utilization efficiency (NUE, kg kg<sup>−1</sup>), (<b>C</b>) N agronomic efficiency (NAE, kg kg<sup>−1</sup>), (<b>D</b>) N recovery efficiency (NRE, %), (<b>E</b>) N partial factor productivity (NPFP, kg kg<sup>−1</sup>), and (<b>F</b>) N marginal benefit (NMB, kg kg<sup>−1</sup>) according to N fertilizer supply (N0, N52.5, N105, N157.5, and N210 represent annual N-fertilizer rates of 0, 52.5, 105.0, 157.5, and 210.0 kg N ha<sup>−1</sup>, respectively). Vertical bars represent standard errors, and columns with different letters indicate statistically significant differences according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Correlation coefficients among nitrogen (N) harvest index (NHI), N utilization efficiency (NUE, kg kg<sup>−1</sup>), N agronomic efficiency (NAE, kg kg<sup>−1</sup>), N recovery efficiency (NRE, %), N partial factor productivity (NPFP, kg kg<sup>−1</sup>), N marginal benefit (NMB, kg kg<sup>−1</sup>), dry matter translocation amount (DTA, mg stem<sup>−1</sup>), dry matter translocation efficiency (DTE, %), N translocation amount (NTA, kg ha<sup>−1</sup>), and N translocation efficiency (NTE, %) across N fertilizer treatments. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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21 pages, 10380 KiB  
Article
Study on the Characteristics of Residual Film–Soil–Root Stubble Complex in Maize Stubble Fields of the Hexi Corridor and Establishment of a Discrete Element Model
by Xiaolong Liu, Ruijie Shi, Wuyun Zhao, Wei Sun, Peiwen Li, Hui Li, Hua Zhang, Jiuxin Wang, Guanping Wang and Fei Dai
Agriculture 2024, 14(9), 1542; https://doi.org/10.3390/agriculture14091542 - 6 Sep 2024
Viewed by 309
Abstract
Plastic film mulching is one of the key technologies for improving agricultural productivity in arid and semi-arid regions. However, residual plastic film can severely disrupt the structure of the topsoil in farmland, leading to a decrease in crop yield. The Hexi Corridor, as [...] Read more.
Plastic film mulching is one of the key technologies for improving agricultural productivity in arid and semi-arid regions. However, residual plastic film can severely disrupt the structure of the topsoil in farmland, leading to a decrease in crop yield. The Hexi Corridor, as the largest seed maize production base in the arid regions of Northwest China, is facing an increasingly prominent issue of residual plastic film recovery. This study designed experiments based on the typical maize planting model in the Hexi Corridor. A discrete element simulation model of the residual film–soil–root stubble complex was established using the Bonding-V2 model and API rapid filling technology. The reliability of the simulation model was verified through shear and puncture tests. The study revealed that the soil type in the Hexi Corridor is heavy sandy soil. The differences between the average maximum shear forces in the simulated and actual shear tests for root stubble–soil complexes at depths of 30 mm, 50 mm, and 100 mm were 4.8%, 6.4%, and 6.5%, respectively. Additionally, the differences in the average maximum vertical loading forces in the simulated and actual puncture tests for root stubble–soil complexes at depths of 50 mm and 100 mm were 6.4% and 12.37%, respectively. The small discrepancies between the simulated and actual values, along with the consistency of particle movement trends with real-world conditions, confirmed the reliability and accuracy of the simulation model. This indicates that the established discrete element flexible model can effectively represent actual field conditions, providing discrete element model parameters and theoretical support for optimizing the design of key components in China’s mechanized root stubble handling and residual film recovery machinery. Full article
(This article belongs to the Section Agricultural Soils)
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<p>Typical planting pattern of maize in Hexi Irrigation District.</p>
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<p>Termination of soil sample properties.</p>
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<p>Measurement of soil moisture content by drying method.</p>
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<p>Ring knife sampling and soil particle density determination.</p>
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<p>Measurement and distribution of soil compaction in the Hexi Irrigation District. (<b>a</b>) Measurement of soil compaction in the Hexi Irrigation District. (<b>b</b>) Distribution pattern of soil compaction in the Hexi Irrigation District. In the figure, A is the soil firmness test area near the stubble, B is the soil firmness test area in the middle of two adjacent stubble, and C is the soil firmness test area near the stubble.</p>
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<p>Soil shear principle.</p>
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<p>Soil shear test device. 1. Frame; 2. Speed control circuit module; 3. Control circuit board; 4. Stepper motor; 5. Lower shear box; 6. Upper shear box; 7. Tension pressure sensor; 8. Leveling device; 9. Handheld instrument; 10. Computer; 11. Weight; 12. Scale plate.</p>
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<p>Piercing test apparatus for root stubble–soil composite. 1. Metal bucket; 2. Soil; 3. Maize root stubble; 4. Instrument; 5. Steel needle; 6. Steel ruler.</p>
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<p>Three-dimensional model of maize root stubble and the discrete element model. 1. Primary root; 2. Secondary root. Note: <span class="html-italic">L</span><sub>1</sub> represents the length of the primary root, mm; <span class="html-italic">L</span><sub>2</sub> represents the stubble height, mm; <span class="html-italic">L</span><sub>3</sub> represents the depth of primary root penetration, mm; <span class="html-italic">D</span><sub>1</sub> represents the diameter of the maize stalk, mm; <span class="html-italic">D</span><sub>2</sub> represents the diameter of the primary root, mm; <span class="html-italic">D</span><sub>3</sub> represents the range of primary root growth diameter when maize stubble is buried 60 mm deep, mm. A is the local enlarged map of discrete element model of maize root stubble.</p>
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<p>Soil three-dimensional model and discrete element model. 1. Soil; 2. Space occupied by maize root stubble. Note: <span class="html-italic">L</span><sub>4</sub> is the soil model length, mm; <span class="html-italic">W</span><sub>1</sub> is the width of soil model, mm; <span class="html-italic">H</span><sub>1</sub> is the height of soil model, mm. B is the local enlarged map of soil discrete element model.</p>
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<p>Three-dimensional model and the discrete element model of the residual film. 1. Residual film 2. Space occupied by maize root stubble. Note: <span class="html-italic">L</span><sub>5</sub> is the residual film model length, mm; <span class="html-italic">W</span><sub>2</sub> is the residual film model width, mm. C is the local enlarged image of discrete element model of residual film.</p>
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<p>Discrete element model of the root stubble–soil–residual film-mulch lifting and pressing shovel. 1. Mulch lifting and pressing shovel; 2. Maize stubble; 3. Residual film; 4. Soil.</p>
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<p>Discrete element shear simulation model of the root stubble–soil composite at different depths. 1. Lower shear box; 2. Upper shear box; 3. Root stubble; 4. Pressure plate; 5. Soil.</p>
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<p>Discrete element piercing simulation model of the root stubble–soil composite. 1. Soil; 2. Maize root stubble; 3. Steel needle.</p>
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<p>Simulation process of the 50 mm depth shear test for the root stubble–soil composite.</p>
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<p>Changes of root stubble, soil, and stress after 50 mm depth shear simulation of the root stubble–soil complex.</p>
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<p>Velocity contour plot of particles during the 50 mm depth shear simulation test for the root stubble–soil composite.</p>
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<p>Angular velocity contour plot of particles during the 50 mm depth shear simulation test for the root stubble–soil composite.</p>
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<p>Horizontal shear force variation curves for the root stubble–soil complex at different depths.</p>
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<p>Simulation process of 100 mm depth puncture test for the root stubble–soil complex.</p>
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<p>Loading force variation curves for the root stubble–soil complex at 50 mm depth.</p>
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15 pages, 2384 KiB  
Article
Soil-Sensitive Weibull Distribution Models of Larix principis-rupprechtii Plantations across Northern China
by Hong Guo, Xianzhao Liu and Dan Liu
Forests 2024, 15(9), 1562; https://doi.org/10.3390/f15091562 - 5 Sep 2024
Viewed by 277
Abstract
Tree diameter distribution models are important tools for forest management decision making. Soil variables affect tree growth and thus diameter distribution. However, few studies have been conducted on diameter distribution models describing the effects of soil. This study developed a soil-sensitive diameter distribution [...] Read more.
Tree diameter distribution models are important tools for forest management decision making. Soil variables affect tree growth and thus diameter distribution. However, few studies have been conducted on diameter distribution models describing the effects of soil. This study developed a soil-sensitive diameter distribution model based on 213 sample plots of Larix principis-rupprechtii plantations in northern China. The Weibull distribution model was modified by a compatible simultaneous system and the percentile method with the inclusion of soil variables. The most significant factors influencing the diameter distribution of L. principis-rupprechtii in terms of both scale and shape were stand characteristics and available K and alkali-hydrolysable N. The adjusted coefficient of determination for parameter γ significantly improved by 16.0%, while the root mean square error for parameter β decreased by 10.4%. The F test indicated a substantial difference between the models with and without soil variables. From the perspective of adjustable R2 values, the Akaike information criterion, root mean square error, relative error index, and absolute error index, the inclusion of stand and soil factors in the tree diameter distribution model enhanced its performance compared to the model that did not consider soil factors. The soil-sensitive diameter distribution model is proven to be effective and accurate. Full article
(This article belongs to the Special Issue Estimation and Monitoring of Forest Biomass and Fuel Load Components)
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<p>Location of the study area and spatial distribution of the sample plots.</p>
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<p>Observations and predictions of parameter β: (<b>a</b>) models without soil variables and (<b>b</b>) soil-sensitive models. The line represents the linear relationship between the predicted and observed values of the β parameter.</p>
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<p>Observations and predictions of parameter β: (<b>a</b>) models without soil variables and (<b>b</b>) soil-sensitive models. The line represents the linear relationship between the predicted and observed values of the β parameter.</p>
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<p>Observations and predictions of parameter γ: (<b>a</b>) models without soil variables and (<b>b</b>) soil-sensitive models. The line represents the linear relationship between the predicted and observed values of the γ parameter.</p>
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<p>Observations and predictions of parameter γ: (<b>a</b>) models without soil variables and (<b>b</b>) soil-sensitive models. The line represents the linear relationship between the predicted and observed values of the γ parameter.</p>
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<p>Observed and fitting diameter distribution in a plot: (<b>a</b>) Beijing, (<b>b</b>) Heibei, (<b>c</b>) Inner Mongolia, and (<b>d</b>) Shanxi.</p>
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19 pages, 5746 KiB  
Article
Dual-Wavelength LiDAR with a Single-Pixel Detector Based on the Time-Stretched Method
by Simin Chen, Shaojing Song, Yicheng Wang, Hao Pan, Fashuai Li and Yuwei Chen
Sensors 2024, 24(17), 5741; https://doi.org/10.3390/s24175741 - 4 Sep 2024
Viewed by 265
Abstract
In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture [...] Read more.
In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system’s performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management. Full article
(This article belongs to the Section Radar Sensors)
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<p>Diagram of the dual-wavelength multi-spectral LiDAR system architecture.</p>
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<p>Physical image of the supercontinuum laser.</p>
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<p>(<b>a</b>) Physical image of APD 210. (<b>b</b>) Spectral response curve of APD 210.</p>
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<p>Dual-wavelength LiDAR demonstration instrument in the laboratory test.</p>
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<p>(<b>a</b>) The intensity of leaf collected by the system at distances of 10 m, 20 m, 30 m, 40 m, and 50 m; (<b>b</b>) is the corresponding reflectance calibrated by a standard SRB.</p>
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<p>The intensity of SRB collected by the system at distances of 10 m, 20 m, 30 m, 40 m, and 50 m.</p>
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<p>Photos of green leaves, dry leaves, diseased leaves, branches, and soil were selected in the experiment.</p>
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<p>Echo waveform of (<b>a</b>) green leaves, (<b>b</b>) dry leaves, (<b>c</b>) yellow branches, and (<b>d</b>) soil.</p>
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<p>Echo waveform of (<b>a</b>) green leaves, (<b>b</b>) dry leaves, (<b>c</b>) yellow branches, and (<b>d</b>) soil.</p>
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<p>(<b>a</b>) Reflectance at 800 nm (blue) and 600 nm (red) as well as (<b>b</b>) NDVI of green leaf, ill leaf (the unhealthy part), ill leaf (the healthy part), dry leaf, green branch, yellow branch, and soil.</p>
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<p>(<b>a</b>) Photo of Red Sumach; (<b>b</b>) 600 nm echo point cloud of Red Sumach at 10 m; (<b>c</b>) 800 nm echo point cloud of Red Sumach at 10 m.</p>
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<p>NDVI point cloud map of Red Sumach.</p>
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18 pages, 2096 KiB  
Article
Sulfate Nutrition Modulates the Oxidative Response against Short-Term Al3+-Toxicity Stress in Lolium perenne cv. Jumbo Shoot Tissues
by Hernan Vera-Villalobos, Lizzeth Lunario-Delgado, Anita S. Gálvez, Domingo Román-Silva, Ana Mercado-Seguel and Cristián Wulff-Zottele
Agriculture 2024, 14(9), 1506; https://doi.org/10.3390/agriculture14091506 - 2 Sep 2024
Viewed by 376
Abstract
Al3+-toxicity in acidic soils is among the main abiotic stress factors that generate adverse effects in plant growth; in leaves, it affects several physiological parameters such as photosynthesis and ROS balance, leading to limited crop production. On the other hand, sulfur [...] Read more.
Al3+-toxicity in acidic soils is among the main abiotic stress factors that generate adverse effects in plant growth; in leaves, it affects several physiological parameters such as photosynthesis and ROS balance, leading to limited crop production. On the other hand, sulfur is a macronutrient that has a key role against oxidative stress and improves plant growth in acidic soils; however, the implication of sulfate nutritional status in the modulation of short-term Al3+-toxicity tolerance mechanisms in plant leaves are barely reported. This study is focused on the role of sulfate on the leaf response of an Al3-sensitive perennial ryegrass (Lolium perenne cv. Jumbo) after 48 h of exposure. Lolium perenne cv. Jumbo seeds were cultivated in hydroponic conditions with modified Taylor Foy solutions supplemented with 120, 240, and 360 μM sulfate in the presence or absence of Al3+-toxicity. The L. perenne cv. Jumbo leaves were collected after 48 h of Al3+-toxicity exposure and processed to evaluate the effects of sulfate on Al3+ toxicity, measuring total proteins, mineral uptake, photosynthesis modulation, and ROS defense mechanism activation. The plants exposed to Al3+-toxicity and cultivated with a 240 µM sulfate amendment showed a recovery of total proteins and Ca2+ and Mg2+ concentration levels and a reduction in TBARS, along with no changes in the chlorophyll A/B ratio, gene expression of proteins related to photosynthesis (Rubisco, ChlAbp, and Fered), or ROS defense mechanism (SOD, APX, GR, and CAT) as compared with their respective controls and the other sulfate conditions (120 and 360 µM). The present study demonstrates that adequate sulfate amendments have a key role in regulating the physiological response against the stress caused by Al3+ toxicity. Full article
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<p>Total protein, Ca, Mg, and TBARS concentrations in leaves of perennial ryegrass shoots cultured at increased sulfate supply after 48 h with or without Al<sup>3+</sup>-toxicity. Total protein (<b>A</b>), calcium (<b>B</b>), magnesium (<b>C</b>) and TBARS (<b>D</b>) in control (black bars) or Al<sup>3+</sup>-treated (gray bars) <span class="html-italic">L. perenne</span> grown in the respective sulfate amendments (120, 240, 360 µM). Two-way ANOVA and Sidak’s test (<span class="html-italic">p</span> values: * &lt;0.05; ** &lt;0.01; and **** &lt;0.001) provided evidence of significant differences.</p>
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<p>Sulfate nutrition modulates photosynthetic pigment amounts in shoots of <span class="html-italic">L. perenne cv. Jumbo</span> after 48 h of Al<sup>3+</sup>-toxicity exposure. Total chlorophyll (<b>A</b>), carotenoids (<b>B</b>), and chlorophyll A/B ratio (<b>C</b>) in leaves of <span class="html-italic">L. perenne</span> grown with different sulfate amendments (120, 240 or 360 µM) in absence (black bars) or presence (grey bars) of Al<sup>3+</sup> toxicity. The values reported are means ± SE (n = 3). Two-way ANOVA and Sidak’s test were carried out (<span class="html-italic">p</span> values: ** &lt;0.01; *** &lt;0.005; and **** &lt;0.001). Asterisks show statistical differences between Al and control for every sulfate condition.</p>
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<p>Sulfate-nutrition-modulated redox status of antioxidant metabolites, ascorbate, and glutathione species in shoots of <span class="html-italic">L. perenne cv. Jumbo</span> after 48 h of Al<sup>3+</sup>-toxicity exposure. Total ascorbate [Asc <sub>tot</sub>] (<b>A</b>), total glutathione [GSH <sub>tot</sub>] amounts (<b>B</b>), reduced ascorbate ratio (<b>C</b>) and reduced glutathione ratio (<b>D</b>) in leaves of <span class="html-italic">L. perenne</span> grown in different sulfate amendments (120, 240 or 360 µM) and in absence (black bars) or presence (grey bars) of 48 h Al<sup>3+</sup>-toxicity. TWO-WAY-ANOVA and Sidak’s test were carried out (<span class="html-italic">p</span> values: * &lt;0.05; ** &lt;0.01; *** &lt;0.005 and **** &lt;0.001). Asterisks show statistical differences for every sulfate condition. The value reported are mean ± SE (n = 3).</p>
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<p>Sulfate nutrition recovers enzymatic activities involved in ROS detoxification mechanism when <span class="html-italic">L. perenne cv. Jumbo</span> is exposed to 48 h Al<sup>3+</sup>-toxicity. Superoxide dismutase, SOD (<b>A</b>), ascorbate peroxidase, APX (<b>B</b>), glutathione reductase, GR (<b>C</b>), and catalase (CAT) (<b>D</b>). Leaves of perennial ryegrass in absence (black bars) or presence of Al<sup>3+</sup>-toxicity (grey bars) grown in three different sulfate conditions (120, 240, or 360 µM) were considered. Two-way ANOVA and Sidak’s test were carried out (<span class="html-italic">p</span> values: * &lt;0.05; ** &lt;0.01; *** &lt;0.005; and **** &lt;0.001). Asterisk shows statistical differences between Al and control for every sulfate condition. The values reported are means ± SE (n = 3).</p>
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<p>Representation of sulfate effects against short-term Al<sup>3+</sup>-toxicity in leaves of <span class="html-italic">L. perenne cv. Jumbo</span>. These changes were observed in 240 µM of SO₄<sup>2−</sup> as an adequate sulfate condition.</p>
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19 pages, 2922 KiB  
Article
Ionic Liquids toward Enhanced Carotenoid Extraction from Bacterial Biomass
by Tiago P. Silva, Luís Alves, Francisco Salgado, José C. Roseiro, Rafał M. Łukasik and Susana M. Paixão
Molecules 2024, 29(17), 4132; https://doi.org/10.3390/molecules29174132 - 30 Aug 2024
Viewed by 287
Abstract
Carotenoids are high added-value products primarily known for their intense coloration and high antioxidant activity. They can be extracted from a variety of natural sources, such as plants, animals, microalgae, yeasts, and bacteria. Gordonia alkanivorans strain 1B is a bacterium recognized as a [...] Read more.
Carotenoids are high added-value products primarily known for their intense coloration and high antioxidant activity. They can be extracted from a variety of natural sources, such as plants, animals, microalgae, yeasts, and bacteria. Gordonia alkanivorans strain 1B is a bacterium recognized as a hyper-pigment producer. However, due to its adaptations to its natural habitat, hydrocarbon-contaminated soils, strain 1B is resistant to different organic solvents, making carotenoid extraction through conventional methods more laborious and inefficient. Ionic liquids (ILs) have been abundantly shown to increase carotenoid extraction in plants, microalgae, and yeast; however, there is limited information regarding bacterial carotenoid extraction, especially for the Gordonia genus. Therefore, the main goal of this study was to evaluate the potential of ILs to mediate bacterial carotenoid extraction and develop a method to achieve higher yields with fewer pre-processing steps. In this context, an initial screening was performed with biomass of strain 1B and nineteen different ILs in various conditions, revealing that tributyl(ethyl)phosphonium diethyl phosphate (IL#18), combined with ethyl acetate (EAc) as a co-solvent, presented the highest level of carotenoid extraction. Afterward, to better understand the process and optimize the extraction results, two experimental designs were performed, varying the amounts of IL#18 and EAc used. These allowed the establishment of 50 µL of IL#18 with 1125 µL of EAc, for 400 µL of biomass (cell suspension with about 36 g/L), as the ideal conditions to achieve maximal carotenoid extraction. Compared to the conventional extraction method using DMSO, this novel procedure eliminates the need for biomass drying, reduces extraction temperatures from 50 °C to 22 ± 2 °C, and increases carotenoid extraction by 264%, allowing a near-complete recovery of carotenoids contained in the biomass. These results highlight the great potential of ILs for bacterial carotenoid extraction, increasing the process efficiency, while potentially reducing energy consumption, related costs, and emissions. Full article
(This article belongs to the Special Issue Recent Advances in Ionic Liquids and Their Applications)
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<p>Wet biomass of <span class="html-italic">G. alkanivorans</span> strain 1B, cultivated in a chemostat with fructose as the C-source and sulfate as the S-source, after centrifugation.</p>
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<p>Representation of the screening of the potential of different ionic liquids (IL#1 to IL#6) toward carotenoid extraction coupled with ethyl acetate (phase-forming solvent).</p>
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<p>Total carotenoids (µg/gDCW), measured by spectrophotometry, after extraction with different ILs, with EAc as a co-solvent.</p>
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<p>Response surface for total carotenoids extracted (µg/gDCW) obtained in #ED1, for the factors IL#18 volume (25–1000 µL) and EAc volume (250–2000 µL).</p>
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<p>Response surface for total carotenoids extracted (µg/gDCW) obtained in #ED2, for the factors IL#18 volume (0–50 µL) and EAc volume (250–1125 µL).</p>
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<p>Total carotenoids (µg/gDCW), measured by spectrophotometry, after extraction with 50 µL of IL#18 with 1125 µL of EAc (optimized conditions) and the conventional extraction protocol using DMSO. Discoloration of <span class="html-italic">G. alkanivorans</span> strain 1B biomass after three sequential extractions using: (<b>A</b>) 50 µL of IL#18 with 1125 µL of EAc versus (<b>B</b>) DMSO.</p>
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26 pages, 6469 KiB  
Article
Hazelnut Cultivation in the Campania Region: Environmental Sustainability of the Recovery of Pruning Residues and Shells through the Life Cycle Assessment Methodology
by Maria Pergola, Angela Maffia, Antonietta Picone, Assunta Maria Palese, Gessica Altieri and Giuseppe Celano
Sustainability 2024, 16(17), 7533; https://doi.org/10.3390/su16177533 - 30 Aug 2024
Viewed by 583
Abstract
Promoting sustainable agriculture is one of the challenges of our century. Thus, this research aimed to estimate the environmental sustainability of hazelnut cultivation in the Campania region (Southern Italy), both in quantitative and economic terms, by estimating the social cost of the pollution. [...] Read more.
Promoting sustainable agriculture is one of the challenges of our century. Thus, this research aimed to estimate the environmental sustainability of hazelnut cultivation in the Campania region (Southern Italy), both in quantitative and economic terms, by estimating the social cost of the pollution. The evaluation of the recovery of pruning residues and shells, from a circular economy perspective, represents the novelty of this paper. The lifecycle assessment methodology was used to analyze and compare twenty-one hazelnut systems that are very different from each other. The results showed that the impacts per kg of unshelled hazelnuts varied among the systems, depending on the impact category considered, and with respect to climate change, the lowest value was 0.32 kg CO2 eq (in BIO4 system), while the highest was 2.48 kg CO2 eq (in INT8 system). Moreover, organic management was more environmentally friendly for almost all impact categories, and ordinary cultivation techniques were the most impactful. Cultivation on embankments or terraced soils had a greater impact when compared to flat soils, especially due to the greater fuel consumption during farm–field transport. Emergency irrigation did not cause an increase in impact if the overall management was sustainable. In economic terms, the total cost of pollution of the agricultural phase varied from a minimum of EUR 0.11 per kg of hazelnuts to a maximum of EUR 0.70. Post-harvest operations up to vacuum packaging did not make any systems more impactful than others since their agricultural management was more sustainable than many others. In accordance with the objectives of the European Green Deal, the recovery of pruning material and shells on farms has proven to be very important for reducing impacts, especially if they are used to replace methane and diesel oil, hence the importance of pursuing this research to make hazelnut cultivation ever more sustainable. Full article
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<p>Study area (Avellino, Caserta, and Salerno provinces, Campania region, Southern Italy) and position of the hazelnut orchard systems under study.</p>
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<p>System boundaries: field agronomic operations, harvesting, and transport of the hazelnuts to the farm; selection, cleaning, calibration, drying, shelling, and packaging of the hazelnuts according to <a href="#sustainability-16-07533-t002" class="html-table">Table 2</a>, <a href="#sustainability-16-07533-t003" class="html-table">Table 3</a> and <a href="#sustainability-16-07533-t004" class="html-table">Table 4</a>.</p>
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<p>Cost of pollution per kg of unshelled hazelnut and per hectare (BIO: organic systems; INT: integrated systems; CONV: system managed according to the ordinary cultivation techniques).</p>
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<p>Cost of pollution per impact categories (BIO: organic systems; INT: integrated systems; CONV: system managed according to the ordinary cultivation techniques).</p>
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<p>Contribution of the cultivation operations on the cost of pollution (BIO: organic systems; INT: integrated systems; CONV: system managed according to the ordinary cultivation techniques).</p>
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<p>The focus on climate change. Emission values per both functional units (kg of unshelled hazelnuts and hectare). (BIO: organic systems; INT: integrated systems; CONV: system managed according to the ordinary cultivation techniques).</p>
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<p>Comparison/sustainability classes per kg of unshelled hazelnuts.</p>
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<p>Environmental cost per kg of hazelnuts of the post-harvest operations distinguished among the systems that carried them out (BIO: organic systems; INT: integrated systems; CONV: system managed according to the ordinary cultivation techniques).</p>
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<p>Total environmental cost distinguished by system and by operations carried out according to system boundaries of each analyzed system (BIO: organic systems; INT: integrated systems; CONV: system managed according to the ordinary cultivation techniques).</p>
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<p>Percentage increase in environmental impacts (kg of CO<sub>2</sub> eq) in reference to different alternatives to dispose pruning residues without considering avoided emissions (BIO: organic systems; INT: integrated systems; CONV: system managed according to the ordinary cultivation techniques).</p>
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28 pages, 4944 KiB  
Review
Innovative Techniques for Electrolytic Manganese Residue Utilization: A Review
by Andrews Larbi, Xiping Chen, Suliman Muhammad Khan and Tang Fangheng
Waste 2024, 2(3), 354-381; https://doi.org/10.3390/waste2030020 - 30 Aug 2024
Viewed by 1185
Abstract
Electrolytic Manganese Residue (EMR) is a secondary material generated during the process of manganese production, poses significant environmental challenges, including land consumption and contamination threats to soil and water bodies due to its heavy metal content, soluble manganese, ammonia nitrogen, and disposal issues. [...] Read more.
Electrolytic Manganese Residue (EMR) is a secondary material generated during the process of manganese production, poses significant environmental challenges, including land consumption and contamination threats to soil and water bodies due to its heavy metal content, soluble manganese, ammonia nitrogen, and disposal issues. This review thoroughly examines EMR, emphasizing its metallurgical principles, environmental impacts, and sustainable treatment methods. We critically analyze various approaches for EMR management, including resource recovery, utilization of construction materials, and advanced treatment techniques to mitigate its environmental challenges. Through an extensive review of recent EMR-related literature and case studies, we highlight innovative strategies for EMR valorization, such as the extraction of valuable metals, conversion into supplementary cementitious materials, and its application in environmental remediation. Our findings suggest that integrating metallurgical principles with environmental engineering practices can unlock EMR’s potential as a resource, contributing to the circular economy and reducing the environmental hazards associated with its disposal. This study aims to deepen the understanding of EMR’s comprehensive utilization, offering insights into future research directions and practical applications for achieving sustainable management of electrolytic manganese waste. Finally, we propose some recommendations to address the issue of EMR, intending to offer guidance for the proper disposal and effective exploitation of EMR. Full article
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<p>Illustration depicting the impact of EMR on ecological and animal life.</p>
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<p>Illustrates the EMR’s morphology: (<b>a</b>) XRD patterns and (<b>b</b>) SEM image referred from [<a href="#B31-waste-02-00020" class="html-bibr">31</a>].</p>
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<p>EMR Bibliometric data from SCOPUS: the ten most globally cited documents related to the use of EMR analyzed using Rstudio.</p>
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<p>EMR Bibliometric data from SCOPUS: Proportional distribution of single countries production.</p>
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<p>Structure symmetry ofsolid-liquid heterogeneous mechanism reactions under sulfuric acid leaching kinematics.</p>
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<p>Schematic for the preparation of cement or cement-related products containing EMR: (<b>a</b>) TiO<sub>2</sub>-EMR cement referred from [<a href="#B115-waste-02-00020" class="html-bibr">115</a>] (<b>b</b>) EG<sup>CH</sup> hardened EMR cementitious material, referred from [<a href="#B14-waste-02-00020" class="html-bibr">14</a>] (<b>c</b>) EMR-GBFS cement, referred from [<a href="#B113-waste-02-00020" class="html-bibr">113</a>].</p>
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19 pages, 2692 KiB  
Article
Sustainable Recovery of the Health of Soil with Old Petroleum Hydrocarbon Contamination through Individual and Microorganism-Assisted Phytoremediation with Lotus corniculatus
by Rimas Meištininkas, Irena Vaškevičienė, Agnieszka I. Piotrowicz-Cieślak, Magdalena Krupka and Jūratė Žaltauskaitė
Sustainability 2024, 16(17), 7484; https://doi.org/10.3390/su16177484 - 29 Aug 2024
Viewed by 397
Abstract
Due to the large number of areas contaminated with TPH, there is significant interest in biological remediation technology research, offering a comprehensive and sustainable approach to soil decontamination and health recovery at the same time. This study aimed to investigate the effectiveness of [...] Read more.
Due to the large number of areas contaminated with TPH, there is significant interest in biological remediation technology research, offering a comprehensive and sustainable approach to soil decontamination and health recovery at the same time. This study aimed to investigate the effectiveness of remediating TPH-contaminated soil (6120 mg kg−1) using Lotus corniculatus along with a microorganism consortium (GTC-GVT/2021) isolated from historic TPH-contaminated sites. This study evaluated the removal of TPH and soil health recovery through changes in soil nutrient content, soil enzymatic activity, and the microbiological community. The growth of L. corniculatus was reduced in TPH-contaminated soil, particularly affecting root biomass by 52.17%. Applying inoculum positively affected total plant biomass in uncontaminated (51.44%) and contaminated (33.30%) soil. The GTC-GVT/2021 inoculum significantly enhanced the degradation of TPH in contaminated soil after 90 days by 20.8% and in conjunction with L. corniculatus by 26.33% compared to the control. The soil enzymatic activity was more pronounced in TPH-contaminated soil treatments, and in most cases, the presence of L. corniculatus and inoculum led to a significantly higher soil enzymatic activity. The cultivation of L. corniculatus and the inoculum resulted in an increased concentration of inorganic P, NH4+, and water-soluble phenols in the soil, while no rise in NO3 was observed. Full article
(This article belongs to the Section Pollution Prevention, Mitigation and Sustainability)
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<p>TPH concentration dynamics after 45 and 90 days in different soil bioremediation treatments: CH: TPH-contaminated soil; CHP: TPH-contaminated soil planted with <span class="html-italic">L. corniculatus</span>; CHM: TPH-contaminated soil with GTC-GVT/2021 inoculum; CHPM: TPH-contaminated soil planted with <span class="html-italic">L. corniculatus</span> + GTC-GVT/2021 inoculum).</p>
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<p>The dehydrogenase (<b>1</b>), urease (<b>2</b>), alkaline phosphatase (<b>3</b>), and acid phosphatase (<b>4</b>) activity in the soil after 90 days in different soil bioremediation treatments: C: control unpolluted soil; CP: control unpolluted soil planted with <span class="html-italic">L. corniculatus</span>; CM: control unpolluted soil with GTC-GVT/2021 inoculum; CPM: control unpolluted soil planted with <span class="html-italic">L. corniculatus</span> + GTC-GVT/2021 inoculum; CH: TPH-contaminated soil; CHP: TPH-contaminated soil planted with <span class="html-italic">L. corniculatus</span>; CHM: TPH-contaminated soil with GTC-GVT/2021 inoculum; CHPM: TPH-contaminated soil planted with <span class="html-italic">L. corniculatus</span> + GTC-GVT/2021 inoculum). The dashed lines indicate the initial concentrations. Dissimilar letters designate a statistically significant difference (<span class="html-italic">p</span> &lt; 0.05) among the treatments (LSD test). (<b>A</b>) uncontaminated soil group, (<b>B</b>) TPH-contaminated soil group.</p>
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<p>Average well-colour development (AWCD) of 31 different carbon sources metabolised substrates in Biolog EcoPlates based on 96 h of soil extract incubation obtained from different soil bioremediation treatments: C: initial unpolluted soil; CP: control unpolluted soil planted with <span class="html-italic">L. corniculatus</span>; CPM: control unpolluted soil planted with <span class="html-italic">L. corniculatus</span> + GTC-GVT/2021 inoculum; CH: initial TPH-contaminated soil; CHP: TPH-contaminated soil planted with <span class="html-italic">L. corniculatus</span>; CHPM: TPH-contaminated soil planted with <span class="html-italic">L. corniculatus</span> + GTC-GVT/2021 inoculum).</p>
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<p>Shoot (<b>A</b>), root (<b>B</b>), dry biomass and root/shoot ratio (<b>C</b>) of <span class="html-italic">L. corniculatus</span> grown in CP, CPM, CHP, and CHPM treatments for 90 days. Letters indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05) between the treatments (LSD test).</p>
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<p>The final concentrations of NH<sub>4</sub><sup>+</sup> (<b>1</b>), (NO<sub>3</sub><sup>−</sup>) (<b>2</b>), inorganic P (<b>3</b>), and water-soluble phenols (<b>4</b>) in the soil after 90 days in different soil bioremediation treatments: C: control unpolluted soil; CP: control unpolluted soil planted with <span class="html-italic">L. corniculatus</span>; CM: control unpolluted soil with GTC-GVT/2021 inoculum; CPM: control unpolluted soil planted with <span class="html-italic">L. corniculatus</span> + GTC-GVT/2021 inoculum; CH: TPH-contaminated soil; CHP: TPH-contaminated soil planted with <span class="html-italic">L. corniculatus</span>; CHM: TPH-contaminated soil with GTC-GVT/2021 inoculum; CHPM: TPH-contaminated soil planted with <span class="html-italic">L. corniculatus</span> + GTC-GVT/2021 inoculum). The dashed lines indicate the initial concentrations. Dissimilar letters designate a statistically significant difference (<span class="html-italic">p</span> &lt; 0.05) among the treatments (LSD test). (<b>A</b>) uncontaminated soil group, (<b>B</b>) TPH-contaminated soil group.</p>
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14 pages, 2274 KiB  
Article
The Role of the Soil Seed Bank in the Recovery and Restoration of a Burned Amazonian Terra Firme Forest
by Vynicius B. Oliveira, Mário A. G. Jardim, Maria Fabíola Barros, Danilo S. Silva, Ima C. G. Vieira and Marcelo Tabarelli
Forests 2024, 15(9), 1513; https://doi.org/10.3390/f15091513 - 29 Aug 2024
Viewed by 404
Abstract
Here, we examine the effects of wildfires on the soil seed bank of a terra firme forest in the eastern Amazon. This seed bank is described via community-level attributes across forest stands exposed to wildfires once or twice, as well as across unburned, [...] Read more.
Here, we examine the effects of wildfires on the soil seed bank of a terra firme forest in the eastern Amazon. This seed bank is described via community-level attributes across forest stands exposed to wildfires once or twice, as well as across unburned, old-growth forest stands. A total of 2345 seeds germinated (837.5 seeds/m2). Across all three forest habitats, the soil seed bank was dominated by a small set of light-demanding species, with two to three species accounting for over 80% of all seeds. On the other hand, the seed bank of all habitats completely lacked seeds from the old-growth flora. Wildfires posed no effects relative to seed density and species richness. However, fire (1) reduced beta diversity, (2) caused an 8% increase in herb abundance and a 4% increase in the number of seeds produced by short-lived pioneers, and (3) resulted in a slight impact on taxonomic species composition. Our results suggest that the soil seed bank, while exhibiting high seed densities, is naturally species poor and, thus, relatively resistant to the first fire events. This implies that the recovery of fire-degraded forests will rely on vertebrate-dispersed seeds coming from any remaining well-preserved old-growth forest stands that are present in the landscape and are highly vulnerable to fire. Full article
(This article belongs to the Special Issue Impact of Disturbance on Forest Regeneration and Recruitment)
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<p>Location of the study area in the eastern Amazon (<b>a</b>), with emphasis on the Tapajós-Arapiuns Extractive Reserve (<b>b</b>), and location of the studied plots (<b>c</b>). Source for burned area data: Mapbiomas Fogo (2023).</p>
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<p>Seed density (<b>a</b>), species richness (<b>b</b>), common species (<b>c</b>), and dominant species (<b>d</b>) of unburned (UF), once-burned (BF1), and twice-burned (BF2) habitats in the Tapajós-Arapiuns Extractive Reserve, Brazil.</p>
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<p>Species accumulation curves for seeds in unburned (UF), once-burned (BF1), and twice-burned (BF2) habitats in the Tapajós-Arapius Extractive Reserve, Brazil.</p>
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<p>Frequency of seeds into categories of life forms (<b>a</b>), regeneration strategies (<b>b</b>), and dispersal syndromes (<b>c</b>) from the soil seed bank for unburned (UF), once-burned (BF1), and twice-burned (BF2) habitas in the Tapajós-Arapiuns Extractive Reserve, Brazil.</p>
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<p>Nonmetric multidimensional scaling (NMDs) between the taxonomic composition of the soil seed bank from unburned (UF), burned-once (BF1), and burned-twice (BF2) habitats in the Tapajós-Arapiuns Extractive Reserve, Brazil.</p>
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<p>Simple linear regression between the NMDS1 (as a proxy for species’ taxonomic composition) and the frequency of short-life pioneer species from the soil seed bank of the Tapajós-Arapiuns Extractive Reserve, Brazil.</p>
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14 pages, 1350 KiB  
Article
Pasture Recovery Period Affects Humic Substances and Oxidations of Organic Matter in Eastern Amazon
by Carlos Augusto Rocha de Moraes Rego, Juan López de Herrera, Paulo Sérgio Rabello de Oliveira, Luciano Cavalcante Muniz, Jean Sérgio Rosset, Eloisa Mattei, Lucas da Silveira, Marinez Carpiski Sampaio, Marcos Gervasio Pereira, Karolline Rosa Cutrim Silva and Ismênia Ribeiro de Oliveira
Agronomy 2024, 14(9), 1937; https://doi.org/10.3390/agronomy14091937 - 28 Aug 2024
Viewed by 287
Abstract
Land management practices that overlook soil limitations and potential have led to varying degrees of degradation. This study evaluates the carbon content in chemical and oxidisable soil fractions across different pasture recovery periods, comparing them to secondary forests. The management practices assessed include [...] Read more.
Land management practices that overlook soil limitations and potential have led to varying degrees of degradation. This study evaluates the carbon content in chemical and oxidisable soil fractions across different pasture recovery periods, comparing them to secondary forests. The management practices assessed include the following: secondary forest (SF), perennial pasture (PP), perennial pasture recovered five years ago (P5), and perennial pasture recovered eight years ago (P8), all on Plinthosols. We analysed carbon levels in oxidisable fractions and humic substances at depths of 0–0.10 m, 0.10–0.20 m, 0.20–0.30 m, and 0.30–0.40 m. The SF and P8 areas showed the highest organic matter content within the humic fractions, compared to the PP and P5 areas. Additionally, the P8 area demonstrated an increase in the labile and moderately recalcitrant fractions of organic matter, standing out among the different fractions evaluated. The multivariate principal component analysis indicated that P8 has the greatest impact on soil quality, followed by FS, P5, and PP. The pasture recovery over the past eight years has significantly improved soil carbon accumulation, highlighting the benefits of land restoration. Full article
(This article belongs to the Special Issue Soil Health and Crop Management in Conservation Agriculture)
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<p>Geographic location of the study in the Pindaré-Mirim/MA.</p>
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<p>Variation in carbon stock (ΔCS) of the humic fractions of soil organic matter in different areas in the Amazon of Maranhão, 0.00–0.40 m section. Legend: PP: perennial pasture, P5: perennial pasture recovered five years ago, and P8: perennial pasture recovered eight years ago.</p>
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<p>Analysis of principal components in different areas in the Amazon of Maranhão, 0.00–0.40 m section. Legend: SF: secondary forest, PP: perennial pasture, P5: perennial pasture recovered five years ago, P8: perennial pasture recovered eight years ago.</p>
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