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Search Results (1,945)

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Keywords = soil organic carbon content

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17 pages, 4652 KiB  
Article
Using Food Industry Byproduct to Stabilize an Expansive Clay
by Nicole L. Shaw, Arvin Farid and Zahra Taheri Sarteshnizi
Waste 2025, 3(1), 1; https://doi.org/10.3390/waste3010001 - 24 Dec 2024
Abstract
The process of purifying agricultural products, at various food processing plants, generates waste materials that consist of precipitated calcium carbonate, organic debris, and trace amounts of soil and agricultural contaminants. A specific food-processing waste, hereafter referred to as a food industry byproduct, FIBP, [...] Read more.
The process of purifying agricultural products, at various food processing plants, generates waste materials that consist of precipitated calcium carbonate, organic debris, and trace amounts of soil and agricultural contaminants. A specific food-processing waste, hereafter referred to as a food industry byproduct, FIBP, is typically stockpiled on land adjacent to the corresponding food processing facilities due to its large volume and chemical composition. The FIBP also contains commercially available unspent lime products, which makes its reuse viable in various applications. An example is construction applications where an organic content of up to 5% by weight is allowed, such as treating expansive clays. Traditionally, lime stabilization has been used for improving the properties of expansive clays, where ground improvement methods are necessary for a large area. However, the process of producing lime is resource- and energy-intensive as it includes crushing and heating limestone in kilns to extract lime. Therefore, one specific doubly sustainable application is the treatment of expansive clays using the FIBP instead of lime. The main application tested here is the treatment of expansive clayey soils underneath a stretch of State Highway 95 near Marsing, ID. Other potential applications are in road and embankment construction. To evaluate the potential of expansive clay stabilization utilizing the FIBP, a series of geotechnical and environmental laboratory testing were conducted to measure the engineering properties (e.g., swell potential, permeability, and strength properties) of expansive clay amended with FIBP. Preliminary testing on blends with an expansive clay suggests benefits such as decreased swelling potential, increased density, and leachate immobilization. Full article
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<p>(<b>a</b>) FIBP-amended road base. (<b>b</b>) Finished gravel surface upon project completion.</p>
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<p>(<b>a</b>) FIBP in powdery and clumped forms. (<b>b</b>) Expansive clay.</p>
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<p>Hydrometer analysis for all samples.</p>
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<p>Combined moisture–density results for all samples.</p>
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<p>(<b>a</b>) Optimum moisture trends. (<b>b</b>) Maximum dry-density trends.</p>
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<p>Direct shear test data to demonstrate how peak effective stress points are selected.</p>
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<p>Mohr–Coulomb failure parameters were measured for all blends.</p>
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<p>Unconfined compressive strength trends for all samples.</p>
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<p>Hydraulic conductivity trends.</p>
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<p>Hydraulic conductivity trends with outlier removed.</p>
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<p>Experimental setup (permeameters) for measuring hydraulic conductivity.</p>
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<p>Swell strain trends.</p>
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<p>Experimental setup (consolidation testing apparatus) used for swell tests.</p>
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<p>Final evaluation of FIBP blends based on strength and swelling properties (Arrow colors correspond to the colors of the bar chart).</p>
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16 pages, 2780 KiB  
Article
Effects of Cover Crops on Soil Inorganic Nitrogen and Organic Carbon Dynamics in Paddy Fields
by Jun Sugai, Naoya Takashima, Koki Muto, Takatoki Kaku, Honoka Nakayama, Naomi Asagi and Masakazu Komatsuzaki
Agriculture 2024, 14(12), 2365; https://doi.org/10.3390/agriculture14122365 - 23 Dec 2024
Abstract
Rice is a staple food in Asia, and its impact on the environment is considerable, such as chemical input concerns. Organic rice farming represents an alternative approach to reducing environmental concerns throughout rice production. However, the precise nutrient management to optimize organic rice [...] Read more.
Rice is a staple food in Asia, and its impact on the environment is considerable, such as chemical input concerns. Organic rice farming represents an alternative approach to reducing environmental concerns throughout rice production. However, the precise nutrient management to optimize organic rice production while recovering soil residual nitrogen (N) for the subsequent crops remains unclear. This study aims to: (1) assess nutrient recovery in soil cultivated with cover crops, including Italian ryegrass and hairy vetch, and (2) investigate the optimization of nutrient management in organic rice farming using cover crops. An experiment was conducted in a paddy field adopting cover crop plots and fallow (FA) plots in four replicates each from 2021 to 2023. In addition, incubation studies were conducted in 2021 and 2022. The incubation study included various treatments: (1) soil from cover crop or FA plots, (2) with or without cover crop residues, (3) with or without weed input (2021). In 2022, fertilizer input replaced weed input. The field study indicated cover crop biomass was larger than that of weeds. Furthermore, it can determine cover crops have more recyclable plant N compared to weeds when incorporated into the soil. In contrast, there was no noticeable difference in soil inorganic N and soil total organic carbon (C) contents between cover crop and FA plots at the 0–90 cm depth. In the incubation study, we found the soil of cover crop plots and cover crop input show less inorganic N than the soil of FA plots and cover crop input during the incubation period. However, the soil of the cover crop plots and cover crop input showed a high inorganic N content after setting the flooded condition. It indicates the soil of cover crop plots, and cover crop input provides N to the soil for a longer period. Overall, our results show that winter cover crop application in paddy fields contributes to N recovery and helps maintain soil fertility. Specifically, the occasional cultivation of a combination of Italian ryegrass and hairy vetch as winter cover crops can contribute to reducing the reliance on chemical fertilizers. This practice also promotes sustainable rice farming in paddy fields. Full article
(This article belongs to the Special Issue The Responses of Food Crops to Fertilization and Conservation Tillage)
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<p>Comparison of CC (cover crop) biomass and weed biomass in a paddy field. Vertical bars mean the standard error. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). The biomass of the CC in 2023 is the sum of the Italian ryegrass and the hairy vetch.</p>
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<p>Comparison of cover crop (CC) N and weed N in a paddy field. Vertical bars mean the standard error. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). Plant N of the CC in 2023 is the sum of the Italian ryegrass and the hairy vetch.</p>
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<p>Comparison of rice grain yield in a paddy field. Yield is on a paddy basis. Vertical bars mean the standard error. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). CC: cover crop. FA: fallow.</p>
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<p>Soil inorganic N dynamics (mg kg<sup>−1</sup>) in 0–90 cm soil profile of the paddy field in April 2021 (<b>A</b>), December 2021 (<b>B</b>), April 2022 (<b>C</b>), December 2022 (<b>D</b>), April 2023 (<b>E</b>) and December 2023 (<b>F</b>). Inorganic N: sum of NH<sub>4</sub>-N and NO<sub>3</sub>-N. CC: cover crop. FA: fallow. Inorganic nitrogen at a depth of 15 cm at each sampling period was calculated by dividing the nitrogen storage in the 0–15 cm soil layer by the soil mass in the same layer. Horizontal bars mean the standard error. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Soil inorganic N stock (Mg ha<sup>−1</sup>) in the 0–30 cm soil profile of paddy fields in April and December from 2021 to 2023. CC: cover crop. FA: fallow. Vertical bars mean the standard error. Different letters indicate significant differences by sampling period (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Soil total organic C content (%) in the 0–90 cm soil profile of paddy fields in April 2021 (<b>A</b>), December 2021 (<b>B</b>), April 2022 (<b>C</b>), December 2022 (<b>D</b>), April 2023 (<b>E</b>) and December 2023 (<b>F</b>). CC: cover crop. FA: fallow. Soil total C at a depth of 15 cm at each sampling period was calculated by dividing the C stock in the 0–15 cm soil layer by the soil mass in the same layer. Horizontal bars mean the standard error. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Soil C stock (Mg ha<sup>−1</sup>) in the 0–30 cm soil profile of paddy fields in April and December from 2021 to 2023. CC: cover crop. FA: fallow. Vertical bars mean the standard error. Different letters indicate significant differences by interactive effect between soil type and sampling period (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Comparison of soil inorganic N between cover crop plot and FA plot in paddy field at week 4. CC: cover crop. FA: fallow. Vertical bars mean the standard error. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Changes in soil inorganic N during the 8 weeks of incubation of paddy soil with or without plant residues input. Vertical bars mean the standard error. CC: cover crop. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). Significant differences were compared by week.</p>
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<p>Interactive effect of cover crop plot or FA plot soil and with or without plant residues input on soil inorganic N in week 8. CC: cover crop. FA: fallow. Vertical bars mean the standard error. Different letters indicate significant differences by treatment (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Changes in soil inorganic N during the 8 weeks of incubation of paddy soil with or without fertilizer input. Vertical bars mean the standard error. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). Significant differences were compared by week.</p>
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<p>Changes in soil inorganic N during the 8 weeks of incubation of a paddy soil with or without cover crop input. CC: cover crop. FA: fallow. Vertical bars mean the standard error. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). Significant differences were compared by week.</p>
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13 pages, 2036 KiB  
Article
Soil Organic Carbon Storage and Stratification in Land Use Types in the Source Area of the Tarim River Basin
by Qin Zhang, Chunfang Yue, Pujia Yu, Hailiang Xu, Jie Wu and Fangyu Sheng
Sustainability 2024, 16(24), 11255; https://doi.org/10.3390/su162411255 - 22 Dec 2024
Viewed by 229
Abstract
Accurate analysis of soil organic carbon (SOC) under different land uses in ecologically fragile arid zones is essential for effective regulatory measures and improvement of ecological quality. This study selected the ecologically fragile Tarim River source area as an example, aiming to quantitatively [...] Read more.
Accurate analysis of soil organic carbon (SOC) under different land uses in ecologically fragile arid zones is essential for effective regulatory measures and improvement of ecological quality. This study selected the ecologically fragile Tarim River source area as an example, aiming to quantitatively assess the SOC content, storage, carbon sequestration potential, and stratification ratio (SR) of different ecological land use types. Soil depths from 0–50 cm were determined and analyzed using the K2Cr2O7-H2SO4 oxidation method, the equivalent soil mass method and mathematical statistics. Forest, shrubland, and grassland ecological land types were included. The results show the following: (1) Both SOC content and storage showed a decrease with increasing soil depth. The total SOC content and storage sequence from high to low were natural forest, grassland, and shrubland. (2) There are variations in the SOC sequestration potential among the different ecological land types and shrubland (40.64 Mg C ha−1) > grassland (37.69 Mg C ha−1). (3) The SRs of the SOC in the forest were significantly greater than those in the shrubland and grassland. The different ecological land types had significant impacts on SR2, SR3, and SR4. SR2 could serve as a reliable index for assessing the impact of management practices on soil quality. The study area has a high potential for soil carbon sequestration in the future under these ecological conservation and management measures. Full article
(This article belongs to the Special Issue Land Use/Cover Change and Its Environmental Effects: Second Edition)
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<p>Map of the study area and the sampling sites.</p>
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<p>Land use classification map of the study area for (<b>a</b>) 2015, (<b>b</b>) 2020.</p>
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<p>Mean values (±standard error) of SOC content at five depths in soils of different ecological land types. The bars represent standard errors. Values with the same capital letters (ecological land type) and lowercase letters (soil depths) indicate no significant difference at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>(<b>a</b>) Mean values of total SOCs at five depths in the different ecological land use types. The bars represent standard errors. Values with the different capital letters (ecological land type) indicate significant difference at <span class="html-italic">p</span> &lt; 0.05. (<b>b</b>) The carbon sequestration potential of SOC under the different ecological land use types.</p>
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<p>The SR of the SOC content under the different ecological land types. The bars represent standard errors. Values with the different capital letters (ecological land type) indicate significant difference at <span class="html-italic">p</span> &lt; 0.05. F and <span class="html-italic">p</span> values are the ANOVA results at the same soil depth.</p>
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17 pages, 2832 KiB  
Article
Effects of Close-to-Nature Transformation of Plantations on Eco-Hydrological Function in Hainan Tropical Rainforest National Park
by Aohua Yang, Guijing Li, Wencheng Peng, Long Wan, Xiqiang Song, Yuguo Liu and Shouqian Nong
Water 2024, 16(24), 3692; https://doi.org/10.3390/w16243692 - 21 Dec 2024
Viewed by 287
Abstract
Girdling is a crucial technique for promoting the close-to-nature transformation of plantation forests in Hainan Tropical Rainforest National Park (HNNP). It has shown effectiveness in aspects such as community structure and biodiversity restoration. However, its impacts on ecological functions like eco-hydrology still require [...] Read more.
Girdling is a crucial technique for promoting the close-to-nature transformation of plantation forests in Hainan Tropical Rainforest National Park (HNNP). It has shown effectiveness in aspects such as community structure and biodiversity restoration. However, its impacts on ecological functions like eco-hydrology still require further in-depth investigation. This study analyzes the impact of girdling on the eco-hydrological indices of three plantations—Acacia mangium, Pinus caribaea, and Cunninghamia lanceolata—through field investigations and laboratory tests. The data was evaluated using a game theory combination weighting-cloud model. The results show that the eco-hydrological indicators of leaf litter in A. mangium increased by 5.77% while those of P. caribaea and C. lanceolata decreased by 11.86% and 5.29%, respectively. Soil bulk density decreased slightly across all plantations while total porosity increased, with A. mangium showing the highest increase of 20.31%. Organic carbon content increased by 76.81% in A. mangium and 7.24% in C. lanceolata, whereas it decreased in P. caribaea. Saturated hydraulic conductivity increased by 33.32% in P. caribaea and 20.91% in A. mangium but decreased in C. lanceolata. Based on the cloud model, the eco-hydrological function of A. mangium improved from ‘medium’ to ‘good’, while that of P. caribaea and C. lanceolata declined towards the ‘poor’ level. In summary, during the process of close-to-nature transformation of tropical rainforests, girdling is an effective method to enhance the ecohydrological functions of broadleaf planted forests. However, for coniferous species, the ecohydrological functions of the planted forests weaken in the short term following the transformation. Full article
(This article belongs to the Section Ecohydrology)
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<p>Study area map.</p>
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<p>Pictures of different plantations before and after the close-to-nature transformation through girdling. (<b>a</b>) Before girdling of <span class="html-italic">Acacia mangium</span> plantations; (<b>b</b>) After girdling of <span class="html-italic">Acacia mangium</span> plantations; (<b>c</b>) Before girdling of <span class="html-italic">Cunninghamia lanceolata</span> plantations; (<b>d</b>) After girdling of <span class="html-italic">Cunninghamia lanceolata</span> plantations; (<b>e</b>) After girdling of <span class="html-italic">Pinus caribaea</span> plantations; (<b>f</b>) Before girdling of <span class="html-italic">Pinus caribaea</span> plantations.</p>
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<p>Litter water holding capacity, litter water absorption rate and soaking time process curve: (<b>a</b>) Litter water holding capacity and soaking time process curve; (<b>b</b>) Litter water absorption rate and soaking time process curve.</p>
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<p>Comprehensive evaluation cloud model map of eco-hydrological function; (<b>a</b>): Standard cloud model of eco-hydrological function of different plantations in Hainan Tropical Rainforest National Park; (<b>b</b>): <span class="html-italic">A. mangium</span> plantation eco-hydrological function cloud model diagram; (<b>c</b>): Cloud model diagram of eco-hydrological function of <span class="html-italic">C. lanceolata</span> plantation; (<b>d</b>): Cloud model diagram of eco-hydrological function of <span class="html-italic">P. caribaea</span> plantation.</p>
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17 pages, 1721 KiB  
Article
The Effect of the Vaccinium myrtillus L. Rhizosphere on the Maturity Index for Predatory Mites (Mesostigmata: Gamasina) in Assessing Anthropogenic Pollution of Forest Soils
by Gabriela Barczyk, Aleksandra Nadgórska-Socha and Marta Kandziora-Ciupa
Forests 2024, 15(12), 2245; https://doi.org/10.3390/f15122245 - 20 Dec 2024
Viewed by 177
Abstract
The soil’s biological quality and its functions are closely linked. They determine the ecological processes and ecosystem services. Therefore, the heavy metal contamination of forest soils, leading to their degradation, is a major international problem. Soil is a habitat for many organisms, and [...] Read more.
The soil’s biological quality and its functions are closely linked. They determine the ecological processes and ecosystem services. Therefore, the heavy metal contamination of forest soils, leading to their degradation, is a major international problem. Soil is a habitat for many organisms, and the strong correlations between soil properties, vegetation, and soil fauna are particularly evident in the rhizosphere. Therefore, comprehensive soil monitoring must take all these elements into account. In forest soils, Vaccinium myrtillus plays a vital role. Despite this, there is still a lack of information in the literature on the interrelationship between microarthropod biodiversity, including predatory soil mites, and heavy metals in the rhizosphere zone of blueberry plants. To fill this gap, we assessed the impact of the V. myrtillus rhizosphere on soil stability and biological quality using a bioindicator based on predatory mites. We conducted the study in Poland, on selected forest sites characterised by varying degrees of soil contamination. In our study, we used a combined analysis based on the following indicators: maturity index (MI), contamination factor (CF), pollution load index (PLI), and potential ecological risk index (PERI), which allowed us to determine the level of soil contamination. We extracted 4190 Gamasina mites from soil samples. We also investigated soil properties such as pH, organic matter content, total carbon, total nitrogen, C/N ratio, and heavy metal concentrations (Cd, Cu, Zn, Pb, and Ni). Our study proved that the rhizosphere zone significantly influences the stability of the predatory soil mite community, but this influence depends on the degree of soil contamination. We found that in unpolluted or moderately polluted soil, soil mites prefer habitats with less biological activity, i.e., non-rhizosphere zones. These main results are fascinating and indicate the need for further in-depth research. Our study’s comprehensive combination of methods provides valuable information that can facilitate the interpretation of environmental results. In addition, our study can be a starting point for analysing the impact of the rhizosphere zones of many other plant species, especially those used in the reclamation or urban spaces. Full article
(This article belongs to the Section Forest Soil)
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<p>Map of sampling locations with the administrative division of the provinces: Kokotek—KO; Miasteczko Śląskie—M; Bukowno—B; Katowice–Kostuchna—K.</p>
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<p>Plot of species accumulation. MNR—Miasteczko Śląskie non-rhizosphere, BNR—Bukowno, non-rhizosphere KNR—Katowice–Kostuchna, non-rhizosphere, KONR—Kokotek, non-rhizosphere, MR—Miasteczko Śląskie, rhizosphere, BR—Bukowno, rhizosphere KR—Katowice–Kostuchna, rhizosphere, KOR—Kokotek, rhizosphere.</p>
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<p>Frequency distribution of life history classes of soil mites in sampling sites. K- and r-values are assigned to Gamasina soil mite taxa; see <a href="#forests-15-02245-t001" class="html-table">Table 1</a>.</p>
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<p>Frequency distribution of life history classes of soil mites in sampling sites. K- and r-values are assigned to Gamasina soil mite taxa; see <a href="#forests-15-02245-t001" class="html-table">Table 1</a>.</p>
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24 pages, 3635 KiB  
Article
Effects of Different Landscape Greening Pest Control Modes on Carbon Storage and Soil Physicochemical Properties
by Laixian Xu, Chao Wang, Youjun He and Yating He
Forests 2024, 15(12), 2235; https://doi.org/10.3390/f15122235 - 19 Dec 2024
Viewed by 312
Abstract
Understanding the effects of landscape greening pest control modes (LGPCMs) on carbon storage and soil physicochemical properties is crucial for promoting the sustainable development of urban landscape greening. Climate change and green development have led to increased landscape pest occurrences. However, the impacts [...] Read more.
Understanding the effects of landscape greening pest control modes (LGPCMs) on carbon storage and soil physicochemical properties is crucial for promoting the sustainable development of urban landscape greening. Climate change and green development have led to increased landscape pest occurrences. However, the impacts of different LGPCMs on carbon storage and soil properties remain unclear. We examined six typical LGPCMs employed in Beijing, China: chemical control (HXFZ), enclosure (WH), light trapping (DGYS), biological agent application (SWYJ), natural enemy release (SFTD), and trap hanging (XGYBQ). Field surveys and laboratory experiments were conducted to analyze their effects on carbon storage and soil physicochemical properties, and their interrelationships. The main results were as follows: (1) Different LGPCMs significantly affected carbon storage in the tree and soil layers (p < 0.05), but not in the shrub and herb layers (p > 0.05). Carbon storage composition across all modes followed the following order: tree layer (64.19%–93.52%) > soil layer > shrub layer > herb layer. HXFZ exhibited the highest tree layer carbon storage (95.82 t/hm2) but the lowest soil layer carbon storage (6.48 t/hm2), while DGYS performed best in the soil, herb, and shrub layers. (2) LGPCMs significantly influenced soil bulk density (SBD), clay (SC), silt particle (SSP), sand (SS), pH, organic carbon (OC), total nitrogen (TN), and heavy metal content (lead (Pb), cadmium (Cd), mercury (Hg)). WH had the highest TN (1.37 g/kg), TP (0.84 g/kg), SC (10.71%) and SSP (42.14%); HXFZ had the highest Cd (8.98 mg/kg), but lowest OC and Pb. DGYS had the highest OC and Hg, and the lowest Cd, SC, and TP. Under different LGPCMs, the heavy metal content in soil ranked as follows: Pb > Cd > Hg. (3) There were significant differences in the relationship between carbon storage and soil physicochemical properties under different LGPCMs. A significant positive correlation was observed between the soil layer carbon storage, TN, and OC, while significant negative correlations were noted between SS and SC as well as SSP. Under SFTD, the tree layer carbon storage showed a negative correlation with Cd, while under DGYS, it correlated negatively with pH and Hg. In summary, While HXFZ increased the short-term tree layer carbon storage, it reduced carbon storage in the other layers and damaged soil structure. Conversely, WH and DGYS better supported carbon sequestration and soil protection, offering more sustainable control strategies. We recommend developing integrated pest management focusing on green control methods, optimizing tree species selection, and enhancing plant and soil conservation management. These research results can provide scientific guidance for collaborative implementation of pest control and carbon sequestration in sustainable landscaping. Full article
(This article belongs to the Section Forest Health)
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<p>Distribution map of 33 sample plots across the 11 districts in Beijing.</p>
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<p>Effects of different LGPCMs on carbon storage in the tree layer, shrub layer, herb layer and soil layer. DGYS indicates the light trapping; HXFZ represents the chemical control; SFTD indicates the natural enemy release; SWYJ denotes the biological agent application; WH denotes the enclosure; XGYBQ represents the trap hanging. Different lowercase letters indicate significant differences between different LGPCMs (<span class="html-italic">p</span> &lt; 0.05). The same applies below.</p>
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<p>The proportion composition of carbon storage in tree layer, shrub layer, herb layer and soil layer under different LGPCMs.</p>
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<p>Effects of different LGPCMs on soil physical properties. SWC is soil water content; SBD is soil bulk density; SC is soil clay; SSP is soil silt particle; and SS is soil sand. Different lowercase letters indicate significant differences among LGPCMs (<span class="html-italic">p</span> &lt; 0.05). The same applies below.</p>
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<p>Effects of different LGPCMs on soil chemical properties. TN is total nitrogen; OC is organic carbon; TP is total phosphorus; pH is soil pH; Pb is lead; Cd is cadmium; Hg is mercury. Different lowercase letters indicate significant differences among LGPCMs (<span class="html-italic">p</span> &lt; 0.05). The same applies below.</p>
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<p>Relationship between carbon storage and soil physicochemical properties under different LGPCMs. TLC is tree layer carbon storage; GLC is shrub layer carbon storage; HLC is herb layer carbon storage; SLC is soil layer carbon storage. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. Circles of different sizes signify the magnitude of the Pearson correlation coefficient.</p>
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21 pages, 5247 KiB  
Article
Contribution of Glomalin-Related Soil Protein to Soil Organic Carbon Following Grassland Degradation and Restoration: A Case from Alpine Meadow of Qinghai–Tibet Plateau
by Zilong Cui, Jilin Xin, Xiaoxuan Yang, Yile Dang, Chengqing Lin, Zhanming Ma, Kaini Wang, Zhaoqi Wang and Yongkun Zhang
Land 2024, 13(12), 2223; https://doi.org/10.3390/land13122223 - 19 Dec 2024
Viewed by 233
Abstract
Glomalin-related soil proteins (GRSP) are an important microbial carbon source for soil organic carbon (SOC) and can also protect SOC by promoting the formation of soil aggregates. However, there is a lack of systematic research on how the contribution of GRSP to SOC [...] Read more.
Glomalin-related soil proteins (GRSP) are an important microbial carbon source for soil organic carbon (SOC) and can also protect SOC by promoting the formation of soil aggregates. However, there is a lack of systematic research on how the contribution of GRSP to SOC changes during grassland degradation and restoration. This study analyzed the changes in SOC, total glomalin-related soil protein (GRSPt), easily extractable glomalin-related soil protein (GRSPe) contents, and the ratios of GRSPe/SOC and GRSPt/SOC at different aggregate fractions in the 0–10 cm and 10–20 cm soil layers during the process of grassland degradation and restoration (from natural Sogong grass patches→degraded bare soil patches→transitional weed patches→naturally restored Sogong grass patches/artificially restored grass patches), to explore the contribution of GRSP to SOC at the aggregate scale during grassland succession. (1) With grassland degradation, the mean weight diameter (MWD) and the contents of SOC and GRSP in all aggregate fractions significantly decreased (p < 0.05); the natural restoration method was more effective in improving MWD than the artificial restoration method; for the SOC content in large aggregates and the GRSPt and GRSPe contents in different aggregate fractions, the artificial restoration method was more effective than the natural restoration method. (2) The contents of GRSPe and GRSPt in all aggregate fractions were significantly and linearly positively correlated with SOC content (p < 0.01). Moreover, during grassland degradation and restoration, the correlation between GRSPt and SOC in large aggregates first increased and then decreased. Notably, the correlation between GRSP and SOC in all aggregate fractions was significantly higher under the natural restoration method compared to the artificial restoration method. (3) During grassland degradation and restoration, the contents of GRSPe and GRSPt in the aggregate fractions of the 0~10 cm soil layer showed a clear decrease and increase, respectively. The change patterns of GRSPe/SOC and GRSPt/SOC were opposite to each other. Redundancy analysis revealed that total nitrogen (TN) was the factor that explained the highest variance in GRSP content, SOC content, and the GRSPe/SOC ratio across the aggregate fractions, while total phosphorus (TP) was the factor with the strongest explanatory power for the GRSPt/SOC ratio. This study found that the process of grassland degradation and restoration significantly altered the MWD, GRSP content in different aggregate fractions, SOC content, and the contribution of GRSP to SOC, with the contribution of GRSP to SOC showing an opposite trend to the change in GRSP content. Moreover, TN and TP were the main factors influencing GRSP changes. This study provides a scientific basis for assessing the carbon sequestration potential and selecting restoration methods for degraded grasslands. Full article
(This article belongs to the Special Issue Soil Legacies, Land Use Change and Forest and Grassland Restoration)
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<p>Succession sequence of patches in the degradation and restoration process of alpine meadows and Spatial distribution of degraded alpine meadow bare soil patches. (<b>a</b>): Natural <span class="html-italic">Kobresia</span> Patches (NK); (<b>b</b>): Degraded Bare Patches (DB); (<b>c</b>): Transitional Weed Patches (TW); (<b>d<sub>1</sub></b>): Naturally Restored <span class="html-italic">Kobresia</span> Patches (NRK); (<b>d<sub>2</sub></b>): Artificially Restored <span class="html-italic">Gramineae</span> Patches (ARG).; (<b>e</b>): Spatial distribution of degraded alpine meadow bare soil patches (35 m × 45 m aerial image).</p>
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<p>Soil Organic Carbon (SOC) content at different stages of degradation and restoration in alpine meadows. (<b>a</b>) SOC changes in the 0~10 cm layer; (<b>b</b>) SOC changes in the 10~20 cm layer. Different uppercase letters indicate significant differences between different aggregate size fractions within the same successional stage (<span class="html-italic">p</span> &lt; 0.05). Different lowercase letters indicate significant differences within the same aggregate size fraction across different successional stages (<span class="html-italic">p</span> &lt; 0.05). &gt;0.25 mm: Macroaggregates; 0.053~0.25 mm: Microaggregates; &lt;0.053 mm: Silt–clay fraction; NK: Natural <span class="html-italic">Kobresia</span> Patches; DB: Degraded Bare Patches; TW: Transitional Weed Patches; NRK: Naturally Restored <span class="html-italic">Kobresia</span> Patches; ARG: Artificially Restored <span class="html-italic">Gramineae</span> Patches.</p>
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<p>Total glomalin-related soil protein (GRSPt) content in soil aggregates at different stages of degradation and restoration in alpine meadows. (<b>a</b>) GRSPt changes in the 0~10 cm layer; (<b>b</b>) GRSPt changes in the 10~20 cm layer. MA: &gt;0.25 mm aggregates; MI: 0.053~0.25 mm aggregates; &lt;0.053 mm aggregates. Error bars represent standard deviation. Different uppercase letters indicate significant differences between different aggregate size fractions within the same successional stage. Different lowercase letters indicate significant differences within the same aggregate size fraction across different successional stages.</p>
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<p>Easily extractable glomalin-related soil protein (GRSPe) content in soil aggregates at different stages of degradation and restoration in alpine meadows. (<b>a</b>) GRSPe changes in the 0~10 cm layer; (<b>b</b>) GRSPe changes in the 10~20 cm layer. MA: &gt;0.25 mm aggregates; MI: 0.053~0.25 mm aggregates; &lt;0.053 mm aggregates. Error bars represent standard deviation. Different uppercase letters indicate significant differences between different aggregate size fractions within the same successional stage. Different lowercase letters indicate significant differences within the same aggregate size fraction across different successional stages.</p>
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<p>Linear regression analysis of SOC and GRSP in soil aggregates at different stages of degradation and restoration in alpine meadows. (<b>a</b>) GRSPe and SOC in the 0~10 cm layer; (<b>b</b>) GRSPe and SOC in the 10~20 cm layer; (<b>c</b>) GRSPt and SOC in the 0~10 cm layer; (<b>d</b>) GRSPt and SOC in the 10~20 cm layer.</p>
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<p>Correlation heatmap of environmental factors with aggregate fractions, their stability, SOC, GRSP, and associated ratios. (<b>a</b>) Correlations in the 0~10 cm soil layer; (<b>b</b>) Correlations in the 10~20 cm soil layer. MA: &gt;0.25 mm aggregates; MI: 0.053~0.25 mm aggregates; SC: &lt;0.053 mm aggregates; MWD: Mean Weight Diameter; MASOC: Macroaggregate SOC content; MISOC: Microaggregate SOC content; SCSOC: Silt–clay SOC content; MAGRSPe: Macroaggregate GRSPe content; MIGRSPe: Microaggregate GRSPe content; SCGRSPe: Silt–clay GRSPe content; MAGRSPt: Macroaggregate GRSPt content; MIGRSPt: Microaggregate GRSPt content; SCGRSPt: Silt–clay GRSPt content; RB: Root Biomass; TP: Total Phosphorus; TN: Total Nitrogen; PLFAs: Phospholipid Fatty Acids; SOC: Soil Organic Carbon. (*) indicates <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Redundancy analysis of environmental factors with aggregate fractions, their stability, SOC, GRSP, and associated ratios. (<b>a</b>) SOC in aggregate fractions with environmental factors (0~10 cm); (<b>b</b>) GRSPt in aggregate fractions with environmental factors (0~10 cm); (<b>c</b>) GRSPe in aggregate fractions with environmental factors (0~10 cm); (<b>d</b>) GRSPe/GRSPt ratio in aggregate fractions with environmental factors (0~10 cm); (<b>e</b>) GRSPt/SOC ratio in aggregate fractions with environmental factors (0~10 cm); (<b>f</b>) GRSPe/SOC ratio in aggregate fractions with environmental factors (0~10 cm); (<b>g</b>) SOC in aggregate fractions with environmental factors (10~20 cm); (<b>h</b>) GRSPt in aggregate fractions with environmental factors (10~20 cm); (<b>i</b>) GRSPe in aggregate fractions with environmental factors (10~20 cm); (<b>j</b>) GRSPe/GRSPt ratio in aggregate fractions with environmental factors (10~20 cm); (<b>k</b>) GRSPt/SOC ratio in aggregate fractions with environmental factors (10~20 cm); (<b>l</b>) GRSPe/SOC ratio in aggregate fractions with environmental factors (10~20 cm).</p>
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<p>Redundancy analysis of environmental factors with aggregate fractions, their stability, SOC, GRSP, and associated ratios. (<b>a</b>) SOC in aggregate fractions with environmental factors (0~10 cm); (<b>b</b>) GRSPt in aggregate fractions with environmental factors (0~10 cm); (<b>c</b>) GRSPe in aggregate fractions with environmental factors (0~10 cm); (<b>d</b>) GRSPe/GRSPt ratio in aggregate fractions with environmental factors (0~10 cm); (<b>e</b>) GRSPt/SOC ratio in aggregate fractions with environmental factors (0~10 cm); (<b>f</b>) GRSPe/SOC ratio in aggregate fractions with environmental factors (0~10 cm); (<b>g</b>) SOC in aggregate fractions with environmental factors (10~20 cm); (<b>h</b>) GRSPt in aggregate fractions with environmental factors (10~20 cm); (<b>i</b>) GRSPe in aggregate fractions with environmental factors (10~20 cm); (<b>j</b>) GRSPe/GRSPt ratio in aggregate fractions with environmental factors (10~20 cm); (<b>k</b>) GRSPt/SOC ratio in aggregate fractions with environmental factors (10~20 cm); (<b>l</b>) GRSPe/SOC ratio in aggregate fractions with environmental factors (10~20 cm).</p>
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21 pages, 4044 KiB  
Article
The Effect of Soil Tillage Systems on the Soil Microbial and Enzymatic Properties Under Soybean (Glycine max L. Merrill) Cultivation—Implications for Sustainable Soil Management
by Jacek Długosz, Bożena Dębska and Anna Piotrowska-Długosz
Sustainability 2024, 16(24), 11140; https://doi.org/10.3390/su162411140 - 19 Dec 2024
Viewed by 366
Abstract
Reducing soil tillage with the application of catch-crop green mass as a mulch is a conservation practice that is used in agriculture to improve soil ecosystem functioning. Such a cultivation method enhances soil organic matter quantity and quality through the improvement of soil [...] Read more.
Reducing soil tillage with the application of catch-crop green mass as a mulch is a conservation practice that is used in agriculture to improve soil ecosystem functioning. Such a cultivation method enhances soil organic matter quantity and quality through the improvement of soil biological activity and nutrient availability, while reducing soil disturbance. Therefore, a three-year field experiment was conducted in the years 2017–2019 to evaluate the effect of three tillage methods (TMs) (conventional, CT; reduced, RT; and strip tillage, ST) on soil microbial and specific enzyme properties (microbial C and N content, the activity of dehydrogenases—DHA, the rate of fluorescein sodium salt hydrolysis—FDAH, CMC-cellulase—Cel and β-glucosidase—Glu) and certain basic soil properties. The study was performed in a field; it was a one-factor experiment that was carried out in a randomized block design. The soil samples were collected from the upper soil layer five times a year: in April (before the sowing of soybean), May, June, August and September (before soybean harvesting). The tillage methods or sampling dates used had no significant effect on the organic carbon and total nitrogen levels. Most of the C-related properties (the content of microbial biomass and the C-cycling enzymatic activity such as Cel and Glu) and microbial activity bioindicators (DHA activity, FDAH rate) revealed significant seasonal changes, whereby each variable was affected in a different order (e.g., the Cel activity was significantly higher in April and September than in other months—22%, while the DHA activity was significantly higher in June and August compared to other months—18%). RT significantly increased the enzymatic activity as compared to CT and ST, and the difference was between 8 and 33% (with a mean of 18%). The exception was the β-glucosidase activity as determined in 2019, which was significantly higher in the case of CT (1.02 mg pNP kg−1 h−1) and ST than in RT (0.705 mg pNP kg−1 h−1). However, the explanation for such phenomenon could not possibly be based on the available data. Our results suggested that the response of the enzyme activities toward the same factor may be due to the inherent variability in enzyme response associated with the spatial variability in soil properties as well as the properties of the enzyme itself and changes in the periodic occurrence of its substrates in the soil. Generally, the reduced tillage combined with plant residues return could be recommended for enhancing soil health and quality by improving its microbial and enzymatic features. The findings above suggest that a reduced tillage system is an important component of soil management in sustainable agriculture. Full article
(This article belongs to the Special Issue Sustainable Environmental Analysis of Soil and Water)
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<p>Climatograms presenting meteorological conditions in the study area: (<b>a</b>) 2017, (<b>b</b>) 2018 and (<b>c</b>) 2019. IV—April, V—May, VI—June, VII—July, VIII—August, IX—September, 1—the 1st decade, 2—the 2nd decade, 3—the 3rd decade.</p>
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<p>Block diagram of the soil properties determined in the laboratory. TOC—total organic carbon, DOC—dissolved organic carbon, Nt—total nitrogen, DNt—dissolved nitrogen, DHA—dehydrogenase, FDAH—rate of fluorescein sodium salt hydrolysis, Cel—CMC-cellulase, Glu—β-glucosidase.</p>
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<p>The content of microbial biomass carbon (MBC) (<b>a</b>,<b>b</b>) and nitrogen (MBN) (<b>c</b>,<b>d</b>) depending on the tillage system and sampling dates; mean values (±SE) (mg kg<sup>−1</sup>). CT—conventional tillage, RT—reduced tillage, ST—strip tillage. IV—April, V—May, VI—June, VIII—August, IX—September. Different capital letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) between tillage systems within the same year. Different small letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) between the sampling months within the same tillage system.</p>
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<p>Principal component analysis derived from the studied soil properties; (<b>a</b>) plot of the first two principal components (PC) for the assessed soil variables; TOC—total organic carbon, Nt—total nitrogen, DHA—dehydrogenase, FDAH—fluorescein sodium salt hydrolysis, Cel—cellulase, Glu—β-glucosidase, DOC—dissolved organic carbon, DNt—dissolved nitrogen, MBC—microbial biomass carbon, MBN—microbial biomass nitrogen, (<b>b</b>) principal component analysis of the properties determined in the individual study years (2017, 2018, 2019); CT—conventional tillage, RT—reduced tillage, ST—strip tillage.</p>
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22 pages, 2754 KiB  
Article
A Comparative Study of Agroecological Intensification Across Diverse European Agricultural Systems to Assess Soil Structure and Carbon Dynamics
by Modupe Olufemi Doyeni, Grazina Kadziene, Simona Pranaitiene, Alvyra Slepetiene, Aida Skersiene, Arman Shamshitov, Alessandra Trinchera, Dylan Warren Raffa, Elena Testani, Sebastien Fontaine, Antonio Rodriguez-Hernandez, Jim Rasmussen, Sara Sánchez-Moreno, Marjoleine Hanegraaf, Akin Un, Simon Sail and Skaidre Suproniene
Agronomy 2024, 14(12), 3024; https://doi.org/10.3390/agronomy14123024 - 18 Dec 2024
Viewed by 373
Abstract
Continuous agricultural activities lead to soil organic carbon (SOC) depletion, and agroecological intensification practices (i.e., reduced soil disturbance and crop diversification) have been suggested as strategies to increase SOC storage. The study aims to assess the effect of agroecological intensification levels (lower (T1) [...] Read more.
Continuous agricultural activities lead to soil organic carbon (SOC) depletion, and agroecological intensification practices (i.e., reduced soil disturbance and crop diversification) have been suggested as strategies to increase SOC storage. The study aims to assess the effect of agroecological intensification levels (lower (T1) and highest (T2)) on the soil C pool and aggregate stability and validate the correlation between different variables compared to the control (lowest/none (T3), where agroecological intensification was not applied. The C-stock, soil microbial biomass carbon (SMB-C), SOC, water extractable organic carbon (WEOC) in bulk soil, fine and coarse soil aggregates, and water-stable aggregates (WSA) were measured during maximum nutrient uptake in plants under diversified agroecological practices across different environmental conditions (core sites: Italy (CS1), France (CS2), Denmark (CS4), Spain (CS5), Netherlands (CS6), Lithuania (CS7), Turkey (CS8), and Belgium (CS9)). The soil aggregate stability varied among the CSs and treatments. At sites CS7 and CS9, WSA was higher in T1 and T2 compared to the control; a similar trend was observed at other sites, except CS1. SMB-C differed among the core sites, with the lowest value obtained in CS5 (52.3 μg g−1) and the highest in CS6 (455.1 μg g−1). The highest average contents of SOC and WEOC were obtained in bulk soil at CS2 (3.1 % and 0.3 g kg−1 respectively). Positive and statistically significant (p < 0.001) correlations were detected among all variables tested with SOC in bulk soil and WSA. This study demonstrates the significance of agroecological practices in improving soil carbon stock and optimizing plant–soil–microbe interactions. Full article
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<p>Selected sites are represented as (i) local-scale and (ii) European-scale gradients of agroecological intensification. See <a href="#agronomy-14-03024-t001" class="html-table">Table 1</a> for the full description of the experimental core sites.</p>
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<p>Amount of water stable aggregates (WSA) (%) within 0.25–1.0 mm soil fractions under diversified agroecological practices and different environmental conditions in tested sites. The sites are arranged according to European-scale gradients of ecological intensification (<a href="#agronomy-14-03024-f001" class="html-fig">Figure 1</a>). Each box plot represents the distribution of four replicates, showing the median, interquartile range, and data range (whiskers). The compact letters shown above each box plot indicate significant differences between treatments in each core site (<span class="html-italic">p</span> &lt; 0.05) based on Tukey’s HSD test or Dunn’s test.</p>
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<p>The C-stock (t ha<sup>−1</sup>) in bulk soil, under diversified agroecological practices and different environmental conditions at experimental sites. The sites are arranged according to European-scale gradients of ecological intensification (<a href="#agronomy-14-03024-f001" class="html-fig">Figure 1</a>). Each box plot represents the distribution of four replicates, showing the median, interquartile range, and data range (whiskers). The compact letters shown above each box plot indicate significant differences between treatments in each core site (<span class="html-italic">p</span> &lt; 0.05) based on Tukey’s HSD test or Dunn’s test.</p>
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<p>The soil microbial biomass carbon (SMB-C) (μg g<sup>−1</sup>) in bulk soil, under diversified agroecological practices and different environmental conditions at experimental sites. The sites are arranged according to European-scale gradients of ecological intensification (<a href="#agronomy-14-03024-f001" class="html-fig">Figure 1</a>). Each box plot represents the distribution of four replicates, showing the median, interquartile range, and data range (whiskers). The compact letters shown above each box plot indicate significant differences between treatments in each core site (<span class="html-italic">p</span> &lt; 0.05) based on Tukey’s HSD test or Dunn’s test.</p>
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<p>(<b>A</b>) The water-extractable organic carbon (WEOC), g kg<sup>−1</sup> in bulk soil, (<b>B</b>) fine (0.25–1.0 mm), and (<b>C</b>) coarse (&gt;1.0 mm) macroaggregates, under diversified agroecological practices and different environmental conditions at experimental sites. The sites are arranged according to European-scale gradients of ecological intensification (<a href="#agronomy-14-03024-f001" class="html-fig">Figure 1</a>). Each box plot represents the distribution of four replicates, showing the median, interquartile range, and data range (whiskers). The compact letters shown above each box plot indicate significant differences between treatments in each core site (<span class="html-italic">p</span> &lt; 0.05) based on Tukey’s HSD test or Dunn’s test.</p>
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<p>(<b>A</b>) SOC, % in bulk soil, (<b>B</b>) (0.25–1.0 mm) and (<b>C</b>) (&gt;1.0 mm), under diversified agroecological practices and different environmental conditions at experimental sites. The sites are arranged according to European-scale gradients of ecological intensification (<a href="#agronomy-14-03024-f001" class="html-fig">Figure 1</a>). Each box plot represents the distribution of four replicates, showing the median, interquartile range, and data range (whiskers). The compact letters shown above each box plot indicate significant differences between treatments in each core site (<span class="html-italic">p</span> &lt; 0.05) based on Tukey’s HSD test or Dunn’s test.</p>
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<p>Pearson correlation analysis between the WSA, SOC, WEOC, C-stock, and SMB-C data. Significance codes: <b>·</b> <span class="html-italic">p</span> &lt; 0.1; * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001.</p>
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26 pages, 7169 KiB  
Article
Geochemical Profiles of Deep Sediment Layers from the Kolubara District (Western Serbia): Contamination Status and Associated Risks of Heavy Metals
by Milica Vidak Vasić, Milena Radomirović, Pedro M. Velasco and Nevenka Mijatović
Agronomy 2024, 14(12), 3009; https://doi.org/10.3390/agronomy14123009 - 18 Dec 2024
Viewed by 334
Abstract
Global awareness of the harmful effects of heavy metal contamination in soil has increased significantly. Understanding the vertical distribution of oxides and elements is vital for tracing the history of potential contamination. Thus, this study focuses on deep sediment cores primarily composed of [...] Read more.
Global awareness of the harmful effects of heavy metal contamination in soil has increased significantly. Understanding the vertical distribution of oxides and elements is vital for tracing the history of potential contamination. Thus, this study focuses on deep sediment cores primarily composed of quartz and clay minerals from a small village in the western Tamnava Basin of Serbia. The aim was to assess the vertical distribution of 11 oxides and 21 elements and the ecological risks of eight heavy metals by analyzing 250 sediment samples from 18 boreholes at depths ranging from 5 to 58.5 m. Deep sediment core samples were analyzed using energy-dispersive X-ray fluorescence spectrometry (ED-XRF). Potential contamination levels were evaluated within the study area. Additionally, samples were analyzed for total carbonate and organic carbon contents and particles retained on a 0.063 mm sieve. Higher than permitted concentrations of vanadium (V), thallium (Tl), and barium (Ba) were found. Notably, this zone is located above a proposed lithium and boron mine in Gornje Nedeljice, making it crucial for monitoring efforts. Even if mining operations do not commence, examining the deep sediment layers in this rural area remains important. This study offers novel and valuable data on the concentrations of potentially toxic elements in undisturbed deep sediment, serving as a benchmark for future comparisons. Full article
(This article belongs to the Section Soil and Plant Nutrition)
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<p>The position of Gornje Crniljevo (Vlašić Mountain) and geological map with sampling sites.</p>
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<p>Bivariate histograms of Tl (<b>a</b>,<b>b</b>), V (<b>c</b>,<b>d</b>), and Ba (<b>e</b>,<b>f</b>) distributions through boreholes and depths of sampling (the color shades vary from the palest yellow to the darkest red as the concentrations of the elements increase).</p>
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<p>The average contribution of heavy metals to the potential ecological risk.</p>
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<p>Three-dimensional scatterplots for the selected variables: (<b>a</b>) LOI, RS, and SiO<sub>2</sub>, (<b>b</b>) LOI, RS, and Al<sub>2</sub>O<sub>3</sub> (LOI—loss on ignition at 1000 °C, RS—remains on the 0.063 mm sieve).</p>
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<p>(<b>a</b>) Eigenvalues of the factors, (<b>b</b>) PCA of the most influential and selected parameters and (<b>c</b>) projection of the cases on the factor plane (samples number 1–250).</p>
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13 pages, 1557 KiB  
Article
A Study of the Effects of Wetland Degradation on Soil-Microbial-Extracellular Enzyme Carbon, Nitrogen, and Phosphorus and Their Ecological Stoichiometry
by Ye Li, Jiuwang Jin, Shuangyi Li, Shuhao Xia and Jianbing Wei
Agronomy 2024, 14(12), 3008; https://doi.org/10.3390/agronomy14123008 - 18 Dec 2024
Viewed by 253
Abstract
Due to the unique geographic location of A’er Xiang, there is a natural landscape where sandy land and lake-marsh wetlands coexist. However, the wetland degradation caused by the disturbance of anthropogenic activities has led to the change in land use. In this study, [...] Read more.
Due to the unique geographic location of A’er Xiang, there is a natural landscape where sandy land and lake-marsh wetlands coexist. However, the wetland degradation caused by the disturbance of anthropogenic activities has led to the change in land use. In this study, the spatial-temporal substitution method was used to select five sample plots: the original wetland converted to forest land for reuse area of five years and ten years; the original wetland converted to cropland for reuse area of five years and ten years; and the native wetland. It aims to investigate the variations in carbon, nitrogen, and phosphorus and their stoichiometric characteristics of soil-microorganisms-extracellular enzymes before and after reuse, and to analyze potential interactions among these elements. The results indicated that following wetlands degradation, changes in land use for five years did not significantly affect the content of soil organic carbon (TOC), total nitrogen (TN), or total phosphorus (TP). However, after ten years, both TOC and TN, except for TP, decreased significantly. Microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) contents in cropland were consistently higher than those in WL, showing a trend of first increasing and then decreasing with longer conversion periods. In contrast, forest land values were lower than in WL and increased as the conversion period lengthened. The microbial biomass phosphorus (MBP) content was ranked across the five sample sites as follows: 10 CL > 5 CL > 5 FL > 10 FL > WL. β-1,4-glucosidase (BG) activity was significantly increased after conversion to forest land and significantly decreased after conversion to cropland. β-1,4-N-glucosidase (NAG) and L-leucine aminopeptidase (LAP) activities were ranked as follows among the five sites: 5 FL > WL > 5 CL > 10 FL > 10 CL. Phosphatase (PHOS) activity showed no significant changes post-conversion, though it was consistently lower compared to WL. Full article
(This article belongs to the Section Soil and Plant Nutrition)
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<p>Variation characteristics of soil C, N, and P contents among different land use types. Note: lowercase letters denote significant differences (<span class="html-italic">p</span> &lt; 0.05). The fertilizer treatment abbreviations are defined in Introduction.</p>
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<p>Variation characteristics of soil MBC, MBN, and MBP contents across different land use types. Note: lowercase letters denote significant differences (<span class="html-italic">p</span> &lt; 0.05). The fertilizer treatment abbreviations are defined in Introduction.</p>
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<p>Variation characteristics of soil extracellular enzyme activity cross different land use types. Note: lowercase letters denote significant differences (<span class="html-italic">p</span> &lt; 0.05). The fertilizer treatment abbreviations are defined in Introduction.</p>
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<p>Correlation between Soil Microbial Extracellular Enzyme Carbon, Nitrogen, and Phosphorus Content and Its Stoichiometric Ratio. Note: * indicates a significant difference at the 0.05 level; ** indicates a significant difference at the 0.01 level; *** indicates a significant difference at the 0.001 level.</p>
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16 pages, 8131 KiB  
Essay
Impact of Different Land-Use Types on Soil Microbial Carbon Metabolism Function in Arid Region of Alpine Grassland
by Keyi Li, Yaoguang Han, Mo Chen, Guangling Yu, Maidinuer Abulaizi, Yang Hu, Bohao Wang, Zailei Yang, Xinping Zhu and Hongtao Jia
Plants 2024, 13(24), 3531; https://doi.org/10.3390/plants13243531 - 18 Dec 2024
Viewed by 287
Abstract
There are discrepancies that exist in the effects of different land uses on soil organic carbon (SOC) and soil microbial carbon metabolism functions. However, the impact of land-use type changes on soil microbial carbon metabolism in alpine grassland arid areas is not well [...] Read more.
There are discrepancies that exist in the effects of different land uses on soil organic carbon (SOC) and soil microbial carbon metabolism functions. However, the impact of land-use type changes on soil microbial carbon metabolism in alpine grassland arid areas is not well understood, hindering our understanding of the carbon cycling processes in these ecosystems. Therefore, we chose three types of land use (continuous reclamation of grassland (RG), abandoned grassland (AG), and natural grazing grassland (GG)) to study the microbial carbon metabolism and its driving factors by the Biolog-ECO method. The results showed that the soil organic carbon content decreased by 16.02% in the RG and by 32.1% in the AG compared to the GG in the 0–20 cm soil layer (p < 0.05). Additionally, microorganisms have the highest utilization efficiency of carbohydrate carbon sources, the average values of average well color development (AWCD) were RG (0.26), AG (0.35), and GG (0.26). In the 0–20 cm soil layer, the Shannon–Wiener and the Simpson indices were 3% and 1% higher in the AG compared to the GG, respectively. The soil TOC/TN and soil available phosphorus (AP) were key factors that affected the diversity of soil microbial and carbon metabolism. They were closely related to land-use types. This study holds that abandoning grasslands accelerates the carbon metabolism of microorganisms, leading to the loss of SOC content. Full article
(This article belongs to the Special Issue Soil Carbon Management for Crop Production)
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<p>Location of the study area. The location of the study area and the test area in Bayinbuluk alpine grassland. Gray area: Xinjiang Uygur Autonomous Region, China. Continuous reclamation of grassland (RG), abandoned grassland (AG), and natural grazing grassland (GG).</p>
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<p>Changes in AWCD during soil microbial community cultivation in grasslands under land-use type. (<b>A</b>) shows the change in AWCD values from 0 to 144 h in the 0–20 cm soil layer. (<b>B</b>) shows the change in AWCD values from 0 to 144 h in the 20–40 cm soil layer. Continuous reclamation of grassland (RG), abandoned grassland (AG), and natural grazing grassland (GG).</p>
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<p>Utilization rate of six carbon sources of soil microorganisms in different treatments of grasslands. (<b>A</b>) shows the utilization of six carbon sources of soil microorganisms in the 0–20 cm soil layer. (<b>B</b>) shows the utilization of six carbon sources of soil microorganisms in the 20–40 cm soil layer. Different superscript lowercase letters indicate significant differences in different processing data, <span class="html-italic">p</span> &lt; 0.05. Continuous reclamation of grassland (RG), abandoned grassland (AG), and natural grazing grassland (GG).</p>
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<p>Heat map of functional carbon metabolism of soil microorganisms. (<b>A</b>) shows the thermogram of soil microbial functional carbon metabolism in the 0–20 cm soil layer. (<b>B</b>) shows the thermogram of soil microbial functional carbon metabolism in the 20–40 cm soil layer. RG 1–9 represents the nine samples treated with RG. AG 1–9 represents the nine samples treated with AG. GG 1–9 represents the nine samples treated with GG. Continuous reclamation of grassland (RG), abandoned grassland (AG), and natural grazing grassland (GG).</p>
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<p>Index of soil microbial community functional diversity for three grassland treatments. ** <span class="html-italic">p</span> &lt; 0.01. Continuous reclamation of grassland (RG), abandoned grassland (AG), and natural grazing grassland (GG).</p>
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<p>RDA analysis of soil physicochemical properties and soil carbon metabolism. (<b>A</b>). RDA analysis of soil physicochemical properties and soil carbon metabolism in the 0–20 cm soil layer. (<b>B</b>). RDA analysis of soil physicochemical properties and soil carbon metabolism in the 20–40 cm soil layer. A1, <span class="html-italic">Putrescine</span>; A2, <span class="html-italic">Phenylethylamine</span>; A3, <span class="html-italic">Glycyl-L-glutamic acid</span>; A4, <span class="html-italic">L-threonine</span>; A5, <span class="html-italic">L-serine</span>; A6, <span class="html-italic">L-phenylalanine</span>; A7, <span class="html-italic">L-asparagine acid</span>; A8, <span class="html-italic">L-arginine</span>; A9, <span class="html-italic">D-malic acid</span>; A10, <span class="html-italic">α-ketobutyric acid</span>; A11, <span class="html-italic">Itaconic acid</span>; A12, <span class="html-italic">γ-hydroxybutyric acid</span>; A13, <span class="html-italic">4-hydroxybenzoic acid</span>; A14, <span class="html-italic">2-hydroxybenzoic acid</span>; A15, <span class="html-italic">D-galacturonic acid</span>; A16, <span class="html-italic">D-galactolactone</span>; A17, <span class="html-italic">D,L-α-glycerol</span>; A18, <span class="html-italic">Glucose-1-phosphate</span>; A19, <span class="html-italic">D-glucosaminic acid</span>; A20, <span class="html-italic">N-acetyl-D-glucosamine</span>; A21 <span class="html-italic">D-mannitol</span>; A22, <span class="html-italic">i-erythritol</span>; A23, <span class="html-italic">D-xylose</span>; A24, <span class="html-italic">β-methyl-D-glucoside</span>; A25, <span class="html-italic">α-D-lactose</span>; A26, <span class="html-italic">D-cellobiose</span>; A27, <span class="html-italic">Glycogen</span>; A28, <span class="html-italic">α-cyclodextrin</span>; A29, <span class="html-italic">Tween 80</span>; A30, <span class="html-italic">Tween 40</span>; A31, <span class="html-italic">Methyl pyruvate</span>. MC: soil moisture content; SOC: soil organic carbon; TN: soil total nitrogen; TP: soil total phosphorus; TK: soil total potassium; AN: soil available nitrogen; AP: soil available phosphorus; AK: soil available potassium. Continuous reclamation of grassland (RG), abandoned grassland (AG), and natural grazing grassland (GG).</p>
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<p>PLS-PM analysis of microbial carbon metabolism function and soil layer with different land-use types. The red and blue arrows represent the positive and negative effects, respectively. The arrow line width reflects the size of the path coefficient. Numbers represent the path coefficient (direct effect), representing the strength and direction of the relationship between the variables. *** represents the statistical significance at <span class="html-italic">p</span> &lt; 0.001; ** represents the statistical significance at <span class="html-italic">p</span> &lt; 0.01. The proportion of variance (R<sup>2</sup>) is shown below each variable in the model. SOC: soil organic carbon; AN: soil available nitrogen; AP: soil available phosphorus; AK: soil available potassium; AWCD: average well color development. Microbial diversity includes the Shannon index, Simpson index, and Pielou evenness index. Carbon utilization capacity includes carbohydrates, amino acids, carboxylic acids, polymers, phenolic acids, and amines.</p>
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15 pages, 4520 KiB  
Article
Study on Factors Influencing the Migration of Heavy Metals from Soil to Vegetables in a Heavy Industry City
by Xiangmei Chen, Yongqiang Ren, Chi Li, Yan Shang, Rui Ji, De Yao and Yingchun He
Sustainability 2024, 16(24), 11084; https://doi.org/10.3390/su162411084 - 18 Dec 2024
Viewed by 417
Abstract
A comprehensive investigation into sustainable agriculture and environmental health was conducted in the Baotou region, encompassing analyses of 90 vegetable samples across 12 varieties and their corresponding rhizosphere soil samples. The physical and chemical properties of the soil, along with the content and [...] Read more.
A comprehensive investigation into sustainable agriculture and environmental health was conducted in the Baotou region, encompassing analyses of 90 vegetable samples across 12 varieties and their corresponding rhizosphere soil samples. The physical and chemical properties of the soil, along with the content and chemical speciations of heavy metals, were studied. Results indicated that the study area soil is alkaline to strongly alkaline, with significant heterogeneity in the organic carbon and phosphorus contents, affecting the uptake of heavy metals by these vegetables. The balance of Ca, K, Mg, and P is crucial for soil nutrient equilibrium and reducing heavy metal uptake. The heavy metal contents in the twelve vegetables were below the national food contaminant limit values, with notable accumulations of Cd, Zn, Cu, and Hg. There was a curvilinear correlation between the rhizosphere soil and vegetable contents of Cd and Hg, but differences in uptake were observed. Cd, Zn, Cu, and Hg contents in vegetables were significant, correlating curvilinearly with soil heavy metal content. Soil chemical forms influenced bioavailability, with Cd exhibiting the highest bioactivity. Thus, element migration variations in vegetables reflect the combined influence of the soil’s physical and chemical properties, heavy metal content, and chemical forms. This study validates food safety protocols and soil management practices. Results demonstrate key relationships between soil properties, metal behavior, and plant uptake, enabling targeted solutions for heavy metal contamination and soil remediation. Findings advance sustainable agriculture while protecting ecosystems and food security. Full article
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<p>Map of the study area and sample points.</p>
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<p>Average percentages of various phases of heavy meatal elements in soil.</p>
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<p>Cumulative graph of average heavy metal content in different vegetable categories.</p>
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<p>Mean enrichment factor (BCF) of heavy metals in twelve different vegetables.</p>
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<p>Correlation between Cd and Hg in three types of vegetables (Chinese cabbage, green onion, and cucumber) and root-zone soil. (<b>a</b>,<b>d</b>): Correlation diagram of cabbage and itsrhizosphere soils concentrations of Hg and Cd concentrations; (<b>b</b>,<b>e</b>): Correlation diagram of cucumber and itsrhizosphere soils concentrations of Hg and Cd concentrations; (<b>c</b>,<b>f</b>):Correlation diagram of Green pepper and itsrhizosphere soils concentrations of Hg and Cd concentrations.</p>
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<p>Proportion of heavy metal speciation in soil easily taken up by vegetables.</p>
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14 pages, 2139 KiB  
Article
Effects of Straw at Different Fermentation Phases on Soil Nutrient Availability and Microbial Activity
by Tian Chen, Yuxia Mei, Xinwei Liu, Zhuqing Zhao and Yunxiang Liang
Agronomy 2024, 14(12), 3005; https://doi.org/10.3390/agronomy14123005 - 17 Dec 2024
Viewed by 332
Abstract
Returning corn straw to the field is beneficial for improving soil fertility, but the fermentation phase significantly affects the dissolved organic carbon (DOC) content. However, there is limited research on the effects of straw at different fermentation phases on soil microorganisms and soil [...] Read more.
Returning corn straw to the field is beneficial for improving soil fertility, but the fermentation phase significantly affects the dissolved organic carbon (DOC) content. However, there is limited research on the effects of straw at different fermentation phases on soil microorganisms and soil nutrients. This study examined the effects of high-temperature fermentation phase straw (HF) and completely fermentation phase straw (CF) on soil nutrient activation and microorganism activity through pot experiments. The pot experiment results indicated a significant increase in soil DOC content following the application of corn straw, among which the high-temperature fermentation phase straw treatment (THF) exhibited the highest DOC content, which was 14% higher than the completely fermentation phase straw treatment (TCF). THF also significantly increased soil alkaline hydrolyzed nitrogen and available phosphorus content as well as urease and phosphatase, and promoted the uptake of nitrogen and phosphorus from soil by Brassica chinensis. THF significantly enhanced bacterial diversity and reduced the presence of pathogenic fungi. Compared to the TCF, the relative proportion of Fusarium under the THF decreased by 32.24%, effectively mitigating the impact of pathogenic fungi. THF also increased soil DOC content, enriched beneficial microbial community structure, increased soil enzyme activity, activated soil nutrients, thereby promoting the uptake of nitrogen and phosphorus by crops. Taken together, the results reveal that the application of high-temperature fermentation phase straw is conducive to soil fertilization and crop growth. Full article
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<p>Temporal changes of composting temperature (<b>a</b>), DOC content (<b>b</b>), and TOC content (<b>c</b>) during the corn straw compost. DOC, dissolved organic carbon; TOC, total organic carbon.</p>
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<p>Plant biomass of <span class="html-italic">B. chinensis</span> under different treatments. Note: T<sub>CK</sub>, control with no straw; T<sub>UF</sub>, treatment with unfermented straw; T<sub>HF</sub>, treatment with high-temperature fermentation phase straw; T<sub>CF</sub>, treatment with completely fermentation phase straw. Different lowercase letters above the bars indicate statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) according to Tukey’s HSD test.</p>
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<p>Soil alkali-hydrolyzed nitrogen (AN) (<b>a</b>), urease (<b>b</b>), available phosphorus (AP) (<b>c</b>), phosphatase (<b>d</b>), AK (available potassium) (<b>e</b>), TOC (<b>f</b>) and DOC (<b>g</b>) under different treatments. Note: T<sub>CK</sub>, control with no straw; T<sub>UF</sub>, treatment with unfermented straw; T<sub>HF</sub>, treament with high-temperature fermentation phase straw; T<sub>CF</sub>, treatment with completely fermentation phase straw. Different lowercase letters above the bars indicate statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) according to Tukey’s HSD test.</p>
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<p>Effects of different treatments on the growth of <span class="html-italic">Bacillus subtilis</span>. Note: T<sub>CK</sub>, control with no straw; T<sub>UF</sub>, treatment with unfermented straw; T<sub>HF</sub>, treament with high-temperature fermentation phase straw; T<sub>CF</sub>, treatment with completely fermentation phase straw. Different lowercase letters above the bars indicate statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) according to Tukey’s HSD test.</p>
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<p>Changes in soil bacterial (<b>a</b>) and fungal (<b>b</b>) α-diversity in soil under different treatments represented by Chao1 and Shannon indices. Non-metric multidimensional scaling (NMDS) of bacterial (<b>c</b>) and fungal (<b>d</b>) community composition of soil samples. Note: T<sub>CK</sub>, control with no straw; T<sub>UF</sub>, treatment with unfermented straw; T<sub>HF</sub>, treament with high-temperature fermentation phase straw; T<sub>CF</sub>, treatment with completely fermentation phase straw.</p>
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<p>Soil bacterial (<b>A</b>) and fungal (<b>B</b>) composition at genus level under 4 different straw treatments. Note: T<sub>CK</sub>, control with no straw; T<sub>UF</sub>, treatment with unfermented straw; T<sub>HF</sub>, treament with high-temperature fermentation phase straw; T<sub>CF</sub>, treatment with completely fermentation phase straw.</p>
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<p>(<b>a</b>–<b>d</b>) Relative proportion of 4 different bacterial genera under different treatments. (<b>e</b>–<b>h</b>) Relative proportion of 4 different fungal genera under different treatments. Note: T<sub>CK</sub>, control with no straw; T<sub>UF</sub>, treatment with unfermented straw; T<sub>HF</sub>, treament with high-temperature fermentation phase straw; T<sub>CF</sub>, treatment with completely fermentation phase straw. Different lowercase letters above the bars indicate statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) according to Tukey’s HSD test.</p>
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<p>Redundancy analysis (RDA) of soil physicochemical properties with the soil bacterial (<b>a</b>) and fungal (<b>b</b>) proportion under different straw treatments. Note: T<sub>CK</sub>, control with no straw; T<sub>UF</sub>, treatment with unfermented straw; T<sub>HF</sub>, treament with high-temperature fermentation phase straw; T<sub>CF</sub>, treatment with completely fermentation phase straw. The length of arrows presents the magnitude of correlation between environmental factors and bacterial community structure. The direction of arrows presents the variation tendency of environmental factors.</p>
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16 pages, 1394 KiB  
Article
Effects of Seven-Year-Optimized Irrigation and Nitrogen Management on Dynamics of Soil Organic Nitrogen Fractions, Soil Properties, and Crop Growth in Greenhouse Production
by Jianshuo Shi, Longgang Jiang, Liying Wang, Chengzhang Wang, Ruonan Li, Lijia Pan, Tianyuan Jia, Shenglin Hou and Zhou Jia
Agriculture 2024, 14(12), 2319; https://doi.org/10.3390/agriculture14122319 - 17 Dec 2024
Viewed by 348
Abstract
Exploring the temporal evolution dynamics of different soil organic nitrogen (N) components under different water–N management practices is a useful approach to accurately assessing N supply and soil fertility. This information can provide a scientific basis for precise water and N management methods [...] Read more.
Exploring the temporal evolution dynamics of different soil organic nitrogen (N) components under different water–N management practices is a useful approach to accurately assessing N supply and soil fertility. This information can provide a scientific basis for precise water and N management methods for greenhouse vegetable production. The objective of this study was to investigate the effects of optimized irrigation and nitrogen management on the dynamics of soil organic nitrogen fractions, soil properties, and crop growth. This research was conducted from 2017 to 2023 in a greenhouse vegetable field in North China. Four treatments were applied: (1) high chemical N application with furrow irrigation (farmers’ practice, FP); (2) no chemical N application with drip irrigation (DN0); (3) 50% N of FP with drip irrigation (DN1); and (4) 75% N of FP with drip irrigation (DN2). The volume in drip irrigation is 70% of that in furrow irrigation. The results showed that in 2023 (after seven years of field trials), compared with FP, the soil organic carbon (SOC), total N, and water use efficiency of the DN1 and DN2 treatments increased by 15.9%, 11.4%, and 11.3% and 7.7%, 47.2% and 44.6%, respectively. However, there was no significant difference in the total crop yield except in the DN0 treatment. Soil organic N was mostly in the form of acid-hydrolyzed N (AHN). After seven years of optimized irrigation and N management, the DN1 treatment significantly increased the content of ammonium N (AN) and amino sugar N (ASN) in AHN compared with the FP treatment. The results of further analysis demonstrated that SOC was the main factor in regulating AHN and non-hydrolyzable N (NHN), while the main regulatory factors for amino acid N (AAN) and ASN in the AHN component were dry biomass and water use efficiency, respectively. From a time scale perspective, optimization of the water and N scheduling, especially in DN1 (reducing the total irrigation volume by 30% and the amount of N applied by 50%), is crucial for the sustainable improvement of soil fertility and the maintenance of vegetable production. Full article
(This article belongs to the Section Agricultural Soils)
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<p>TN (<b>a</b>), SOC (<b>b</b>), C/N (<b>c</b>), NO<sub>3</sub><sup>−</sup>-N (<b>d</b>), pH (<b>e</b>), and EC (<b>f</b>) under various irrigation and N application rates in 2017, 2019, 2021, and 2023. Different lowercase letters mean significant differences among treatments in the same year, and different uppercase letters mean significant differences among different years of the same treatment (<span class="html-italic">p</span> &lt; 0.05). Vertical bars represent standard error of mean. DN0: no chemical N application with drip irrigation; DN1: 50% N of FP with drip irrigation; DN2: 75% N of FP with drip irrigation. TN: soil total N; SOC: soil organic carbon; C/N: the ratio of soil organic C to soil total N; NO<sub>3</sub><sup>−</sup>-N: nitrate nitrogen; pH: soil pH; EC: soil electrical conductivity.</p>
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<p>AN content (<b>a</b>), AAN content (<b>b</b>), ASN content (<b>c</b>), UN content (<b>d</b>), AHN content (<b>e</b>), and NHN content (<b>f</b>) under various irrigation and N application rates in 2017, 2019, 2021, and 2023. Different lowercase letters mean significant differences among treatments in the same year, and different uppercase letters mean significant differences among different years of the same treatment (<span class="html-italic">p</span> &lt; 0.05). Vertical bars represent standard error of mean. DN0: no chemical N application with drip irrigation; DN1: 50% N of FP with drip irrigation; DN2: 75% N of FP with drip irrigation. AN: ammonium N; AAN: amino acid N; ASN: amino sugar N; UN: hydrolyzable unknown N; AHN: acid hydrolyzed N; NHN: non-hydrolyzable N.</p>
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<p>Percentage of soil organic N fraction contents in TN in (<b>a</b>) 2017, (<b>b</b>) 2019, (<b>c</b>) 2021, and (<b>d</b>) 2023 under various irrigation and N application rates. AN: ammonium N; AAN: amino acid N; ASN: amino sugar N; UN: hydrolyzable unknown N; NHN: non-hydrolyzable N. DN0: no chemical N application with drip irrigation; DN1: 50% N of FP with drip irrigation; DN2: 75% N of FP with drip irrigation.</p>
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<p>The relative importance drivers of AN (<b>a</b>), AAN (<b>b</b>), UN (<b>c</b>), ASN (<b>d</b>), AHN (<b>e</b>), and NHN (<b>f</b>). The vertical bars represent 95% confidence intervals. SOC: soil organic carbon; TP: soil total phosphorus; EC: soil electrical conductivity; TK: soil total potassium; TDM: total dry biomass; AK: soil available potassium; TY: total yield; NO<sub>3</sub><sup>−</sup>-N: nitrate nitrogen; annual WUE: annual water use efficiency.</p>
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