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22 pages, 3602 KiB  
Article
Design and Optimization for Straw Treatment Device Using Discrete Element Method (DEM)
by Shaochuan Li, Peisong Diao, Xianghao Li, Yongli Zhao and Hongda Zhao
Agriculture 2025, 15(2), 152; https://doi.org/10.3390/agriculture15020152 (registering DOI) - 12 Jan 2025
Viewed by 76
Abstract
Due to the dense crop residue in the Huang-Huai-Hai region, challenges such as large resistance, increased power consumption, and straw backfilling arise in the process of no-till seeding under the high-speed operations. This paper presents the design of a straw treatment device to [...] Read more.
Due to the dense crop residue in the Huang-Huai-Hai region, challenges such as large resistance, increased power consumption, and straw backfilling arise in the process of no-till seeding under the high-speed operations. This paper presents the design of a straw treatment device to address these issues. The cutting edge of a straw-cutting disc is optimized using an involute curve, and the key structural parameters of the device are designed by analyzing the process of stubble cutting and clearing. In this study, the Discrete Element Method (DEM) was employed to construct models of compacted soil and hollow, flexible wheat straw, forming the foundation for a comprehensive interaction model between the tool, soil, and straw. Key experimental variables, including working speed, rotation speed, and installation centre distance, were selected. The power consumption of the straw-cutting disc (PCD) and the straw-clearing rate (SCR) were used as evaluation metrics. Response surface methodology was applied to develop regression models linking the experimental factors with the evaluation indexes using Design-Expert 12 software. Statistical significance was assessed through ANOVA (p < 0.05), and factor interactions were analyzed via response surface analysis. The optimal operational parameters were found to be a working speed of 14 km/h, a rotation speed of 339.2 rpm, and an installation centre distance of 100 cm. Simulation results closely matched the predicted values, with errors of 1.59% for SCR and 9.68% for PCD. Field validation showed an SCR of 86.12%, improved machine passability, and favourable seedling emergence. This research provides valuable insights for further parameter optimization and component development. Full article
(This article belongs to the Section Agricultural Technology)
20 pages, 1672 KiB  
Article
Enhancing Soil Health in Rice Cultivation: Optimized Zn Application and Crop Residue Management in Calcareous Soils
by Ranjan Laik, Elsaffory Bakry Awad Eltahira, Biswajit Pramanick, Nidhi, Santosh Kumar Singh and Harold van Es
Sustainability 2025, 17(2), 489; https://doi.org/10.3390/su17020489 - 10 Jan 2025
Viewed by 335
Abstract
Crop residue, a readily available biomass, is the largest source of organic matter in soil, and zinc (Zn) significantly influences microbial activity. Understanding the optimal Zn rates for enhanced biological activity in crop residue-amended soils is crucial. A study at RPCAU, Pusa, examined [...] Read more.
Crop residue, a readily available biomass, is the largest source of organic matter in soil, and zinc (Zn) significantly influences microbial activity. Understanding the optimal Zn rates for enhanced biological activity in crop residue-amended soils is crucial. A study at RPCAU, Pusa, examined the combined effects of Zn applications and long-term crop residue amendments on soil biological properties in a rice–wheat cropping system. Conducted on Zn-deficient calcareous soil, the experiment used a split-plot design with four crop residue levels (0, 25, 50, and 100%) and four Zn rates (0, 2.5, 5, and 10 kg ha−1). Crop residues were incorporated each season, while Zn was applied initially in 1994 and again in 2018. The results showed significant improvements in soil organic carbon, organic C-stock, and reductions in soil bulk density. A linear–plateau regression model revealed that Zn application at 10 kg ha−1 increased soil active carbon and soil respiration by 35% and 53%, respectively, with the required crop residue levels at 73.73% and 90.28%. ACE protein increased by 9.6% with Zn application at 5 kg ha−1, with a required crop residue level of 91.06%. The highest values of soil available nutrients and grain yield of rice were observed with 100% residue incorporation and 10 kg ha−1 Zn application. Thus, applying 10 kg ha−1 Zn along with 100% crop residue incorporation significantly improves soil biological properties and soil organic carbon levels in calcareous soil under a rice–wheat cropping system. Full article
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Figure 1
<p>Effect of crop residue management and Zn application on (<b>a</b>) soil active carbon, (<b>b</b>) soil respiration, and (<b>c</b>) ACE protein in upland calcareous soils [CR<sub>1</sub>, CR<sub>2</sub>, CR<sub>3</sub>, and CR<sub>4</sub> are 0%, 25%, 50%, and 100% crop residue levels, respectively, and Zn<sub>1</sub>, Zn<sub>2</sub>, Zn<sub>3</sub>, and Zn<sub>4</sub> are 0, 2.5, 5.0, and 10 kg ha<sup>−1</sup> zinc application rates, respectively; line above the bar represents standard error].</p>
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<p>(<b>a</b>–<b>d</b>) The linear–plateau regression model for CR and active carbon with (<b>a</b>) no Zn application, (<b>b</b>) Zn @ 2.5 kg ha<sup>−1</sup>, (<b>c</b>) Zn @ 5 kg ha<sup>−1</sup>, and (<b>d</b>) Zn @ 10 kg ha<sup>−1</sup>.</p>
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<p>(<b>a</b>–<b>d</b>) The linear–plateau regression model for CR and active carbon with (<b>a</b>) no Zn application, (<b>b</b>) Zn @ 2.5 kg ha<sup>−1</sup>, (<b>c</b>) Zn @ 5 kg ha<sup>−1</sup>, and (<b>d</b>) Zn @ 10 kg ha<sup>−1</sup>.</p>
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<p>(<b>a</b>–<b>d</b>) The linear–plateau regression model for CR and soil respiration with (<b>a</b>) no Zn application, (<b>b</b>) Zn @ 2.5 kg ha<sup>−1</sup>, (<b>c</b>) Zn @ 5 kg ha<sup>−1</sup>, and (<b>d</b>) Zn @ 10 kg ha<sup>−1</sup>.</p>
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<p>(<b>a</b>–<b>d</b>) The linear–plateau regression model for CR and ACE protein with (<b>a</b>) no Zn application, (<b>b</b>) Zn @ 2.5 kg ha<sup>−1</sup>, (<b>c</b>) Zn @ 5 kg ha<sup>−1</sup>, and (<b>d</b>) Zn @ 10 kg ha<sup>−1</sup>.</p>
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30 pages, 5546 KiB  
Article
A Simple Drainage-Friendly Approach for Estimating Waterlogging Impacts on Cotton Yields Regarding Accompanying High Temperatures
by Long Qian, Yunying Luo and Kai Duan
Sustainability 2025, 17(2), 474; https://doi.org/10.3390/su17020474 - 9 Jan 2025
Viewed by 288
Abstract
Due to climate change, cotton production is extensively restricted by waterlogging, especially under accompanying high temperatures. Yield production functions are powerful tools in agricultural water management, but there is a lack of consideration for crop dynamic growth and the impact of accompanied high [...] Read more.
Due to climate change, cotton production is extensively restricted by waterlogging, especially under accompanying high temperatures. Yield production functions are powerful tools in agricultural water management, but there is a lack of consideration for crop dynamic growth and the impact of accompanied high temperatures during waterlogging. In this work, to simulate cotton yields under waterlogging regarding accompanying high temperatures, a comprehensive stress index was proposed, and a dynamic yield production function model was accordingly developed. The model was calibrated and evaluated by using multi-year and multi-site experimental data in the Hubei Province of China, and, then, it was applied under various waterlogging scenarios. The results showed that including the impact of accompanying high temperatures can effectively improve model performance, and the temperature threshold for triggering this additional impact was 30 °C. The dynamic model exhibited satisfactory performance during both calibration and evaluation, with low relative mean absolute error values (RMAE = 12.12% and 21.51%) and low coefficient of residual mass values (CRM = -0.028 and 0.063). According to model simulations, even under the same amount of excessive water, yield losses can vary from 3.90% to 33.93% due to different waterlogging timings and air temperature conditions. In summary, the present model is a convenient and powerful tool for crop drainage schedules and sustainable agriculture under global climate change. Full article
(This article belongs to the Section Sustainable Agriculture)
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<p>Locations and images of the two employed cotton waterlogging experiments. Experiment 1 and Experiment 2 conducted in Jingzhou city and Wuhan city in Hubei Province, respectively.</p>
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<p>Precipitation and air temperature during the experimental years in the two experiments. Precipitation represents the accumulated values over all experimental years; daily temperature represents the averaged values over all experimental years.</p>
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<p>Precipitation and air temperature during the experimental years in the two experiments. Precipitation represents the accumulated values over all experimental years; daily temperature represents the averaged values over all experimental years.</p>
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<p>A flow chart of the present work.</p>
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<p>Schematic of the effective utilization range of soil aeration during an assumed 10-day waterlogging event. The stepped black line indicates the daily water level. The effective utilization range of soil aeration is illustrated by the blue dotted box. Z<sub>up</sub> and Z<sub>cr</sub> (m) are the upper and lower water level boundaries of the effective utilization range of soil aeration, respectively.</p>
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<p>The curves of the crop dry matter growth response to waterlogging stress.</p>
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<p>Schematic of the effective utilization range of high temperatures during an assumed 10-day waterlogging event. The lower portion of this figure is derived from <a href="#sustainability-17-00474-f004" class="html-fig">Figure 4</a>. The stepped black lines in the upper portion and the lower portion of this figure indicate the daily maximum temperatures and daily water levels, respectively.</p>
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<p>The seed cotton yield outcomes of the dynamic YPFW (<b>a</b>) and dynamic YPFW (-HT) (<b>b</b>) after calibration.</p>
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<p>The dynamic cotton dry matter accumulations over time under different cases of waterlogging stress in 2010 during calibration. ‘S3W7’ indicates 3-day submergence followed by 7-day waterlogging. The suffixes ‘-Budding’, ‘-Flowering’, ‘-Boll-opening’, and ‘-Multiple’ indicate that the waterlogging treatments were established at the budding, flowering, boll-opening, and multiple stages, respectively.</p>
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<p>The seed cotton yield outcomes of the dynamic YPFW and dynamic YPFW(-HT) in the top 15% accompanying high-temperature cases of the calibration dataset. The ‘accompanying high-temperature’ feature was quantified by the number of days with accompanying high temperatures (daily maximum temperature &gt;30 °C). As a result, all the cases shown in this figure correspond to the accompanying high-temperature days of more than 8 days.</p>
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<p>The seed cotton yield outcomes of the dynamic YPFW (<b>a</b>) and the dynamic YPFW(-HT) (<b>b</b>) during evaluation.</p>
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<p>The seed cotton yield outcomes of the dynamic YPFW and dynamic YPFW (-HT) in the top 15% accompanying high-temperature cases of the evaluation dataset (i.e., the data displayed in <a href="#sustainability-17-00474-f010" class="html-fig">Figure 10</a>). The ‘accompanying high-temperature’ feature was quantified by the accompanying high-temperature days (daily maximum temperature &gt;30 °C, as preciously calibrated). All the cases shown in this figure corresponds to the accompanying high-temperature days of more than 20 days.</p>
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<p>The dynamic cotton dry matter accumulations over time under waterlogging stress at the beginning of the flowering stage: (<b>a</b>,<b>b</b>) describe the dynamic dry matter accumulations under 25 °C and 33 °C, respectively. The potential dry matter accumulation curve (Equation (2)) has very limited influence on the results of our assumed cases; thus, it was randomly selected from Experiment 2; this curve is illustrated by ‘Potential’ in this figure. The arrows indicate the timing of the imposing waterlogging events. All of the above explanations are applicable to <a href="#sustainability-17-00474-f013" class="html-fig">Figure 13</a>.</p>
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<p>The dynamic cotton dry matter accumulations over time under waterlogging stress at the end of the flowering stage: (<b>a</b>,<b>b</b>) describe the dynamic dry matter accumulations under 25 °C and 33 °C, respectively. The arrows represent waterlogging timings.</p>
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22 pages, 5112 KiB  
Article
Parameter Calibration Method for Discrete Element Simulation of Soil–Wheat Crop Residues in Saline–Alkali Coastal Land
by Jie Liu, Tong Lu, Shuai Zheng, Yu Tian, Miaomiao Han, Muhao Tai, Xiaoning He, Hongxiu Li, Dongwei Wang and Zhuang Zhao
Agriculture 2025, 15(2), 129; https://doi.org/10.3390/agriculture15020129 - 9 Jan 2025
Viewed by 282
Abstract
After wheat harvesting in coastal saline–alkali land, when the straw is returned to the field and the soil is rotary tilled, the lack of reliable discrete element simulation parameter models restricts the optimization and improvement of special tillage and land preparation equipment for [...] Read more.
After wheat harvesting in coastal saline–alkali land, when the straw is returned to the field and the soil is rotary tilled, the lack of reliable discrete element simulation parameter models restricts the optimization and improvement of special tillage and land preparation equipment for saline–alkali land to some extent. In this study, the Hertz–Mindlin with JKR model was used to calibrate the discrete element simulation parameters. Taking the soil-wheat crop residue mixture’s angle of repose as the test index, four groups of parameters that significantly affect the angle of repose and their optimal value ranges were screened out through the Plackett–Burman test and the steepest ascent test. Then, the Box–Behnken test was conducted to obtain the quadratic regression model of the significant parameters and the angle of repose, and the optimal values of the significant parameters were obtained. The optimal parameter combination was used for simulation tests, and the relative errors between the measured values and the simulation test values of the angle of repose and the wheat residue coverage rate were 0.74% and 1.34%. The reliable parameters provide a theoretical basis for the optimization and improvement of the equipment for soil preparation in saline–alkali land. Full article
(This article belongs to the Section Agricultural Soils)
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<p>Soil moisture content and particle size distribution test. (<b>a</b>) Soil moisture determination; (<b>b</b>) soil screening test.</p>
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<p>Direct shear test on soil. (<b>a</b>) ZJ-1B strain-controlled direct shear instrument; (<b>b</b>) test soil sample.</p>
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<p>Curves of soil shear stress and shear displacement.</p>
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<p>Mohr–Coulomb strength envelope diagram.</p>
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<p>Test of wheat crop residue density.</p>
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<p>Test for determining the soil static friction coefficient. (<b>a</b>) Test bench for the determination of the static friction factor; (<b>b</b>) Schematic diagram of static friction factor measurement test. 1. High-precision universal level; 2. test sample; 3. test sample container; 4. substrate; 5. handle adjustment.</p>
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<p>Schematic diagram of rolling friction factor measurement test.</p>
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<p>Collision recovery coefficient measurement test. (<b>a</b>) Test bench for determination of collision recovery coefficient; (<b>b</b>) Schematic diagram of collision recovery coefficient measurement.</p>
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<p>Measurement principle of angle of repose.</p>
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<p>Response surface diagram of the interaction between parameters that affect the angle of repose. (<b>a</b>) Response surface diagram of the interaction of X<sub>1</sub> and X<sub>4</sub> factors on the angle of repose. (<b>b</b>) Response surface diagram of the interaction of X<sub>1</sub> and X<sub>7</sub> factors on the angle of repose. (<b>c</b>) Response surface diagram of interaction between X<sub>1</sub> and X<sub>10</sub> factors on angle of repose. (<b>d</b>) Response surface diagram of the interaction of X<sub>4</sub> and X<sub>7</sub> factors on the angle of repose. (<b>e</b>) Response surface diagram of the interaction of X<sub>4</sub> and X<sub>10</sub> factors with respect to the angle of repose. (<b>f</b>) Response surface diagram of X<sub>7</sub> and X<sub>10</sub> factors interacting with the angle of repose.</p>
Full article ">Figure 10 Cont.
<p>Response surface diagram of the interaction between parameters that affect the angle of repose. (<b>a</b>) Response surface diagram of the interaction of X<sub>1</sub> and X<sub>4</sub> factors on the angle of repose. (<b>b</b>) Response surface diagram of the interaction of X<sub>1</sub> and X<sub>7</sub> factors on the angle of repose. (<b>c</b>) Response surface diagram of interaction between X<sub>1</sub> and X<sub>10</sub> factors on angle of repose. (<b>d</b>) Response surface diagram of the interaction of X<sub>4</sub> and X<sub>7</sub> factors on the angle of repose. (<b>e</b>) Response surface diagram of the interaction of X<sub>4</sub> and X<sub>10</sub> factors with respect to the angle of repose. (<b>f</b>) Response surface diagram of X<sub>7</sub> and X<sub>10</sub> factors interacting with the angle of repose.</p>
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<p>Soil angle of repose test. (<b>a</b>) Field test; (<b>b</b>) simulation test.</p>
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<p>Particle bed for simulation test.</p>
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<p>Surface roughness simulation cross-section.</p>
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<p>Simulation cross-section of wheat crop residue coverage. (<b>a</b>) Particle bed surface after simulation; (<b>b</b>) grain distribution of wheat crop residues after soil particles were hidden.</p>
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<p>Field trial.</p>
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17 pages, 6383 KiB  
Article
Potential of Cover Crop Use and Termination with a Roller-Crimper in a Strip-Till Silage Maize (Zea mays L.) Production System in the Central Valley of California
by Robert Willmott, Jennifer Valdez-Herrera, Jeffrey P. Mitchell and Anil Shrestha
Agronomy 2025, 15(1), 132; https://doi.org/10.3390/agronomy15010132 - 7 Jan 2025
Viewed by 299
Abstract
The potential of terminating cover crops with a roller-crimper is of increasing interest. A two-year (2020/21 and 2021/22) study was conducted in Fresno, CA, USA. Five cover crop treatments (rye (Secale cereale L.) alone, ultra-high diversity mix, multiplex cover crop mix, fava [...] Read more.
The potential of terminating cover crops with a roller-crimper is of increasing interest. A two-year (2020/21 and 2021/22) study was conducted in Fresno, CA, USA. Five cover crop treatments (rye (Secale cereale L.) alone, ultra-high diversity mix, multiplex cover crop mix, fava bean (Vicia faba L.) + phacelia (Phacelia tanacetifolia Benth.), and rye + field pea (Pisum sativum L.) + purple vetch (Vicia americana Muhl. Ex Willd.)) were planted in November, roller-crimped in April, and silage maize (Zea mays L.) was strip-till planted in the residue in May. Cover crop kill, soil cover by residue, weed cover, amount of organic residue, and silage maize yield were recorded. The roller-crimper resulted in 95 to 100% kill of the cover crops. Soil cover at maize canopy closure (mid-July) was approximately 90% in the rye plots while it was 30 to 70% in the other treatments. The fava bean + phacelia cover crop disintegrated the most rapidly. Weed cover was <5% in all the treatments until maize canopy closure. The cover crops added 6.7 to 14 MT ha−1 of residue. Maize silage yield was similar across the treatments. Therefore, in this study, cover crops were successfully terminated by the roller-crimper, allowing successful strip-till establishment and production of silage maize. Full article
(This article belongs to the Section Farming Sustainability)
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<p>Average monthly precipitation (in mm) during the experimental period in 2020/2021 and 2021/2022.</p>
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<p>Average monthly maximum and minimum temperatures (in degree Celsius) during the experimental period in 2020/2021 and 2021/2022.</p>
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<p>Termination of the cover crops in the treatment plots with a 15 ft wide rear-mounted roller-crimper.</p>
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<p>Maize planting in the strip-tilled rows with a GPS equipped John Deere 7300 Max Emerge 2 vacuum planter<sup>®</sup> (John Deere and Co., Moline, IL, USA).</p>
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<p>View of the treatment plots showing the very patchy bare spots and weed populations in the inter-row spaces.</p>
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<p>Treatment plots in mid-June in the preliminary study. ‘Trophy’ rape + ‘Tillage’ radish + phacelia (<b>top left</b>); bell bean + pea (<b>top right</b>); rye + purple vetch + bell bean (<b>bottom left</b>); multiplex mix (<b>bottom right</b>). (Photo: Lynn Sosnoskie).</p>
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<p>A picture of the plots taken on 4 April 2021 immediately after roller-crimping.</p>
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<p>Percent kill of the cover crops in the treatments after termination with the roller-crimper (average of 2020/21 and 2021/22) in different times of the season. Bars with the same letters at each evaluation time are not significantly different according to the Fisher’s Least Significant Difference (LSD) test at 0.05 level.</p>
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<p>Visuals of the kill of the cover crops after termination with the roller-crimper. Picture taken on 6 June 2021 (<b>left</b>) and on 29 June 2021 (<b>right</b>).</p>
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<p>Percent soil cover from the cover crop residues in the treatments after termination with the roller-crimper (average of 2020/21 and 2021/22) in different times of the season. Bars with the same letters for each evaluation time are not significantly different according to the Fisher’s Least Significant Difference (LSD) test at 0.05 level.</p>
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<p>Average (2020/21 and 2021/22) aboveground dry biomass (±SE) in the treatments after cover crop termination. Bars with the same letters for each evaluation time are not significantly different according to the Fisher’s Least Significant Difference (LSD) test at 0.05 level.</p>
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<p>Percent weed in the treatment plots after termination with the roller-crimper (average of 2020/21 and 2021/22) in different times of the season. Bars with the same letters for each evaluation time are not significantly different according to the Fisher’s Least Significant Difference (LSD) test at 0.05 level.</p>
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<p>Silage maize yield (average of 2020/21 and 2021/22 in metric tons per hectare) in the different treatments. Silage maize yield from four random areas of the adjacent standard conventional field is also presented for comparative purposes. There were no differences between the treatments at a 0.05 level of significance.</p>
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18 pages, 2784 KiB  
Article
Bacterial Isolation from Natural Grassland on Nitrogen-Free Agar Yields Many Strains Without Nitrogenase
by Amrit Koirala, Nabilah Ali Alshibli, Bikram K. Das and Volker S. Brözel
Microorganisms 2025, 13(1), 96; https://doi.org/10.3390/microorganisms13010096 - 6 Jan 2025
Viewed by 441
Abstract
Nitrogen inputs for sustainable crop production for a growing population require the enhancement of biological nitrogen fixation. Efforts to increase biological nitrogen fixation include bioprospecting for more effective nitrogen-fixing bacteria. As bacterial nitrogenases are extremely sensitive to oxygen, most primary isolation methods rely [...] Read more.
Nitrogen inputs for sustainable crop production for a growing population require the enhancement of biological nitrogen fixation. Efforts to increase biological nitrogen fixation include bioprospecting for more effective nitrogen-fixing bacteria. As bacterial nitrogenases are extremely sensitive to oxygen, most primary isolation methods rely on the use of semisolid agar or broth to limit oxygen exposure. Without physical separation, only the most competitive strains are obtained. The distance between strains provided by plating on solid media in reduced oxygen environments has been found to increase the diversity of culturable potential diazotrophic bacteria. To obtain diverse nitrogen-fixing isolates from natural grasslands, we plated soil suspensions from 27 samples onto solid nitrogen-free agar and incubated them under atmospheric and oxygen-reducing conditions. Putative nitrogen fixers were confirmed by subculturing in liquid nitrogen-free media and PCR amplification of the nifH genes. Streaking of the 432 isolates on nitrogen-rich R2A revealed many cocultures. In most cases, only one community member then grew on NFA, indicating the coexistence of nonfixers in coculture with fixers when growing under nitrogen-limited conditions. To exclude isolates able to scavenge residual nitrogen, such as that from vitamins, we used a stringent nitrogen-free medium containing only 6.42 μmol/L total nitrogen and recultured them in a nitrogen-depleted atmosphere. Surprisingly, PCR amplification of nifH using various primer pairs yielded amplicons from only 17% of the 442 isolates. The majority of the nifH PCR-negative isolates were Bacillus and Streptomyces. It is unclear whether these isolates have highly effective uptake systems or nitrogen reduction systems that are not closely aligned with known nitrogenase families. We advise caution in determining the nitrogen fixation ability of plants from growth on nitrogen-free media, even where the total nitrogen is very limited. Full article
(This article belongs to the Special Issue Microbial Nitrogen Cycle)
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<p>Diversity of isolates obtained on NFA incubated aerobically or under reduced oxygen conditions; (<b>a</b>) flow chart illustrating the isolation and processing of putative nitrogen-fixing bacteria from Sioux Prairie soil samples; (<b>b</b>) culturable counts on NFA incubated under aerobic and reduced oxygen conditions at 28 °C for 14 d, with letters indicating significant differences as determined by Duncan’s new multiple range test; and (<b>c</b>) colony characteristics of an isolate on R2A (left) and NFA (right).</p>
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<p>Diversity of purified isolates determined by the V1–V3 region of 16S rRNA sequences; (<b>a</b>) shown at the phylum/class level, and (<b>b</b>) at the genus level.</p>
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<p>(<b>a</b>) Molecular phylogenetic analysis of partial 16S rRNA sequences (V1–V3) obtained from potential diazotrophic isolates via maximum likelihood in PhyML via the aBayes analysis method. Branch tips are colored according to the genus classification, and unclassified sequences are not presented. The red stars on the outer ring indicate isolates that yielded <span class="html-italic">nifH</span> sequences by PCR. (<b>b</b>) Distribution of potential diazotrophs compared to <span class="html-italic">nifH</span>-positive isolates, shown by incubation conditions.</p>
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14 pages, 1074 KiB  
Review
Impacts of Biochar Application on Inorganic Phosphorus Fractions in Agricultural Soils
by Liwen Lin, Yutao Peng, Lin Zhou, Baige Zhang, Qing Chen and Hao Chen
Agriculture 2025, 15(1), 103; https://doi.org/10.3390/agriculture15010103 - 5 Jan 2025
Viewed by 406
Abstract
Inorganic phosphorus (P) is a key component of soil P pools, influencing their availability and mobility. Although studies on biochar’s effect on inorganic P fractions in various soils are growing, a critical review of these findings is lacking. Herein, we conducted a quantitative [...] Read more.
Inorganic phosphorus (P) is a key component of soil P pools, influencing their availability and mobility. Although studies on biochar’s effect on inorganic P fractions in various soils are growing, a critical review of these findings is lacking. Herein, we conducted a quantitative meta-analysis of 74 peer-reviewed datasets, drawing general conclusions and confirming the absence of publication bias through funnel plot statistics. The results showed that biochars can influence soil inorganic P fractions, with their effects depending on biochar (i.e., feedstock, pyrolysis temperature and time, C:N ratio, pH, ash and P content) and soil-related properties (i.e., pH, texture, P content). Specifically, the addition of biochar significantly enhanced the diverse soil inorganic P fractions and P availability (as indicated by Olsen-P). Only biochars produced from wood residues and having high C/N ratios (>200) did not significantly increase the labile P fractions (water extracted soil phosphorus (H2O-P), Olsen-P, and soil calcium compounds bound phosphorus (Ca2-P)). The application of biochars derived from crop residues significantly increased the soil P associated with iron and aluminum oxides, while there was no significant effect on manure- and wood residue-derived biochars. In addition, applications of low temperature biochars and manure residue-derived biochars could increase the proportions of soil highly stable P. We identified knowledge gaps in biochar production and its potential for soil phosphorus regulation. Due to the complex processes by which biochar affects soils, more systematic evaluations and predictive methods (e.g., modeling, machine learning) are needed to support sustainable agriculture and environmental practices. Full article
(This article belongs to the Special Issue Feature Review in Agricultural Soils—Intensification of Soil Health)
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<p>Grand mean of all cases for diverse inorganic P fractions when biochar was applied regardless of experimental conditions.</p>
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<p>Effect of explanatory variables on content of Olsen-P (<b>a</b>), H<sub>2</sub>O-P (<b>b</b>), Al-P (<b>c</b>), Fe-P (<b>d</b>), Ca<sub>2</sub>-P (<b>e</b>), Ca<sub>8</sub>-P (<b>f</b>), Ca<sub>10</sub>-P (<b>g</b>) and R-P (<b>h</b>) in soil. Symbols indicate the mean % change in effect size with 95% confidence interval. The number after the name of group indicates the amount of pairwise comparison. The red dotted line indicates the zero line. Orange, blue, and grey dots represent the positive, negative, and no significant effects.</p>
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12 pages, 2650 KiB  
Article
A Sensitive and Selective Electrochemical Aptasensor for Carbendazim Detection
by Suthira Pushparajah, Mahnaz Shafiei and Aimin Yu
Biosensors 2025, 15(1), 15; https://doi.org/10.3390/bios15010015 - 3 Jan 2025
Viewed by 335
Abstract
Carbendazim (CBZ) is used to prevent fungal infections in agricultural crops. Given its high persistence and potential for long-term health effects, it is crucial to quickly identify pesticide residues in food and the environment in order to mitigate excessive exposure. Aptamer-based sensors offer [...] Read more.
Carbendazim (CBZ) is used to prevent fungal infections in agricultural crops. Given its high persistence and potential for long-term health effects, it is crucial to quickly identify pesticide residues in food and the environment in order to mitigate excessive exposure. Aptamer-based sensors offer a promising solution for pesticide detection due to their exceptional selectivity, design versatility, ease of use, and affordability. Herein, we report the development of an electrochemical aptasensor for CBZ detection. The sensor was fabricated through a one-step electrodeposition of platinum nanoparticles (Pt NPs) and reduced graphene oxide (rGO) on a glassy carbon electrode (GCE). Then, a CBZ-specific aptamer was attached via Pt-sulfur bonds. Upon combining CBZ with the aptamer on the electrode surface, the redox reaction of the electrochemical probe K4[Fe(CN)6] is hindered, resulting in a current drop. Under optimized conditions (pH of 7.5 and 25 min of incubation time), the proposed aptasensor showed a linear current reduction to CBZ concentrations between 0.5 and 15 nM. The limit of detection (LOD) for this proposed aptasensor is 0.41 nM. Along with its repeatable character, the aptasensor demonstrated better selectivity for CBZ compared to other potential compounds. The recovery rates for detecting CBZ in skim milk and tap water using the standard addition method were 98% and 96%, respectively. The proposed aptasensor demonstrated simplicity, sensitivity, and selectivity for detecting CBZ with satisfactory repeatability. It establishes a strong foundation for environmental monitoring of CBZ. Full article
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<p>Schematic illustration of the preparation of the electrochemical aptasensor for CBZ detection.</p>
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<p>SEM images of (<b>A</b>) Pt-rGO/GCE and (<b>B</b>) Apt-Pt-rGO/GCE. XPS spectra of (<b>C</b>) wide scan of Apt-Pt-rGO/GCE, and (<b>D</b>) Peak binding energy shift of Pt 4f (a) before and (b) after aptamer immobilization.</p>
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<p>(<b>A</b>) CV plots and (<b>B</b>) Nyquist diagrams of EIS of (a) bare GCE, (b) Pt-rGO/GCE, and (c) Apt-Pt-rGO/GCE in a 0.1 M KCl solution containing 1.0 mM K<sub>4</sub>[Fe(CN)<sub>6</sub>].</p>
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<p>(<b>A</b>) DPVs of 1.0 mM K<sub>4</sub>[Fe(CN)<sub>6</sub>] at the aptasensor before and after adding 4 nM and 10 nM of CBZ in pH 7.0 PBS containing 0.1 M KCl. The effects of (<b>B</b>) incubation time (pH fixed at 7.0) and (<b>C</b>) pH (incubation time fixed at 25 min) on the CBZ current response.</p>
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<p>(<b>A</b>) DPV responses of the aptasensor toward CBZ with different concentrations (0, 0.5, 1, 2, 4, 6, 8, 10, and 15 nM) in pH 7.5 PBS containing 1.0 mM K<sub>4</sub>[Fe(CN)<sub>6</sub>] and 0.1 M KCl. (<b>B</b>) Linear curve of ΔI vs. CBZ concentration (nM).</p>
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<p>(<b>A</b>) Selectivity performance of the aptasensor in 10 nM of ciprofloxacin, acetaminophen, ascorbic acid, glucose, NaCl, KI, KNO<sub>3</sub>, and (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub> in pH 7.5 PBS containing 1.0 mM K<sub>4</sub>[Fe(CN)<sub>6</sub>]. (<b>B</b>) Repeatability of the aptasensor in five samples containing 15 nM CBZ. (<b>C</b>) Current response of the aptasensor to 2 nM of CBZ when kept at 4 °C for 0, 7, 14, and 21 days.</p>
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22 pages, 12737 KiB  
Article
Potato Plant Variety Identification Study Based on Improved Swin Transformer
by Xue Xing, Chengzhong Liu, Junying Han, Quan Feng, Enfang Qi, Yaying Qu and Baixiong Ma
Agriculture 2025, 15(1), 87; https://doi.org/10.3390/agriculture15010087 - 2 Jan 2025
Viewed by 271
Abstract
Potato is one of the most important food crops in the world and occupies a crucial position in China’s agricultural development. Due to the large number of potato varieties and the phenomenon of variety mixing, the development of the potato industry is seriously [...] Read more.
Potato is one of the most important food crops in the world and occupies a crucial position in China’s agricultural development. Due to the large number of potato varieties and the phenomenon of variety mixing, the development of the potato industry is seriously affected. Therefore, accurate identification of potato varieties is a key link to promote the development of the potato industry. Deep learning technology is used to identify potato varieties with good accuracy, but there are relatively few related studies. Thus, this paper introduces an enhanced Swin Transformer classification model named MSR-SwinT (Multi-scale residual Swin Transformer). The model employs a multi-scale feature fusion module in place of patch partitioning and linear embedding. This approach effectively extracts features of various scales and enhances the model’s feature extraction capability. Additionally, the residual learning strategy is integrated into the Swin Transformer block, effectively addressing the issue of gradient disappearance and enabling the model to capture complex features more effectively. The model can better capture complex features. The enhanced MSR-SwinT model is validated using the potato plant dataset, demonstrating strong performance in potato plant image recognition with an accuracy of 94.64%. This represents an improvement of 3.02 percentage points compared to the original Swin Transformer model. Experimental evidence shows that the improved model performs better and generalizes better, providing a more effective solution for potato variety identification. Full article
(This article belongs to the Section Digital Agriculture)
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<p>Selected samples of potato plants, where (<b>a</b>–<b>d</b>) are blue background images and (<b>e</b>,<b>f</b>) are natural background images.</p>
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<p>Example of data preprocessing.</p>
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<p>Swin Transformer model structure.</p>
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<p>Patch Partition and Linear Embedding process.</p>
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<p>Patch Merging operation process.</p>
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<p>Swin Transformer Block structure.</p>
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<p>MSFF module structure.</p>
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<p>Residual structure.</p>
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<p>MSR-SwinT overall structure.</p>
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<p>Improved Swin Transformer Block.</p>
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<p>Training accuracy curves for different learning rates.</p>
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<p>MSR-SwinT model visualization results.</p>
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<p>Accuracy curve and loss curve of validation set of different models. (<b>a</b>) Accuracy curve, (<b>b</b>) Loss curve.</p>
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<p>Confusion matrix for different models.</p>
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18 pages, 1442 KiB  
Article
Coffee and Cocoa By-Products as Valuable Sources of Bioactive Compounds: The Influence of Ethanol on Extraction
by Blanca Martínez-Inda, Nerea Jiménez-Moreno, Irene Esparza and Carmen Ancín-Azpilicueta
Antioxidants 2025, 14(1), 42; https://doi.org/10.3390/antiox14010042 - 1 Jan 2025
Viewed by 485
Abstract
Cocoa and coffee are two of the world’s most important crops. Therefore, their by-products are generated in large quantities. This work proposes a simple method for the valorization of these residues by obtaining phenolic compounds and melanoidins by solid–liquid extraction using different hydroalcoholic [...] Read more.
Cocoa and coffee are two of the world’s most important crops. Therefore, their by-products are generated in large quantities. This work proposes a simple method for the valorization of these residues by obtaining phenolic compounds and melanoidins by solid–liquid extraction using different hydroalcoholic solutions as extracting solvents (0, 25, 50, 75, 100% ethanol). Extracts of both by-products presented the highest antioxidant capacity and total phenolic and melanoidin content when using 50–75% ethanol in the solvent. Among all the extracts, those obtained from spent coffee grounds at 75% ethanol showed the highest concentrations of total phenolic compounds (13.5 ± 1.3 mmol gallic acid equivalents/g dry matter) and melanoidins (244.4 ± 20.1 mg/g dry matter). Moreover, the sun protection factor values of the coffee extracts obtained with 50 and 75% of ethanol as extraction solvent (7.8 ± 0.9 and 8.5 ± 0.7, respectively) showed their potential for use in the cosmetic sector. The most important phenolic compounds identified in the coffee by-products extracts were phenolic acids, and most of them were found in higher concentration in extracts obtained with lower percentages of ethanol (0–25%). Protocatechuic acid was the most abundant phenolic in cocoa extracts, with concentrations ranging from 18.49 ± 2.29 to 235.35 ± 5.55 µg/g dry matter, followed by 4-hydroxybenzoic acid, (-)-epicatechin and (+)-catechin. Esculetin was found in both coffee and cocoa extracts, which had not been reported to date in these residues. In summary, the use of 75% ethanol as an extraction solvent seems a good strategy to obtain extracts rich in phenolic compounds from food by-products rich in melanoidins, such as coffee and cocoa by-products. The high antioxidant potential of these extracts makes them of great interest for the cosmetic and nutraceutical industries. Full article
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<p>Antioxidant activity (mmol Trolox/g dry matter) of cocoa bean shell (CBS) and spent coffee grounds (SCG) extracts measured by three methods (DPPH, FRAP and ABTS). Different letters indicate significant differences according to the extracting solvent (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Sun Protection Factor (SPF) at a concentration of 0.2 mg/mL of cocoa bean shell (CBS) and spent coffee grounds (SCG) extracts. Different letters indicate significant difference using Kruskal–Wallis test with Bonferroni correction at 0.01 level of significance (<span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Heatmap of the phenolic compounds identified by HPLC-MS/MS using the peak areas relative to mg of dry matter in CBS (<b>A</b>) and SCG (<b>B</b>) extracts. The more intense the red color, the greater the signal intensity of the corresponding phenolic compound.</p>
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15 pages, 9175 KiB  
Article
Development and Characterization of Biodegradable, Binderless Fiberboards from Eggplant Straw Fibers
by Hailun Fan, Xiulun Wang, Tingting Wu, Jianzhong Sun and Jun Liu
Materials 2025, 18(1), 37; https://doi.org/10.3390/ma18010037 - 25 Dec 2024
Viewed by 297
Abstract
Currently, wood-based panels are mainly made from wood and adhesives containing formaldehyde. With the growing demand for raw materials and increasing concern for human health, the use of residues from annual crops to manufacture binder-free biodegradable biomass boards has attracted increasing interest. The [...] Read more.
Currently, wood-based panels are mainly made from wood and adhesives containing formaldehyde. With the growing demand for raw materials and increasing concern for human health, the use of residues from annual crops to manufacture binder-free biodegradable biomass boards has attracted increasing interest. The aim of this study was to develop a biodegradable bio-board without any adhesives using eggplant straw fibers. The bio-boards were produced via simple mechanical refinement of eggplant straw fibers and were formed under pressures of 2.0 MPa, 3.5 MPa, 5.0 MPa, 6.5 MPa, and 8.0 MPa. The mechanical properties and dimensional stability of the manufactured bio-boards were evaluated. With increasing applied pressure, the bending rupture stress of the bio-boards increased from 27.69 MPa to 45.29 MPa, the tensile rupture stress varied from 12.45 MPa to 24.62 MPa, the water absorption decreased from 91.45% to 88.29%, and the contact angle increased from 89.67° to 90.45°. The bio-boards were subjected to morphological analysis (SEM) and porosity and crystallinity measurements (XRD), and the results indicated that the water absorption of the bio-boards was due to a combination of porosity and crystallinity. The results showed that eggplant straw is suitable for manufacturing bio-boards. Full article
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<p>Making process of bio-board.</p>
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<p>(<b>a</b>–<b>e</b>) Surface images of bio-board samples produced at 2.0–8.0 MPa, (<b>f</b>–<b>j</b>) Cross-sectional images of bio-board samples produced at 2.0–8.0 MPa.</p>
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<p>XRD results and crystallinity of bio-board samples produced at different pressures (<span class="html-italic">p</span> &lt; 0.05). (<b>a</b>) XRD results of bio-board samples; (<b>b</b>) crystallinity of bio-boards.</p>
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<p>Density, moisture content, and porosity of bio-board samples produced at different pressures. (<span class="html-italic">p</span> &lt; 0.05) (<b>a</b>) Density and moisture content; (<b>b</b>) porosity.</p>
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<p>Rupture stress and screw holding force of bio-board samples produced at different pressures. (<b>a</b>) Bending rupture stress and tensile rupture stress; (<b>b</b>) screw holding force. (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>WA, TS, LE, and contact angle of bio-board samples produced at different pressures. (<b>a</b>) Water absorption (WA), thickness swelling (TS), and linear expansion (LE); (<b>b</b>) contact angle. (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Correlation analysis between water absorption, porosity, and non-crystallinity.</p>
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14 pages, 4808 KiB  
Article
From Crop Residue to Corrugated Core Sandwich Panels as a Building Material
by Aadarsha Lamichhane, Arun Kuttoor Vasudevan, Mostafa Mohammadabadi, Kevin Ragon, Jason Street and Roy Daniel Seale
Materials 2025, 18(1), 31; https://doi.org/10.3390/ma18010031 - 25 Dec 2024
Viewed by 436
Abstract
This study explores the potential of using underutilized materials from agricultural and forestry systems, such as rice husk, wheat straw, and wood strands, in developing corrugated core sandwich panels as a structural building material. By leveraging the unique properties of these biobased materials [...] Read more.
This study explores the potential of using underutilized materials from agricultural and forestry systems, such as rice husk, wheat straw, and wood strands, in developing corrugated core sandwich panels as a structural building material. By leveraging the unique properties of these biobased materials within a corrugated geometry, the research presents a novel approach to enhancing the structural performance of such underutilized biobased materials. These biobased materials were used in different lengths to consider the manufacturing feasibility of corrugated panels and the effect of fiber length on their structural performance. The average lengths for wood strands and wheat straws were 12–15 cm and 3–7.5 cm, respectively, while rice husks were like particles, about 7 mm long. Due to the high silica content in rice husk and wheat straw, which negatively impacts the bonding performance, polymeric diphenylmethane diisocyanate (pMDI), an effective adhesive for such materials, was used for the fabrication of corrugated panels. Wood strands and phenol formaldehyde (PF) adhesive were used to fabricate flat outer layers. Flat panels were bonded to both sides of the corrugated panels using a polyurethane adhesive to develop corrugated core sandwich panels. Four-point bending tests were conducted to evaluate the panel’s bending stiffness, load-carrying capacity, and failure modes. Results demonstrated that sandwich panels with wood strand corrugated cores exhibited the highest bending stiffness and load-bearing capacity, while those with wheat straw corrugated cores performed similarly. Rice husk corrugated core sandwich panels showed the lowest mechanical performance compared to other sandwich panels. Considering the applications of these sandwich panels as floor, wall, and roof sheathing, all these panels exhibited superior bending performance compared to 11.2 mm- and 17.42 mm-thick commercial OSB (oriented strand board) panels, which are commonly used as building materials. These sandwich structures supported a longer span than commercial OSB panels while satisfying the deflection limit of L/360. The findings suggest the transformative potential of converting renewable yet underutilized materials into an engineered concept, corrugated geometry, leading to the development of high-performance, carbon-negative building materials suitable for flooring and roof applications. Full article
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<p>Rice husk, wheat straws, and wood strands with different fiber lengths.</p>
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<p>(<b>a</b>) Strand orientation for wood panels (<b>b</b>) rice husk, (<b>c</b>) wheat straw, and (<b>d</b>) wood strand corrugated panels.</p>
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<p>Sandwich panels fabricated from (<b>a</b>) wheat straw, (<b>b</b>) rice husk, and (<b>c</b>) wood strand corrugated cores and flat wood strand outer layers.</p>
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<p>Flowchart summarizing the fabrication and testing processes of corrugated panels.</p>
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<p>Bending stiffness for short and long span panels made up of rice husk, wheat straw, wood strands, and OSB panels of different thicknesses.</p>
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<p>Maximum normal stress for agricultural wastes and wood strands as well as types of failure where C, B, and S stands for crushing, bending, and shear failure, respectively.</p>
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<p>Comparison of equivalent distributed load and bending stiffness for short- and long-span specimens.</p>
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<p>Failure modes for 17.42 mm (0.75-inch)-thick OSB and corrugated core sandwich panels: (<b>a</b>,<b>b</b>) Simple bending failure for wood strand corrugated sandwich panels and 17.42-mm-(0.75-inch) thick OSB. (<b>c</b>) Wheat straw corrugated core panel with delamination within corrugated core. (<b>d</b>) Rice husk corrugated core panel with crushing failure along the core.</p>
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<p>Distributed load for different span lengths under specific deflection limits of L/240 and L/360.</p>
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14 pages, 5081 KiB  
Article
Chemical Control of the Invasive Weed Trianthema portulacastrum: Nethouse Studies
by Yaakov Goldwasser, Onn Rabinowitz, Jackline Abu-Nasser, Evgeny Smirnov, Guy Achdary and Hanan Eizenberg
Plants 2025, 14(1), 19; https://doi.org/10.3390/plants14010019 - 25 Dec 2024
Viewed by 314
Abstract
Trianthema portulacastrum L. (Aizoaceae), commonly known as desert horse purslane or black pigweed, is a C4 dicot succulent invasive annual plant that is widespread in agricultural fields in Southeast Asia, tropical America, Africa, and Australia. In Israel, Trianthema portulacastrum is an invasive weed [...] Read more.
Trianthema portulacastrum L. (Aizoaceae), commonly known as desert horse purslane or black pigweed, is a C4 dicot succulent invasive annual plant that is widespread in agricultural fields in Southeast Asia, tropical America, Africa, and Australia. In Israel, Trianthema portulacastrum is an invasive weed of increasing importance in agricultural fields, including mainly corn, tomato, alfalfa watermelon, and groundnut crops. The significance of this weed in crops has been recently reported in neighboring countries of Jordan and Egypt. In previous studies, we have examined and described the spread, biology, and germination requirements of Trianthema portulacastrum in Israel. The present study aimed to investigate the efficiency of single pre- and post-emergence herbicides and the combination of pre-applied herbicides for the control of this invasive weed in pots in a nethouse. We conducted three sequential experiments in a nethouse: (1) screening of pre-emergence herbicides, (2) screening of post-emergence herbicides, and (3) assessment of residual activity of combined pre-emergence herbicides in three distinct Hula Valley soil types. Efficacy was evaluated through weekly assessments of seedling emergence and vigor, with the final shoot fresh weight determined upon the experiment’s completion. In all experiments, weekly counts and vigor estimation of T. portulacastrum seedlings were conducted, and shoot fresh weights were determined at the end of the experiments. The results of pre-emergence herbicide screening showed that Fomesafen, Terbutryne, Flurochloridon, Sulfosulfuron, Cyrosulfamid + Izoxaflutole, and Dimethenamid were the most effective herbicides, leading to complete eradication of T. portulacastrum plants. Results of the post-emergence screening revealed that Saflufenacil, Foramsulfuron, Tembotrione + Isoxdifen-ethyl, and Rimsulfurom Methyl completely controlled the weed. In the soil residual study, three herbicide combinations (Fomesafen + Terbutryn, Sulfosulfuron + Fomesafen, and Dimethenamid + Flurochloridon) provided effective control across all soil types. These findings provide a foundation for future field trials investigating integrated pre- and post-emergence herbicide programs for T. portulacastrum management in various crops. Full article
(This article belongs to the Special Issue Plant Chemical Ecology)
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<p>Global distribution of <span class="html-italic">T. portulacastrum</span>, updated March 2024, according to the European and Mediterranean Plant Protection Organization (EPPO) Global Database. <a href="https://gd.eppo.int" target="_blank">https://gd.eppo.int</a>.</p>
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<p>Pots of the pre-emergence trial at 18 DAA in the nethouse, 5 replications = 5 pots per treatment. 0 = control, 1 = Dimethenamid 2520 g/ha, 2 = Imazapic 480 g/ha, 3 = imazamox 32 g/ha, 4 = Saflufenacil 35 g/ha, 5 = Fomesafen 625 g/ha, 6 = Terbutryne 1000 g/h, 7 = Flurochloridon 625 g/ha, 8 = Sulfosulfuron 38 g/ha, 9 = Cyprosulfamid 72 g/ha + Izoxaflutole 72 g/ha.</p>
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<p>Pots of the post-emergence trial at 8 DAA in the nethouse, 6 replications with 6 pots per treatment. Herbicide and application rate: 0 = control, 1 = Imazapic 480, 2 = Saflufenacil 35 g/ha, 3 = Foramsulfuron 45 g/ha, 4 = Tembotrione 44 g/ha + Isoxdifen-ethyl 110 g/ha, 5 = Imazamox 32 g/ha, 6 = Bentazon 1920 g/ha, 7 = Pyraflufen 10 g/ha, 8 = Rimsulfuron Methyl 37.5 g/ha, 9 = Aclonifen 1200 g/ha.</p>
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<p>Weekly vitality estimations of <span class="html-italic">T. portulacastrum</span> plants in the pre-emergence pot experiment. Bars at each DAA topped with different letters are statistically different according to Tukey–Kramer HSD, <span class="html-italic">p</span> = 0.01. See <a href="#plants-14-00019-t002" class="html-table">Table 2</a> for full herbicide chemical names and application rates. DAA: days after application.</p>
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<p><span class="html-italic">T. portulacastrum</span> final shoot fresh weight at the termination of the pre-emergence pot experiment, 32 DAA. See <a href="#plants-14-00019-t002" class="html-table">Table 2</a> for full herbicide chemical names and application rates. Bars topped with different letters are statistically different according to Tukey–Kramer HSD, <span class="html-italic">p</span> = 0.01.</p>
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<p>Weekly vitality estimations of <span class="html-italic">T. portulacastrum</span> plants in the post-emergence pot experiment. Herbicides were applied on <span class="html-italic">T. portulacastrum</span> at the 4-leaf stage on August 23rd, 13 days after seeding. See <a href="#plants-14-00019-t002" class="html-table">Table 2</a> for full herbicide chemical names and application rates. Bars at each DAA topped with different letters are statistically different according to Tukey–Kramer HSD, <span class="html-italic">p</span> = 0.01.</p>
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<p><span class="html-italic">T. portulacastrum</span> shoot fresh weights at the termination of the post-emergence applied herbicides experiment, 32 DAA. See <a href="#plants-14-00019-t002" class="html-table">Table 2</a> for full herbicide chemical names and application rates. Bars topped with different letters are statistically different according to Tukey–Kramer HSD, <span class="html-italic">p</span> = 0.01.</p>
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<p><span class="html-italic">T. portulacastrum</span> final shoot fresh weight at the termination of the pre-emergence pot experiment in Lehavot soil seeded 0 days after herbicide application. See <a href="#plants-14-00019-t002" class="html-table">Table 2</a> for herbicide abbreviations and rates. Bars topped with different letters are statistically different according to Tukey–Kramer HD, <span class="html-italic">p</span> = 0.01.</p>
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<p><span class="html-italic">T. portulacastrum</span> final shoot fresh weight at the termination of the pre-emergence pot experiment in Menara soil seeded 0 days after herbicide application. See <a href="#plants-14-00019-t002" class="html-table">Table 2</a> for herbicide abbreviations and rates. Bars topped with different letters are statistically different according to Tukey–Kramer HD, <span class="html-italic">p</span> = 0.01.</p>
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<p><span class="html-italic">T. portulacastrum</span> final shoot fresh weight at the termination of the pre-emergence pot experiment in Shamir soil seeded 0 days after herbicide application. See <a href="#plants-14-00019-t002" class="html-table">Table 2</a> for herbicide abbreviations and rates. Bars topped with different letters are statistically different according to Tukey–Kramer HD, <span class="html-italic">p</span> = 0.01.</p>
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<p><span class="html-italic">T. portulacastrum</span> final shoot fresh weight at the termination of the pre-emergence pot experiment in Lehavot soil seeded 50 days after herbicide application. See <a href="#plants-14-00019-t002" class="html-table">Table 2</a> for herbicide abbreviations and rates. Bars topped with different letters are statistically different according to Tukey–Kramer HD, <span class="html-italic">p</span> = 0.01.</p>
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<p><span class="html-italic">T. portulacastrum</span> final shoot fresh weight at the termination of the pre-emergence pot experiment in Menara soil seeded 50 days after herbicide application. See <a href="#plants-14-00019-t002" class="html-table">Table 2</a> for herbicide abbreviations and rates. Bars topped with different letters are statistically different according to Tukey–Kramer HSD, <span class="html-italic">p</span> = 0.01.</p>
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<p><span class="html-italic">T. portulacastrum</span> final shoot fresh weight at the termination of the pre-emergence pot experiment in Shamir soil seeded 50 days after herbicide application. See <a href="#plants-14-00019-t002" class="html-table">Table 2</a> for herbicide abbreviations and rates. Bars topped with different letters are statistically different according to Tukey–Kramer HSD, <span class="html-italic">p</span> = 0.01.</p>
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27 pages, 6060 KiB  
Article
Organic Amendments Enhance the Remediation Potential of Economically Important Crops in Weakly Alkaline Heavy Metal-Contaminated Bauxite Residues
by Xingfeng Zhang, Qiankui Yu, Bo Gao, Maosheng Hu, Hongxu Chen, Yexi Liang and Haifeng Yi
Agriculture 2025, 15(1), 15; https://doi.org/10.3390/agriculture15010015 - 25 Dec 2024
Viewed by 303
Abstract
Heavy metal (HM) pollution in soil has emerged as a global concern. This study introduces a novel approach to ameliorate HM-contaminated bauxite residues (BRs) characterized by weak alkalinity and low nutrient levels. By cultivating economically important crops, this method aims to enhance the [...] Read more.
Heavy metal (HM) pollution in soil has emerged as a global concern. This study introduces a novel approach to ameliorate HM-contaminated bauxite residues (BRs) characterized by weak alkalinity and low nutrient levels. By cultivating economically important crops, this method aims to enhance the remediation of heavy metal-contaminated BR while simultaneously promoting economically important crop production. Using a pot experiment, we investigated the effects of four organic amendments (peat, cow dung, bagasse, and microbial fertilizer) on the growth and BR properties of four economically important crops (castor, ramie, sugarcane, and cassava). The application of these organic amendments obviously reduced the BRs pH by 0.6–2.22%. Organic amendment applications significantly increased the soil organic matter (SOM) content and cation exchange capacity (CEC) by 14.35 to 179.94% and 6.87 to 12.14%, respectively. Additionally, the use of organic amendments enhanced BR enzyme activity, with microbial fertilizer demonstrating a substantial increase in BR invertase activity from 131.49 to 687.61%. Superoxide dismutase (SOD) activity and malondialdehyde (MDA) content remarkably increased, whereas catalase (CAT) activity did not show significant differences. HM content analysis in different plant parts revealed HMs primarily found in the plant roots. Organic amendments mitigate the transfer of HMs from roots to shoots, thereby reducing HM content in the available parts of economically important crops. The pot experiment results demonstrated the effectiveness of the four combinations in achieving both the repair and production objectives. These combinations include planting castor and ramie with cow dung, sugarcane with peat, and cassava with bagasse. These findings underscore the feasibility of cultivating economically important crops in HM-contaminated BRs, enhancing BR quality, and augmenting farmers’ incomes. This study provides a scientific basis for mine remediation and reclamation using BRs. Full article
(This article belongs to the Section Agricultural Soils)
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Figure 1
<p>The FTIR spectra of four amendments.</p>
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<p>pH, SOM, CEC, and enzyme activity. Values represent the mean ± standard error (n = 3). (*, ** and ***) indicates significant differences (<span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01 and <span class="html-italic">p</span> &lt; 0.001) between treatments for the same plant. (<b>a</b>): pH; (<b>b</b>): SOM content; (<b>c</b>): CEC content; (<b>d</b>) catalase activity; (<b>e</b>): urease activity; (<b>f</b>): invertase activity.</p>
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<p>The content of HMs in BR. Values represent the mean ± standard error (n = 3). Different letters indicate significant differences between treatments for the same plant (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>HM effective state content in BRs. Values represent the mean ± standard error (n = 3). Different letters indicate significant differences between treatments for the same plant (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Plant physiological parameters. U denotes enzyme activity unit size. Values represent the mean ± standard error (n = 3). (*, ** and ***) indicates significant differences (<span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01 and <span class="html-italic">p</span> &lt; 0.001) between treatments for the same plant. (<b>a</b>): MDA content; (<b>b</b>): CAT content; (<b>c</b>): SOD content.</p>
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<p>HM content in the shoots. Values represent the mean ± standard error (n = 3). Sugarcane shoot refers to another part of the plant excluding the stem. Different letters indicate significant differences between treatments for the same plant (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>HM content in the roots. Values represent the mean ± standard error (n = 3). Different letters indicate significant differences between treatments for the same plant (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>HM accumulation amounts in the plants. Values represent the mean ± standard error (n = 3). Different letters indicate significant differences between treatments for the same plant (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>The principal component analysis in the BRs and plants. In the principal component analysis of the BRs, the red arrow represents the physical and chemical properties of the BR, and the blue arrow represents the BR enzyme activity and the effective state HMs content. In the principal component analysis of the plants, the red arrow represents the parameters of plant growth, and the blue arrow represents the physiological parameters of the plant and the physical and chemical properties of the BR. The angle of the arrow represents the correlation (acute angle is positive correlation, obtuse angle is negative correlation), and the length represents the contribution (the projections on the X and Y axes represent the contribution to the first principal component and the second principal component, respectively).</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
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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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