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Nitrogen, Volume 5, Issue 4 (December 2024) – 18 articles

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15 pages, 316 KiB  
Article
Peanut Cake as an Alternative Protein Source to Soybean Meal on Performance, Nitrogen Utilization, and Carcass Traits in Feedlot Lambs
by Maria Leonor Garcia Melo Lopes de Araújo, Gleidson Giordano Pinto de Carvalho, André Gustavo Leão, Douglas dos Santos Pina, Ronaldo Lopes Oliveira, Laís Santana Bezerra Dias, Jéssica Dias Caribé, Rodolpho Almeida Rebouças, Luciana de Jesus Rodrigues, Rosani Valéria Marcelina Matoso Silva, Camila de Oliveira Nascimento, Victor Guimarães Oliveira Lima, Taiala Cristina de Jesus Pereira, Mara Lúcia Albuquerque Pereira and Henry Daniel Ruiz Alba
Nitrogen 2024, 5(4), 1092-1106; https://doi.org/10.3390/nitrogen5040070 - 28 Nov 2024
Viewed by 267
Abstract
Substituting soybean meal (SM) with other protein sources can be advantageous in reducing production costs without increasing nitrogen losses in the environment. Peanut cake (PC) might be a strategy in ruminant herds to result in a performance similar to that observed in animals [...] Read more.
Substituting soybean meal (SM) with other protein sources can be advantageous in reducing production costs without increasing nitrogen losses in the environment. Peanut cake (PC) might be a strategy in ruminant herds to result in a performance similar to that observed in animals fed SM. This study assessed the PC inclusion at rates of 0, 250, 500, 750, and 1000 g/kg on intake and digestibility, performance, nitrogen balance, microbial protein synthesis, and carcass traits of lambs. Forty-five entire, crossbreed Dorper × Santa Inês lambs (average age = five months and 24.49 ± 5.27 kg of BW) were distributed in a completely randomized design. Only the ether extract intake was not affected by the PC inclusion, and only the crude protein digestibility was affected by the diets. The total weight and average daily gains decreased, and the feed conversion increased. Hot carcass yield was influenced quadratically, the leg depth increased and the loin-eye area decreased. The total replacement of soybean meal with peanut cake in diets for lambs did not compromise nutrient digestibility, microbial protein synthesis, and carcass characteristics. Nevertheless, peanut cake as a protein source in the diet had adverse effects on nutrient intake and growth performance of feedlot lambs. Full article
18 pages, 579 KiB  
Article
Effect of a Slow-Release Urea Nanofertilizer on Soil Microflora and Yield of Direct Seeded Rice (Oryza sativa L.)
by Yashika Sehgal, Anu Kalia, Buta Singh Dhillon and Gurmeet Singh Dheri
Nitrogen 2024, 5(4), 1074-1091; https://doi.org/10.3390/nitrogen5040069 - 25 Nov 2024
Viewed by 584
Abstract
Nitrogen fertilizers have a significant impact on the growth of rice. The overuse and inappropriate application of nitrogen fertilizers have resulted in environmental pollution, in addition to subjecting both humans and livestock to negative health hazards. Finding a viable substitute for traditional nitrogen [...] Read more.
Nitrogen fertilizers have a significant impact on the growth of rice. The overuse and inappropriate application of nitrogen fertilizers have resulted in environmental pollution, in addition to subjecting both humans and livestock to negative health hazards. Finding a viable substitute for traditional nitrogen fertilizers is crucial and essential to help improve crop yield and minimize environmental damage. Nano-nitrogen fertilizers offer a possible alternative to traditional fertilizers due to a slow/controlled release of nitrogen. The present work aimed to study the effect of a slow-release urea nanofertilizer on soil ammonical (NH4-N) and nitrate-N (NO3-N) content, culturable soil microflora, and soil enzyme activities in three different soil samples procured from Ludhiana and Patiala districts through a soil column study. Seven treatments, including 0, 50 (75 kg/ha N), 75 (112.5 kg/ha N), and 100% (150 kg/ha N) of the recommended dose (RD) of conventional urea and nano-urea fertilizer were applied. The leachate samples collected from nano-urea treatment exhibited NH4-N for the first two weeks, followed by NO3-N appearance. The higher NH4-N and NO3-N contents in the leachate were recorded for light-textured soil as compared to medium- and heavy-textured soil samples. The soil microbial counts and enzyme activities were recorded to be maximum in light-textured soils. Therefore, this slow-release formulation could be more useful for light-textured soils to decrease applied N-fertilizer losses, as well as for improving the soil microbial viable cell counts and soil enzyme activities. The effect of urea nanofertilizer on the growth and yield of direct-seeded rice (Oryza sativa L.) was also evaluated under field conditions. Both studies were performed independently. Numerically, the highest shoot height, fresh and dry shoot weight, and significantly maximum total chlorophyll, carotenoid, and anthocyanins were recorded in the T2 (100% RDF through nano-urea) treatment. The yield-attributing traits, including the number of filled grains and thousand-grain weight, were also recorded to have increased in T2 treatment. A numerical increase in NPK for plant and grain of rice at 100% RDN through nano-urea was recorded. The soil application of the product exhibited no negative effect on the soil microbial viable cell count on different doses of nano-urea fertilizer. The soil nitrogen fixer viable counts were rather improved in nano-urea treatments. The results reflect that nano-urea fertilizer could be considered as a possible alternative to conventional fertilizer. Full article
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<p>Effect of different N-fertilizer levels of conventional and nano-urea formulation on the grain nitrogen, phosphorus, and potassium content. Mean values with different alphabetic scripting on bars depict statistically significant differences.</p>
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16 pages, 3427 KiB  
Systematic Review
Slow-Release Fertilisers Control N Losses but Negatively Impact on Agronomic Performances of Pasture: Evidence from a Meta-Analysis
by Gunaratnam Abhiram
Nitrogen 2024, 5(4), 1058-1073; https://doi.org/10.3390/nitrogen5040068 - 17 Nov 2024
Viewed by 421
Abstract
High nitrogen (N) losses and low nitrogen utilisation efficiency (NUE) of conventional-nitrogen fertilisers (CNFs) are due to a mismatch between N-delivery and plant demand; thus, slow-release N fertilisers (SRNFs) are designed to improve the match. A quantitative synthesis is lacking to provide the [...] Read more.
High nitrogen (N) losses and low nitrogen utilisation efficiency (NUE) of conventional-nitrogen fertilisers (CNFs) are due to a mismatch between N-delivery and plant demand; thus, slow-release N fertilisers (SRNFs) are designed to improve the match. A quantitative synthesis is lacking to provide the overall assessment of SRNFs on pasture. This meta-analysis analyses application rate and type of SRNFs on N losses and agronomic performances with 65 data points from 14 studies in seven countries. Standardized mean difference of SRNFs for nitrate leaching losses and N2O emission were −0.87 and −0.69, respectively, indicating their effectiveness in controlling losses. Undesirably, SRNFs had a more negative impact on dry matter (DM) yield and NUE than CNFs. Subgroup analysis showed that SRNF type and application rate had an impact on all tested parameters. The biodegradable coating-type of SRNF outperformed other types in controlling N losses and improving agronomic performances. High application rates (>100 kg N ha−1) of SRNFs are more effective in controlling N losses. In conclusion, SRNFs are more conducive to controlling N losses, but they showed a negative impact on yield and NUE in pasture. Further studies are recommended to assess the efficacy of SRNFs developed using advanced technologies to understand their impact on pastoral agriculture. Full article
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<p>Schematic diagram for inclusion criteria of articles for this systematic review and meta-analysis (PRISMA) [<a href="#B32-nitrogen-05-00068" class="html-bibr">32</a>].</p>
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<p>The summary of the reported parameters from each study included in this meta-analysis [<a href="#B10-nitrogen-05-00068" class="html-bibr">10</a>,<a href="#B24-nitrogen-05-00068" class="html-bibr">24</a>,<a href="#B25-nitrogen-05-00068" class="html-bibr">25</a>,<a href="#B27-nitrogen-05-00068" class="html-bibr">27</a>,<a href="#B35-nitrogen-05-00068" class="html-bibr">35</a>,<a href="#B36-nitrogen-05-00068" class="html-bibr">36</a>,<a href="#B37-nitrogen-05-00068" class="html-bibr">37</a>,<a href="#B38-nitrogen-05-00068" class="html-bibr">38</a>,<a href="#B39-nitrogen-05-00068" class="html-bibr">39</a>,<a href="#B40-nitrogen-05-00068" class="html-bibr">40</a>,<a href="#B41-nitrogen-05-00068" class="html-bibr">41</a>,<a href="#B42-nitrogen-05-00068" class="html-bibr">42</a>,<a href="#B43-nitrogen-05-00068" class="html-bibr">43</a>,<a href="#B44-nitrogen-05-00068" class="html-bibr">44</a>].</p>
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<p>The nitrate leaching losses of conventional nitrogen fertilisers (CNFs) and slow-release nitrogen fertilisers (SRNFs) based on (<b>a</b>) SRNF types (PC—polymer coating, BC—biodegradable coating, IC—inorganic coating, and PCH—polymer chain), (<b>b</b>) fertiliser application rates (kg N/ha) and (<b>c</b>) overall studies. SMD stands for standard mean difference. Numbers next to range graph indicate the number of studies included for analysis.</p>
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<p>The correlation between effect size (standardized mean difference: SMD) and application rate of SRNFs for (<b>a</b>) nitrate leaching losses, (<b>b</b>) ammonium leaching losses, (<b>c</b>) N<sub>2</sub>O emission, (<b>d</b>) dry matter yield, (<b>e</b>) nitrogen utilisation efficiency (NUE) and (<b>f</b>) herbage nitrogen. Dark shade and light shades indicate a 95% confidence interval and a 95% prediction level, respectively.</p>
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<p>The effect of SRNF on ammonium leaching losses. SRNF and CNF refer to slow-release nitrogen fertiliser and conventional nitrogen fertiliser, respectively [<a href="#B10-nitrogen-05-00068" class="html-bibr">10</a>,<a href="#B27-nitrogen-05-00068" class="html-bibr">27</a>,<a href="#B36-nitrogen-05-00068" class="html-bibr">36</a>].</p>
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<p>The effect of SRNF on N<sub>2</sub>O emission. SRNF and CNF refer to slow-release nitrogen fertiliser and conventional nitrogen fertiliser, respectively [<a href="#B10-nitrogen-05-00068" class="html-bibr">10</a>,<a href="#B27-nitrogen-05-00068" class="html-bibr">27</a>,<a href="#B42-nitrogen-05-00068" class="html-bibr">42</a>,<a href="#B44-nitrogen-05-00068" class="html-bibr">44</a>].</p>
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<p>Dry matter yield of conventional nitrogen fertilisers (CNFs) and slow-release nitrogen fertilisers (SRNFs) based on (<b>a</b>) SRNF types (PC—polymer coating, BC—biodegradable coating, IC—inorganic coating, and PCH—polymer chain), (<b>b</b>) fertiliser application rates (kg N/ha), and (<b>c</b>) overall studies. SMD stands for standard mean difference. Numbers next to range graph indicate the number of studies included for analysis.</p>
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<p>The plant nutrient demand (PND) and nutrient delivery by slow-release nitrogen fertiliser (NDSRNF) for (<b>a</b>) other crops and (<b>b</b>) pasture. The blue shaded area shows the PND of pasture during continuous grass grazing.</p>
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<p>Herbage nitrogen (HN) in a pasture of conventional nitrogen fertilisers (CNFs) and slow-release nitrogen fertilisers (SRNFs) based on (<b>a</b>) SRNF types (PC—polymer coating, BC—biodegradable coating, IC—inorganic coating, and PCH—polymer chain), (<b>b</b>) fertiliser application rates (kg N/ha), and (<b>c</b>) overall studies. SMD stands for standard mean difference. Numbers next to range graph indicate the number of studies included for analysis.</p>
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<p>Nitrogen utilisation efficiency (NUE) in a pasture of conventional nitrogen fertilisers (CNFs) and slow-release nitrogen fertilisers (SRNFs) based on (<b>a</b>) SRNF types (PC—polymer coating, BC—biodegradable coating, IC—inorganic coating, and PCH—polymer chain), (<b>b</b>) fertiliser application rates (kg N/ha), and (<b>c</b>) overall studies. SMD stands for standard mean difference. Numbers next to range graph indicate the number of studies included for analysis.</p>
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10 pages, 695 KiB  
Article
In Situ Nitrate Monitoring for Improved Fertigation in On-Demand Coupled Aquaponic Systems
by Sofia Faliagka, Ioannis Naounoulis, Eleftheria Maria Pechlivani and Nikolaos Katsoulas
Nitrogen 2024, 5(4), 1048-1057; https://doi.org/10.3390/nitrogen5040067 - 7 Nov 2024
Viewed by 509
Abstract
Fertigation practices in soilless crop cultivation often rely on predetermined recipes, which may lead to suboptimal nutrient concentrations due to inherent human error or environmental fluctuations. To address this challenge, the integration of in situ real-time nutrient analyzers becomes imperative for ensuring the [...] Read more.
Fertigation practices in soilless crop cultivation often rely on predetermined recipes, which may lead to suboptimal nutrient concentrations due to inherent human error or environmental fluctuations. To address this challenge, the integration of in situ real-time nutrient analyzers becomes imperative for ensuring the delivery of high-quality supply solutions. This study assesses the effectiveness of a real-time nitrate (NO3) analyzer in optimizing the mineral composition of the nutrient solution for fertigating a decoupled aquaponic cucumber crop. The analyzer was integrated into the programmable logic controller of the greenhouse’s hydroponic system. The NO3 analyzer was activated during solution preparation, dynamically adjusting the NO3 concentration based on real-time measurements from either the aquaculture or drainage solution by adding the necessary water or/and nutrients in order to prepare a solution to meet the needs of the crop. Four treatments were evaluated: hydroponics (HP), coupled aquaponics (CAP), decoupled aquaponics (DCAP) with EC adjustment, and decoupled aquaponics with NO3 adjustment (DCAP_N). Results indicated that the DCAP_N treatment, with NO3 adjustment, yielded the highest crop productivity, outperforming DCAP, HP, and CAP treatments by 7.4%, 21.2%, and 56.1%, respectively. Additionally, DCAP_N demonstrated superior water use efficiency (WUE) and fertilizer use efficiency (FUE), exhibiting a 21.5% and 52.5% increase over the HP treatment, respectively. These findings align with the European Green Deal’s objectives by enhancing nutrient management practices, which are crucial for minimizing nutrient loss and ensuring the sustainable and efficient use of fertilizers. Full article
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<p>(<b>a</b>) Mean plant height (cm) and (<b>b</b>) mean number of leaves of the different treatments of the crop during the whole cultivation period. Lowercase letters indicate differences between treatments (<span class="html-italic">p</span> &lt; 0.05) for each DAT.</p>
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17 pages, 4732 KiB  
Article
Nitrogen Assimilation, Biomass, and Yield in Response to Application of Algal Extracts, Rhizobium sp., and Trichoderma asperellum as Biofertilizers in Hybrid Maize
by Sandra Pérez-Álvarez, Erick H. Ochoa-Chaparro, Julio César Anchondo-Páez, César M. Escobedo-Bonilla, Joel Rascón-Solano, Marco A. Magallanes-Tapia, Luisa Patricia Uranga-Valencia, Reinier Hernández-Campos and Esteban Sánchez
Nitrogen 2024, 5(4), 1031-1047; https://doi.org/10.3390/nitrogen5040066 - 1 Nov 2024
Viewed by 595
Abstract
Nitrogen is essential for plants’ growth, yield, and crop quality, and its deficiency limits food production worldwide. In addition, excessive fertilization and inefficient use of N can increase production costs and cause environmental problems. A possible solution to this problem is the application [...] Read more.
Nitrogen is essential for plants’ growth, yield, and crop quality, and its deficiency limits food production worldwide. In addition, excessive fertilization and inefficient use of N can increase production costs and cause environmental problems. A possible solution to this problem is the application of biofertilizers, which improve N assimilation and increase biomass and yield. Therefore, the objective of this research was to evaluate the impact of the application of a combination of green and red algae (Ulva lactuca and Solieria spp.), Rhizobium sp., Trichoderma asperellum, and the combination of the above three biofertilizers on N assimilation. A completely randomized design was performed, with 10 plants per treatment and five treatments: T1 = control; T2 = algal extracts; T3 = Rhizobium sp.; T4 = T. asperellum; T5 = T2 + T3 + T4. Our analyses showed that the biofertilizers’ application was better than the control. The application of Rhizobium sp. had the best performance amongst all of the biofertilizers, with the highest nitrate reductase activity in maize leaves, which enhanced photosynthesis, increasing biomass and yield. The use of Rhizobium sp. showed increases in biomass (13.4%) and yield (11.82%) compared to the control. This research shows that biofertilizers can be a key component for sustainable agricultural practices. Full article
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<p>Application of biofertilizers in maize plants. T1 = control: plants without biofertilizers applied. T2 = algae extracts: foliar application of algae extracts to maize plants. T3 = <span class="html-italic">Rhizobium</span> sp.: root inoculation of maize plants with bacteria from the genus <span class="html-italic">Rhizobium</span> sp. T4 = <span class="html-italic">Trichoderma asperellum</span>: root inoculation of maize plants with the fungus <span class="html-italic">Trichoderma asperellum</span>. T5 = combination of algae extracts, <span class="html-italic">Rhizobium</span> sp., and <span class="html-italic">Trichoderma asperellum</span>: combined application of the three aforementioned biofertilizers.</p>
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<p>Effects of biofertilizers’ application on total biomass: T1 = control; T2 = algal extracts; T3 = <span class="html-italic">Rhizobium</span> sp.; T4 = <span class="html-italic">T. asperellum</span>; T5 = T2 + T3 + T4. Means with equal letters do not differ according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effects of biofertilizers’ application on yield: T1 = control; T2 = algal extracts; T3 = <span class="html-italic">Rhizobium</span> sp.; T4 = <span class="html-italic">T. asperellum</span>; T5 = T2 + T3 + T4. Means with equal letters do not differ according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effects of biofertilizers’ application on nitrate reductase activity “In Vivo”: T1 = control; T2 = algal extracts; T3 = <span class="html-italic">Rhizobium</span> sp.; T4 = <span class="html-italic">T. asperellum</span>; T5 = T2+T3+T4. Means with equal letters do not differ according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effects of biofertilizers’ application on SPAD values: T1 = control; T2 = algal extracts; T3 = <span class="html-italic">Rhizobium</span> sp.; T4 = <span class="html-italic">T. asperellum</span>; T5 = T2 + T3 + T4. Means with equal letters do not differ according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effects of the application of biofertilizers on photosynthetic pigment activity: T1 = control; T2 = algal extracts; T3 = <span class="html-italic">Rhizobium</span> sp.; T4 = <span class="html-italic">T. asperellum</span>; T5 = T2 + T3 + T4. Means with equal letters do not differ according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effects of biofertilizers’ application on leaf numbers: T1 = control; T2 = algal extracts; T3 = <span class="html-italic">Rhizobium</span> sp.; T4 = <span class="html-italic">T. asperellum</span>; T5 = T2 + T3 + T4. Means with equal letters do not differ according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effects of biofertilizers’ application on plant height: T1 = control; T2 = algal extracts; T3 = <span class="html-italic">Rhizobium</span> sp.; T4 = <span class="html-italic">T. asperellum</span>; T5 = T2 + T3 + T4. Means with equal letters do not differ according to Duncan’s multiple range test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>The figure shows the Pearson correlation analysis between the variables of biomass, yield, nitrate reductase activity, SPAD values, photosynthetic pigments, number of leaves, and plant height, evaluating the effects of the biofertilizers. The colors in the heatmap represent the magnitude and direction of the correlations, with red tones indicating strong positive correlations and blue tones indicating negative or weak correlations. The numerical values within each cell indicate the correlation coefficient between pairs of variables, providing a clear view of the interdependent relationships among the measured variables.</p>
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<p>(<b>a</b>) Venn diagram representing the effects of different treatments on plant growth and physiological variables. (<b>b</b>) Interaction diagram: The bar chart shows how the different variables (biomass, yield, nitrate reductase activity, SPAD values, photosynthetic pigments, number of leaves, and plant height) respond to each treatment (T1 to T5). The arrows indicate an increase in these variables compared to the control (T1).</p>
Full article ">Figure 10 Cont.
<p>(<b>a</b>) Venn diagram representing the effects of different treatments on plant growth and physiological variables. (<b>b</b>) Interaction diagram: The bar chart shows how the different variables (biomass, yield, nitrate reductase activity, SPAD values, photosynthetic pigments, number of leaves, and plant height) respond to each treatment (T1 to T5). The arrows indicate an increase in these variables compared to the control (T1).</p>
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16 pages, 4495 KiB  
Article
How Do Satellite Precipitation Products Affect Water Quality Simulations? A Comparative Analysis of Rainfall Datasets for River Flow and Riverine Nitrate Load in an Agricultural Watershed
by Mahesh R. Tapas
Nitrogen 2024, 5(4), 1015-1030; https://doi.org/10.3390/nitrogen5040065 - 1 Nov 2024
Viewed by 516
Abstract
Excessive nitrate loading from agricultural runoff leads to substantial environmental and economic harm, and although hydrological models are used to mitigate these effects, the influence of various satellite precipitation products (SPPs) on nitrate load simulations is often overlooked. This study addresses this research [...] Read more.
Excessive nitrate loading from agricultural runoff leads to substantial environmental and economic harm, and although hydrological models are used to mitigate these effects, the influence of various satellite precipitation products (SPPs) on nitrate load simulations is often overlooked. This study addresses this research gap by evaluating the impacts of using different satellite precipitation products—ERA5, IMERG, and gridMET—on flow and nitrate load simulations with the Soil and Water Assessment Tool Plus (SWAT+), using the Tar-Pamlico watershed as a case study. Although agricultural activities are higher in the summer, this study found the lowest nitrate load during this season due to reduced runoff. In contrast, the nitrate load was higher in the winter because of increased runoff, highlighting the dominance of water flow in driving riverine nitrate load. This study found that although IMERG predicts the highest annual average flow (120 m3/s in Pamlico Sound), it unexpectedly results in the lowest annual average nitrate load (1750 metric tons/year). In contrast, gridMET estimates significantly higher annual average nitrate loads (3850 metric tons/year). This discrepancy underscores the crucial impact of rainfall datasets on nitrate transport predictions and highlights how the choice of dataset can significantly influence nitrate load simulations. Full article
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<p>Elevation map of the study area watershed in eastern North Carolina, USA, showing elevation categories from &lt;10 m to &gt;150 m. The inset map provides the geographic location of the watershed within the broader southeastern U.S. region.</p>
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<p>Framework for SWAT+ model setup, calibration, and multi-objective optimization for analyzing flow and nitrate load in the Tar-Pamlico watershed. The diagram outlines key data inputs, model detailing, and scenario analysis with multiple rainfall datasets.</p>
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<p>Seasonal rainfall comparison for Greenville, NC (2001–2019) using ERA5, gridMET, and IMERG datasets [The bar chart shows seasonal variations in rainfall, highlighting differences between the three datasets for Fall, Spring, Summer, and Winter].</p>
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<p>Annual average flow values (m<sup>3</sup>/s) from 2003 to 2019 at five locations in the Tar-Pamlico watershed, comparing predictions from three rainfall datasets: ERA5, IMERG, and gridMET [IMERG consistently predicts higher flows across all locations, with gridMET generally estimating the lowest values]. As SWAT+ does not account for backflows, the flow values at Pamlico and Pamlico Sound may be overestimated compared to actual conditions, where backflow could reduce overall flow rates.</p>
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<p>Streamflow comparison for the study area using ERA5, gridMET, and IMERG datasets [The maps depict spatial variations in streamflow (m<sup>3</sup>/s) across subbasins, with flow categorized into five classes, highlighting differences in streamflow estimates among the datasets].</p>
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<p>Seasonal average flow values at the outlet of Lower Tar sub-watershed for three rainfall datasets [The plot illustrates the flow patterns for ERA5, IMERG, and gridMET rainfall datasets, showing distinct seasonal peaks with variability across the years].</p>
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<p>Annual average nitrate load (metric tons/year) across five sub-watersheds in the Tar-Pamlico Basin, comparing predictions from three rainfall datasets: ERA5, IMERG, and gridMET [gridMET consistently estimates the highest nitrate loads, while IMERG predicts significantly lower values across all locations].</p>
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<p>Spatial distribution of annual nitrate load across SWAT+ delineated channels using ERA5, gridMET, and IMERG datasets [The maps show nitrate load (metric tons per year) categorized into five classes, highlighting variability in nitrate transport estimates among the datasets across the watershed].</p>
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<p>Comparison of monthly nitrate load (metric tons/month) from three datasets: ERA5, IMERG, and gridMET from 2003 to 2019 [The data show significant variations, with peak loads occurring in different periods for each dataset].</p>
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14 pages, 2474 KiB  
Article
Effect of Nitrogen and Sulphur Fertilization on Winter Oilseed Rape Yield
by Wacław Jarecki, Joanna Korczyk-Szabó and Milan Macák
Nitrogen 2024, 5(4), 1001-1014; https://doi.org/10.3390/nitrogen5040064 - 1 Nov 2024
Viewed by 975
Abstract
Oilseed rape is one of many crops with high nutritional requirements, particularly for nitrogen (N) and sulphur (S). Both macronutrients affect important physiological plant functions and are essential for the proper growth and development of oilseed rape. The objective of the experiment was [...] Read more.
Oilseed rape is one of many crops with high nutritional requirements, particularly for nitrogen (N) and sulphur (S). Both macronutrients affect important physiological plant functions and are essential for the proper growth and development of oilseed rape. The objective of the experiment was to investigate the impact of nitrogen and sulphur fertilization on the yield of the winter oilseed rape cultivar LG Absolut. The experiment was conducted during the 2019/2020, 2020/2021, and 2022/2023 growing seasons on Haplic Cambisol soil formed from loess, with medium levels of mineral nitrogen and sulphur. In the experiment, two nitrogen fertilization treatments (150 and 200 kg ha−1) were compared in combination with three additional sulphur fertilization rates (20, 40, and 60 kg ha−1). The results demonstrated that the effectiveness of N and S fertilization varied between individual years. On average, the highest seed yields were obtained with the application of 200 kg N ha−1 combined with sulphur, regardless of the rate. This was attributed to a significant increase in soil–plant analysis development (SPAD) values, the number of pods per plant, and the thousand-seed weight. The increase in seed yield with the higher nitrogen rate without sulphur ranged from 0.36 to 0.57 t ha−1 compared to the lower rate (control 150 N kg ha−1). Supplementary sulphur fertilization increased seed yield ranging from 0.22 to 0.76 t ha−1. The protein content in the seeds increased, while the fat content decreased, following the application of the higher nitrogen rate. The decrease in fat content was mitigated by higher rates of sulphur. The application of 60 kg S ha−1 yielded similar results of the tested parameters to the lower rates. Therefore, for soils with moderate levels of mineral nitrogen and sulphur, it is recommended to fertilize winter oilseed rape with 200 kg N ha−1 combined with 20 or 40 kg S ha−1. Full article
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<p>Weather conditions during the growing seasons.</p>
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<p>Soil–Plant analysis development (SPAD). A—nitrogen (150 kg ha<sup>−1</sup>)–control, B—nitrogen (200 kg ha<sup>−1</sup>), C—nitrogen + sulphur (150 kg ha<sup>−1</sup> + 20 kg ha<sup>−1</sup>), D—nitrogen + sulphur (200 kg ha<sup>−1</sup> + 20 kg ha<sup>−1</sup>), E—nitrogen + sulphur (150 kg ha<sup>−1</sup> + 40 kg ha<sup>−1</sup>), F–nitrogen + sulphur (200 kg ha<sup>−1</sup> + 40 kg ha<sup>−1</sup>), G—nitrogen + sulphur (150 kg ha<sup>−1</sup> + 60 kg ha<sup>−1</sup>), H—nitrogen + sulphur (200 kg ha<sup>−1</sup> + 60 kg ha<sup>−1</sup>).</p>
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<p>Number of pods per plant. A—nitrogen (150 kg ha<sup>−1</sup>)–control, B—nitrogen (200 kg ha<sup>−1</sup>), C—nitrogen + sulphur (150 kg ha<sup>−1</sup> + 20 kg ha<sup>−1</sup>), D—nitrogen + sulphur (200 kg ha<sup>−1</sup> + 20 kg ha<sup>−1</sup>), E—nitrogen + sulphur (150 kg ha<sup>−1</sup> + 40 kg ha<sup>−1</sup>), F—nitrogen + sulphur (200 kg ha<sup>−1</sup> + 40 kg ha<sup>−1</sup>), G—nitrogen + sulphur (150 kg ha<sup>−1</sup> + 60 kg ha<sup>−1</sup>), H—nitrogen + sulphur (200 kg ha<sup>−1</sup> + 60 kg ha<sup>−1</sup>).</p>
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<p>Number of seeds per pod. A—nitrogen (150 kg ha<sup>−1</sup>)–control, B—nitrogen (200 kg ha<sup>−1</sup>), C—nitrogen + sulphur (150 kg ha<sup>−1</sup> + 20 kg ha<sup>−1</sup>), D—nitrogen + sulphur (200 kg ha<sup>−1</sup> + 20 kg ha<sup>−1</sup>), E—nitrogen + sulphur (150 kg ha<sup>−1</sup> + 40 kg ha<sup>−1</sup>), F—nitrogen + sulphur (200 kg ha<sup>−1</sup> + 40 kg ha<sup>−1</sup>), G—nitrogen + sulphur (150 kg ha<sup>−1</sup> + 60 kg ha<sup>−1</sup>), H—nitrogen + sulphur (200 kg ha<sup>−1</sup> + 60 kg ha<sup>−1</sup>).</p>
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<p>Thousand-seed weight (g). A—nitrogen (150 kg ha<sup>−1</sup>)–control, B—nitrogen (200 kg ha<sup>−1</sup>), C—nitrogen + sulphur (150 kg ha<sup>−1</sup> + 20 kg ha<sup>−1</sup>), D—nitrogen + sulphur (200 kg ha<sup>−1</sup> + 20 kg ha<sup>−1</sup>), E—nitrogen + sulphur (150 kg ha<sup>−1</sup> + 40 kg ha<sup>−1</sup>), F—nitrogen + sulphur (200 kg ha<sup>−1</sup> + 40 kg ha<sup>−1</sup>), G—nitrogen + sulphur (150 kg ha<sup>−1</sup> + 60 kg ha<sup>−1</sup>), H—nitrogen + sulphur (200 kg ha<sup>−1</sup> + 60 kg ha<sup>−1</sup>).</p>
Full article ">Figure 6
<p>Seed yield in t ha<sup>−1</sup>. A—nitrogen (150 kg ha<sup>−1</sup>)–control, B—nitrogen (200 kg ha<sup>−1</sup>), C—nitrogen + sulphur (150 kg ha<sup>−1</sup> + 20 kg ha<sup>−1</sup>), D—nitrogen + sulphur (200 kg ha<sup>−1</sup> + 20 kg ha<sup>−1</sup>), E—nitrogen + sulphur (150 kg ha<sup>−1</sup> + 40 kg ha<sup>−1</sup>), F—nitrogen + sulphur (200 kg ha<sup>−1</sup> + 40 kg ha<sup>−1</sup>), G—nitrogen + sulphur (150 kg ha<sup>−1</sup> + 60 kg ha<sup>−1</sup>), H—nitrogen + sulphur (200 kg ha<sup>−1</sup> + 60 kg ha<sup>−1</sup>).</p>
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9 pages, 899 KiB  
Article
Impact of Nodulation Efficiency and Concentrations of Soluble Sugars and Ureides on Soybean Water Deficit During Vegetative Growth
by Helena Chaves Tasca, Douglas Antônio Posso, Altemir José Mossi, Cimélio Bayer, Rogério Luís Cansian, Geraldo Chavarria and Tanise Luisa Sausen
Nitrogen 2024, 5(4), 992-1000; https://doi.org/10.3390/nitrogen5040063 - 17 Oct 2024
Viewed by 797
Abstract
Drought is the primary limiting factor affecting soybean productivity, and is exacerbated by climate change. In legumes like soybeans, biological nitrogen fixation (BNF) is the main form of nitrogen acquisition, with nitrogen being converted into ureides. A greenhouse experiment was conducted using the [...] Read more.
Drought is the primary limiting factor affecting soybean productivity, and is exacerbated by climate change. In legumes like soybeans, biological nitrogen fixation (BNF) is the main form of nitrogen acquisition, with nitrogen being converted into ureides. A greenhouse experiment was conducted using the soybean cultivar BMX Zeus IPRO, with two water treatments applied during the vegetative phase: control (C) and water deficit (D). The relative water content and number of nodules were reduced in the D plants. Ureide concentrations (allantoin and allantoic acid) were higher in nodules under D conditions. However, no differences were observed in allantoin, total ureide, and soluble sugar concentrations in leaves. Our results suggest that reducing the number of nodules may be a key strategy for maintaining BNF under drought conditions and that ureide accumulation could be the primary metabolic response in this soybean cultivar. These findings indicate that the effects of water restriction on BNF are likely associated with local metabolic responses rather than a systemic ureide feedback mechanism inhibiting BNF. Full article
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Figure 1
<p>Number of nodules (NN) (<b>a</b>), fresh weight of nodules (NFW) (<b>b</b>), and dry weight of nodules (NDW) (<b>c</b>) in soybean plants (<span class="html-italic">Glycine max</span> (L.) Merr.) measured using a Cultivar BMX Zeus IPRO at V6 vegetative stage, after eight days the imposition of control (C) and water deficit (D) treatments. Bars represent means ± standard error (<span class="html-italic">n</span> = 10). Asterisk (***) indicates a significant difference between treatments by the <span class="html-italic">t</span>-test (<span class="html-italic">p</span> ≤ 0.001).</p>
Full article ">Figure 2
<p>Concentration of allantoin in leaves (LA) (<b>a</b>), allantoic acid in leaves (LAA) (<b>b</b>), total ureides in leaves (LTU) (<b>c</b>), and concentration of allantoin in nodules (NA) (<b>d</b>), allantoic acid in nodules (NAA) (<b>e</b>), and total ureides in nodules (NTU) (<b>f</b>) in soybean plants (<span class="html-italic">Glycine max</span> (L.) Merr.) measured using a Cultivar BMX Zeus IPRO after eight days of imposition of control (C) and water deficit (D) treatments. Bars represent means ± standard error (<span class="html-italic">n</span> = 10). Asterisk (***) indicates a significant difference between treatments according to the <span class="html-italic">t</span>-test (<span class="html-italic">p</span> ≤ 0.001).</p>
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15 pages, 1942 KiB  
Article
Role of Nitrogen Fertilization and Sowing Date in Productivity and Climate Change Adaptation Forecast in Rice–Wheat Cropping System
by Khalid Hussain, Erdoğan Eşref Hakki, Ayesha Ilyas, Sait Gezgin and Muhammad Asif Kamran
Nitrogen 2024, 5(4), 977-991; https://doi.org/10.3390/nitrogen5040062 - 16 Oct 2024
Viewed by 551
Abstract
Global food security is at risk due to climate change. Soil fertility loss is among the impacts of climate change which reduces the productivity of rice–wheat cropping systems. This study investigated the effects of varying nitrogen levels and transplanting/sowing dates on the grain [...] Read more.
Global food security is at risk due to climate change. Soil fertility loss is among the impacts of climate change which reduces the productivity of rice–wheat cropping systems. This study investigated the effects of varying nitrogen levels and transplanting/sowing dates on the grain yield (GY) and biological yield (BY) of rice and wheat cultivars over two growing seasons (2017–2019). Additionally, the impact of climate change on the productivity of both crops was tested under a 1.5 °C temperature increase and 510 ppm CO2 concentration while nitrogen fertilization and sowing window adjustments were evaluated as adaptation options using the DSSAT and APSIM models. Results indicated that the application of 120 kg N ha−1 significantly enhanced both GY and BY in all rice cultivars. The highest wheat yields were obtained with 140 kg N ha−1 for all cultivars. Rice transplanting on the 1st of July and wheat sowing on the 15th of November showed the best yields. The statistical indices of the model’s forecast results were satisfactory for rice (R2 = 0.83–0.85, root mean square error (RMSE) = 341–441, model efficiency (EF) = 0.82–0.89) and wheat (R2 = 0.84–0.89, RMSE = 213–303, EF = 0.88–0.91). Both models predicted yield loss in wheat (20–25%) and rice (28–30%) under a climate change scenario. The models also predicted that increased nitrogen application and earlier planting would be necessary to reduce the impacts of climate change on the productivity of both crops. Full article
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<p>Relationships between modeled and measured rice grain yield during (<b>a</b>) DSSAT calibration, (<b>b</b>) DSSAT evaluation (<b>c</b>) APSIM calibration and (<b>d</b>) APSIM evaluation.</p>
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<p>Climate change impact assessment and adaptation strategies in three high-yielding rice cultivars at baseline (2017–2018 conditions), Scenario 1 (+1.5 °C with 510 ppm CO<sub>2</sub>), Scenario 2 (Scenario 1 with 10% fertilizer increase), Scenario 3 (Scenario 1 with 10 days earlier sowing) by using (<b>a</b>) DSSAT and (<b>b</b>) APSIM after calibration and evaluation. Standard error is presented as error bars.</p>
Full article ">Figure 3
<p>Climate change impact assessment and adaptation strategies in three high-yielding wheat cultivars at baseline (2017–2018 conditions), Scenario 1 (+1.5 °C with 510 ppm CO<sub>2</sub>), Scenario 2 (Scenario 1 with 10% fertilizer increase), Scenario 3 (Scenario 1 with 15 days earlier transplanting) by using (<b>a</b>) DSSAT and (<b>b</b>) APSIM after calibration and evaluation. Standard error is presented as error bars.</p>
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36 pages, 8178 KiB  
Article
Co-Inoculation of Soybean Seeds with Azospirillum and/or Rhizophagus Mitigates the Deleterious Effects of Waterlogging in Plants under Enhanced CO2 Concentrations
by Eduardo Pereira Shimoia, Douglas Antônio Posso, Cristiane Jovelina da-Silva, Adriano Udich Bester, Nathalia Dalla Corte Bernardi, Ivan Ricardo Carvalho, Ana Cláudia Barneche de Oliveira, Luis Antonio de Avila and Luciano do Amarante
Nitrogen 2024, 5(4), 941-976; https://doi.org/10.3390/nitrogen5040061 - 15 Oct 2024
Viewed by 741
Abstract
Rising CO2 levels, as predicted by global climate models, are altering environmental factors such as the water cycle, leading to soil waterlogging and reduced oxygen availability for plant roots. These conditions result in decreased energy production, increased fermentative metabolism, impaired nutrient uptake, [...] Read more.
Rising CO2 levels, as predicted by global climate models, are altering environmental factors such as the water cycle, leading to soil waterlogging and reduced oxygen availability for plant roots. These conditions result in decreased energy production, increased fermentative metabolism, impaired nutrient uptake, reduced nitrogen fixation, and altered leaf gas exchanges, ultimately reducing crop productivity. Co-inoculation techniques involving multiple plant growth-promoting bacteria or arbuscular mycorrhizal fungi have shown promise in enhancing plant resilience to stress by improving nutrient uptake, biomass production, and nitrogen fixation. This study aimed to investigate carbon and nitrogen metabolism adaptations in soybean plants co-inoculated with Bradyrhizobium elkanii, Azospirillum brasilense, and Rhizophagus intraradices under waterlogged conditions in CO2-enriched environments. Plants were grown in pots in open-top chambers at ambient CO2 concentration (a[CO2]) and elevated CO2 concentration (e[CO2]). After reaching the V5 growth stage, the plants were subjected to waterlogging for seven days, followed by a four-day reoxygenation period. The results showed that plants’ co-inoculation under e[CO2] mitigated the adverse effects of waterlogging. Notably, plants inoculated solely with B. elkanii under e[CO2] displayed results similar to co-inoculated plants under a[CO2], suggesting that co-inoculation effectively mitigates the waterlogging stress, with plant physiological traits comparable to those observed under elevated CO2 conditions. Full article
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<p>Schematic representation of the treatments and experimental design. Soybean plants were cultivated under different CO<sub>2</sub> concentrations (ambient <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) and subsequent reoxygenation (four days). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple co-inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 2
<p>Leaf gaseous exchange. Net CO<sub>2</sub> assimilation (<span class="html-italic">A</span>) (<b>A</b>), stomatal conductance (<span class="html-italic">g<sub>s</sub></span>) (<b>B</b>), transpiration (<span class="html-italic">E</span>) (<b>C</b>), and internal CO<sub>2</sub> concentration (<span class="html-italic">C<sub>i</sub></span>) (<b>D</b>) in soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± standard deviation (SD), <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 3
<p>Pigment content. Chlorophyll <span class="html-italic">a</span> content (Chlo<span class="html-italic">_a</span>) (<b>A</b>), chlorophyll <span class="html-italic">b</span> (Chlo<span class="html-italic">_b</span>) (<b>B</b>), total chlorophyll (Chlo-total) (<b>C</b>), and carotenoids (Carot) (<b>D</b>) in soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± SD, <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 4
<p>Peroxide content and lipid peroxidation in leaves. Accumulation of hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) (<b>A</b>) and lipid peroxidation (MDA) (<b>B</b>) in leaves of soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± SD, <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 5
<p>Peroxide content and lipid peroxidation in roots. Accumulation of hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) (<b>A</b>) and lipid peroxidation (MDA) (<b>B</b>) in roots of soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± SD, <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 6
<p>Antioxidant enzyme activity in leaves. The activity of the enzymes superoxide dismutase (SOD) (<b>A</b>), catalase (CAT) (<b>B</b>), and ascorbate peroxidase (APX) (<b>C</b>) in leaves of soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± SD, <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 7
<p>Antioxidant enzyme activity in roots. The activity of the enzymes superoxide dismutase (SOD) (<b>A</b>), catalase (CAT) (<b>B</b>), and ascorbate peroxidase (APX) (<b>C</b>) in roots of soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± SD, <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 8
<p>Fermentative enzyme activity. Activity of the enzymes lactate dehydrogenase (LDH) (<b>A</b>), pyruvate decarboxylase (PDC) (<b>B</b>), alcohol dehydrogenase (ADH) (<b>C</b>), and alanine aminotransferase (Ala-At) (<b>D</b>) in roots of soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± SD, <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 9
<p>Shoot biometric parameters. Leaf area (LA) (<b>A</b>), shoot dry mass (SDM) (<b>B</b>), and stem diameter (SD) (<b>C</b>) in soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± SD, <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
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<p>Root biometric parameters. Accumulation of total soluble sugars (TSS) (<b>A</b>) and root fresh mass (RFM) (<b>B</b>) in roots of soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± SD, <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
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<p>Principal component analysis (PCA) was performed using PC1 and PC2 derived from morphophysiological and biochemical characteristics in the shoots of soybean plants grown under different symbiotic associations and subjected to waterlogging for seven days, followed by four days of reoxygenation, under either elevated CO<sub>2</sub> (<span class="html-italic">e</span>[CO<sub>2</sub>]) or ambient CO<sub>2</sub> (<span class="html-italic">a</span>[CO<sub>2</sub>]) conditions. I—first sampling during waterlogging; II—second sampling during reoxygenation; 400—plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>]; 700—plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>]; Ctrl—plants maintained as hydric controls; Wtlg—plants subjected to waterlogging; Rox—plants undergoing reoxygenation; IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation of <span class="html-italic">Azospirillum brasilense</span> and <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation of <span class="html-italic">Rhizophagus intraradices</span> and <span class="html-italic">Bradyrhizobium</span>; CAR—triple co-inoculation of <span class="html-italic">Bradyrhizobium</span>, <span class="html-italic">Azospirillum brasilense</span>, and <span class="html-italic">Rhizophagus intraradices</span>. Ellipses of different colors delineate the 95% confidence intervals, with colors chosen according to the water treatment in each CO<sub>2</sub> environment. Different symbols represent the microbiological treatments.</p>
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<p>Principal component analysis (PCA) was performed using PC1 and PC2 derived from morphophysiological and biochemical characteristics in the roots of soybean plants grown under different symbiotic associations and subjected to waterlogging for seven days, followed by four days of reoxygenation, under either elevated CO<sub>2</sub> (<span class="html-italic">e</span>[CO<sub>2</sub>]) or ambient CO<sub>2</sub> (<span class="html-italic">a</span>[CO<sub>2</sub>]) conditions. I—first sampling during waterlogging; II—second sampling during reoxygenation; 400—plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>]; 700—plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>]; Ctrl—plants maintained as hydric controls; Wtlg—plants subjected to waterlogging; Rox—plants undergoing reoxygenation; IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation of <span class="html-italic">Azospirillum brasilense</span> and <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation of <span class="html-italic">Rhizophagus intraradices</span> and <span class="html-italic">Bradyrhizobium</span>; CAR—triple co-inoculation of <span class="html-italic">Bradyrhizobium</span>, <span class="html-italic">Azospirillum brasilense</span>, and <span class="html-italic">Rhizophagus intraradices</span>. Ellipses of different colors delineate the 95% confidence intervals, with colors chosen according to the water treatment in each CO<sub>2</sub> environment. Different symbols represent the microbiological treatments.</p>
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<p>Hierarchical clustering analysis (HCA) of H<sub>2</sub>O<sub>2</sub>, MDA, Chlo<span class="html-italic">_a</span>, Chlo<span class="html-italic">_b</span>, SOD, CAT, and APX enzyme activities, gas exchange parameters (<span class="html-italic">g<sub>s</sub></span>, <span class="html-italic">E</span>, <span class="html-italic">A</span>, and <span class="html-italic">C<sub>i</sub></span>), and biometric measurements (LA, SD, SDW) in the shoots of waterlogged soybean plants grown under elevated CO<sub>2</sub> (<span class="html-italic">e</span>[CO<sub>2</sub>]) or ambient CO<sub>2</sub> (<span class="html-italic">a</span>[CO<sub>2</sub>]) conditions. Variations in red and blue colors indicate increases and decreases, respectively, for each variable. Light gray shades represent control plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>], while dark gray shades represent waterlogged plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>]. Red shades indicate control plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>], and blue shades represent waterlogged plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>].</p>
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<p>Hierarchical clustering analysis (HCA) of H<sub>2</sub>O<sub>2</sub>, MDA, Chlo<span class="html-italic">_a</span>, Chlo<span class="html-italic">_b</span>, SOD, CAT, and APX enzyme activities, gas exchange parameters (<span class="html-italic">g<sub>s</sub></span>, <span class="html-italic">E</span>, <span class="html-italic">A</span>, and <span class="html-italic">C<sub>i</sub></span>), and biometric measurements (LA, SD, SDW) in the shoots of reoxygenated soybean plants grown under elevated CO<sub>2</sub> (<span class="html-italic">e</span>[CO<sub>2</sub>]) or ambient CO<sub>2</sub> (<span class="html-italic">a</span>[CO<sub>2</sub>]) conditions. Variations in red and blue colors indicate increases and decreases, respectively, for each variable. Light gray shades represent control plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>], while dark gray shades represent waterlogged plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>]. Red shades indicate control plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>], and blue shades represent waterlogged plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>].</p>
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<p>Hierarchical clustering analysis (HCA) of H<sub>2</sub>O<sub>2</sub>, MDA, TSS, SOD, CAT, APX, ADH, LDH, PDC, and Ala-AT enzyme activities, as well as RFW in the roots of waterlogged soybean plants grown under elevated CO<sub>2</sub> (<span class="html-italic">e</span>[CO<sub>2</sub>]) or ambient CO<sub>2</sub> (<span class="html-italic">a</span>[CO<sub>2</sub>]) conditions. Variations plotted in red and blue colors on a log10 scale indicate increases and decreases, respectively, for each variable. Light gray shades represent control plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>], while dark gray shades denote waterlogged plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>]. Red shades indicate control plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>], and blue shades represent waterlogged plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>].</p>
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<p>Hierarchical clustering analysis (HCA) of H<sub>2</sub>O<sub>2</sub>, MDA, TSS, SOD, CAT, APX, ADH, LDH, PDC, and Ala-AT enzyme activities, as well as RFW in the roots of reoxygenated soybean plants grown under elevated CO<sub>2</sub> (<span class="html-italic">e</span>[CO<sub>2</sub>]) or ambient CO<sub>2</sub> (<span class="html-italic">a</span>[CO<sub>2</sub>]) conditions. Variations plotted in red and blue colors on a log10 scale indicate increases and decreases, respectively, for each variable. Light gray shades represent control plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>], while dark gray shades denote waterlogged plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>]. Red shades indicate control plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>], and blue shades represent waterlogged plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>].</p>
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14 pages, 307 KiB  
Review
Advances in the Study of NO3 Immobilization by Microbes in Agricultural Soils
by Xingling Wang and Ling Song
Nitrogen 2024, 5(4), 927-940; https://doi.org/10.3390/nitrogen5040060 - 11 Oct 2024
Viewed by 579
Abstract
The extensive application of nitrogen (N) fertilizers in agriculture has resulted in a considerable accumulation of N in the soil, particularly nitrate (NO3), which can be easily lost to the surrounding environments through leaching and denitrification. Improving the immobilization of [...] Read more.
The extensive application of nitrogen (N) fertilizers in agriculture has resulted in a considerable accumulation of N in the soil, particularly nitrate (NO3), which can be easily lost to the surrounding environments through leaching and denitrification. Improving the immobilization of NO3 by soil microorganisms in agriculture is crucial to improve soil N retention capacity and reduce the risk of NO3 loss. In this paper, we reviewed the significance of microbial immobilization of soil NO3 in soil N retention, the techniques to quantify soil gross microbial NO3 immobilization rate, and its influencing factors. Specifically, we discussed the respective contribution of fungi and bacteria in soil NO3 retention, and we clarified that the incorporation of organic materials is of vital importance in enhancing soil microbial NO3 immobilization capacities in agricultural soils. However, there is still a lack of research on the utilization of NO3 by microorganisms of different functional groups in soil due to the limited techniques. In the future, attention should be paid to how to regulate the microbial NO3 immobilization to make soil NO3 supply capacity match better with the crop N demand, thereby improving N use efficiency and reducing NO3 losses. Full article
(This article belongs to the Special Issue Microbial Nitrogen Cycling)
12 pages, 1041 KiB  
Article
Contrasting Life-Form Influences Guam Ficus Foliar Nutrient Dynamics
by Thomas E. Marler
Nitrogen 2024, 5(4), 915-926; https://doi.org/10.3390/nitrogen5040059 - 11 Oct 2024
Viewed by 527
Abstract
Tropical trees that remain evergreen and exhibit leaf litterfall that is gradual over time coexist with trees that are seasonally deciduous and exhibit pulsed litterfall. The manner in which these trees acquire, store, and contribute nutrients to the biogeochemical cycle may differ. Green [...] Read more.
Tropical trees that remain evergreen and exhibit leaf litterfall that is gradual over time coexist with trees that are seasonally deciduous and exhibit pulsed litterfall. The manner in which these trees acquire, store, and contribute nutrients to the biogeochemical cycle may differ. Green and senesced leaves from deciduous Ficus prolixa trees were compared with those from Ficus tinctoria on the island of Guam. The results enabled stoichiometry and resorption calculations. F. prolixa’s young green leaf nitrogen (N) and potassium (K) concentrations were double, and the phosphorus (P) concentration was triple, those of F. tinctoria. Concentrations converged as the leaves aged such that no differences in concentration occurred for senesced leaves, indicating that nutrient resorption proficiency did not differ between the two species. In contrast, the resorption efficiency was greater for F. prolixa than F. tinctoria for all three nutrients. The N:P values of 6–11 and K:P values of 5–7 were greater for young F. tinctoria leaves than young F. prolixa leaves. The N:K values were 1.1–1.6 and did not differ between the two species. No differences in pairwise stoichiometry occurred for senesced leaves for any of the nutrients. These Guam results conformed to global trends indicating that seasonally deciduous plants are more acquisitive and exhibit greater nutrient resorption efficiency. The differences in how these two native trees influence the community food web and nutrient cycling lies mostly in the volume and synchronicity of pulsed F. prolixa litter inputs, and not in differences in litter quality. These novel findings inform strategic foresight about sustaining ecosystem health in Guam’s heavily threatened forests. Full article
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<p>The number of leaf defoliation events for seven <span class="html-italic">Ficus prolixa</span> trees in Guam for each month. A total of 49 defoliation events were observed from September 2016 to January 2019.</p>
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<p>Leaf nutrient concentrations of <span class="html-italic">Ficus prolixa</span> (circles, solid lines) and <span class="html-italic">Ficus tinctoria</span> (triangles, dashed lines) leaves as influenced by age. (<b>A</b>) Nitrogen, (<b>B</b>) Phosphorus, (<b>C</b>) Potassium. Means ± SD, <span class="html-italic">n</span> = 7. Markers with the same letters are not different according to Tukey’s honest significant difference test.</p>
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<p>Nutrient stoichiometry of <span class="html-italic">Ficus prolixa</span> (circles, solid lines) and <span class="html-italic">Ficus tinctoria</span> (triangles, dashed lines) leaves as influenced by age. (<b>A</b>) Nitrogen:Potassium, (<b>B</b>) Nitrogen:Phosphorus, (<b>C</b>) Potassium:Phosphorus Means ± SD, <span class="html-italic">n</span> = 7. Markers with the same letters are not different according to Tukey’s honest significant difference test. Nitrogen: Potassium interaction, NS.</p>
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<p>Nutrient resorption efficiency of <span class="html-italic">Ficus prolixa</span> (open bars) and <span class="html-italic">Ficus tinctoria</span> (shaded bars) leaves. (<b>A</b>) Nitrogen, (<b>B</b>) Phosphorus, (<b>C</b>) Potassium. Means ± SD, <span class="html-italic">n</span> = 7.</p>
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12 pages, 1337 KiB  
Article
Removal of Nitrogen, Phosphates, and Chemical Oxygen Demand from Community Wastewater by Using Treatment Wetlands Planted with Ornamental Plants in Different Mineral Filter Media
by José Luis Marín-Muñiz, Gonzalo Ortega-Pineda, Irma Zitácuaro-Contreras, Monserrat Vidal-Álvarez, Karina E. Martínez-Aguilar, Luis M. Álvarez-Hernández and Sergio Zamora-Castro
Nitrogen 2024, 5(4), 903-914; https://doi.org/10.3390/nitrogen5040058 - 5 Oct 2024
Viewed by 1138
Abstract
This study aimed to explore the impact of various ornamental plants (Heliconia psittacorum, Etlingera elatior, Spatyphilum walisii) grown in different filter media (porous river rock (PR) and tepezyl (TZ)) on the removal of pollutants in vertical-subsurface-microcosm treatment wetlands (TWs). [...] Read more.
This study aimed to explore the impact of various ornamental plants (Heliconia psittacorum, Etlingera elatior, Spatyphilum walisii) grown in different filter media (porous river rock (PR) and tepezyl (TZ)) on the removal of pollutants in vertical-subsurface-microcosm treatment wetlands (TWs). This study also sought to assess the adaptability of these plant species to TW conditions. Twenty-four microcosm systems were utilized, with twelve containing PR and twelve containing TZ as the filter media. Each porous media type had three units planted with each species, and three were left unplanted. Rural community wastewater was treated in the TWs. The results showed no significant differences in the effects of the porous media on pollutant removal performance (p > 0.05). However, it was noted that while both porous media were efficient, TZ, a residue of construction materials, is recommended for sites facing economic constraints. Additionally, the removal efficiency was found to be independent of the type of ornamental plant used (p > 0.05); however, the measured parameters varied with plant spp. The adaptation of the plants varied depending on the species. H. psittacorum grew faster and produced a larger number of flowers compared to the other species (20–22 cm). S. wallisii typically produced 7–8 flowers. E. elatior did not produce flowers, and some plants showed signs of slight disease and pests, with the leaves turning yellow. In terms of plant biomass, the type of porous media used did not have a significant effect on the production of above (p = 0.111) or below-ground biomass (p = 0.092). The removal percentages for COD in the presence and absence of plants were in the ranges of 64–77% and 27–27.7%, respectively. For TN, the numbers were 52–65% and 30–31.8%, and for N-NO3, they were 54–60% and 12–18%. N-NH4 saw removal rates of 67–71% and 28–33%, while P-PO4 saw removal rates of 60–72% and 22–25%. The difference in removal percentages between microcosms with and without plants ranged from 30 to 50%, underscoring the importance of plants in the bio-removal processes (phytoremediation). These results reveal that incorporating ornamental plants in TWs with TZ for wastewater in rural areas holds great promise for enhancing the visual appeal of these systems and ultimately gaining public approval. Our findings also enable us to offer recommendations for selecting suitable plants and substrates, as well as designing combinations for TWs. Full article
(This article belongs to the Special Issue Soil Nitrogen Cycling—a Keystone in Ecological Sustainability)
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<p>Scheme of the microcosm TWs under study. PR: TWs with porous river rock, TZ: TWs with tepezil.</p>
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<p>Individual plant height over time.</p>
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<p>Effect of substrate media and plants on the biomass production of different ornamental vegetation. Values are average ± standard error. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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12 pages, 4316 KiB  
Article
Iodine-Enriched Urea Reduces Volatilization and Improves Nitrogen Uptake in Maize Plants
by João Victor da Costa Cezar, Everton Geraldo de Morais, Jucelino de Sousa Lima, Pedro Antônio Namorato Benevenute and Luiz Roberto Guimarães Guilherme
Nitrogen 2024, 5(4), 891-902; https://doi.org/10.3390/nitrogen5040057 - 2 Oct 2024
Viewed by 697
Abstract
Urea is the primary source of nitrogen (N) used in agriculture. However, it has a high N loss potential through volatilization. Various mechanisms can be employed to reduce N volatilization losses by inhibiting urease. When added to urea, iodine (I) has high potential [...] Read more.
Urea is the primary source of nitrogen (N) used in agriculture. However, it has a high N loss potential through volatilization. Various mechanisms can be employed to reduce N volatilization losses by inhibiting urease. When added to urea, iodine (I) has high potential for this purpose. Thus, this study aimed to determine whether adding I to urea reduces volatilization losses and increases N uptake in maize plants. Maize plants were cultivated in greenhouse conditions for 36 days. Urea treatments were applied at 15 days of testing, including iodine-enriched urea, conventional urea, and no urea application. Additionally, a study concerning N volatilization from urea was conducted using the same treatments under the same environmental conditions. Iodine was incorporated and adhered to urea, at an I concentration of 0.2%, using potassium iodate (KIO3). Under controlled conditions and over a short period of time, it was observed that the application of iodine-enriched urea increased the chlorophyll b content, root N accumulation, and total N accumulation in maize plants compared with conventional urea. Moreover, iodine-enriched urea reduced N losses from volatilization by 11% compared with conventional urea. The reduction in N volatilization correlated positively with the increased chlorophyll b, total chlorophyll, root N accumulation, and total N accumulation favored by the iodine-enriched urea treatment. Our findings demonstrated that adding I to urea is an efficient and promising strategy to reduce N losses and increase N uptake in plants. Full article
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<p>Conventional urea and iodine-enriched urea photos.</p>
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<p>A scheme for the nitrogen volatilization study that was carried out.</p>
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<p>Chlorophyll content, dry matter production, and nitrogen accumulation in maize according to the urea fertilization. Full bar: total chlorophyll (<b>a</b>) or total biomass production (<b>b</b>) or total nitrogen accumulation (<b>c</b>). <b><span class="html-italic">NS</span></b>, NS, and ns: without a statistical difference in total, shoot, and root dry matter production (<span class="html-italic">p</span> &gt; 0.05). The means in <a href="#nitrogen-05-00057-f003" class="html-fig">Figure 3</a>a follow the same scheme with bold–italic capitals, capital letters, and minuscule letters, not differentiating the treatments for total chlorophyll, chlorophyll <span class="html-italic">a</span> (Chl <span class="html-italic">a</span>), and chlorophyll <span class="html-italic">b</span> (Chl <span class="html-italic">b</span>) contents, respectively (<span class="html-italic">p</span> &gt; 0.05). The means in <a href="#nitrogen-05-00057-f003" class="html-fig">Figure 3</a>c follow the same scheme with bold–italic capitals, capital letters, and minuscule letters, not differentiating the treatments for total, shoot, and root nitrogen accumulations, respectively (<span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Cumulative nitrogen (N) volatilization according to the urea fertilization treatments. Treatments whose bootstrap-generated confidence intervals do not overlap in the figure were statistically different (<span class="html-italic">p</span> &lt; 0.05). The amount of N added was 150 mg per mini-lysimeter.</p>
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<p>Principal component analysis (<b>a</b>) and Pearson correlation (<b>b</b>) for variables analyzed in the maize cultivation and N volatilization studies. Significant correlation coefficients (<span class="html-italic">p</span> &lt; 0.05) are indicated by bold numbers, with positive and negative correlations distinguished by red and blue, respectively. White boxes indicate non-significance without numbers.</p>
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20 pages, 1919 KiB  
Article
A Mixture of Summer Legume and Nonlegume Cover Crops Enhances Winter Wheat Yield, Nitrogen Uptake, and Nitrogen Balance
by Jun Wang, Upendra M. Sainju and Shaohong Zhang
Nitrogen 2024, 5(4), 871-890; https://doi.org/10.3390/nitrogen5040056 - 2 Oct 2024
Viewed by 647
Abstract
Cover crops protecting soil erosion during the summer fallow in the monsoon weather may enhance dryland winter wheat yield and N relations. We examined the effects of four summer cover crops (soybean (Glycine max L., SB), sudangrass (Sorghum sudanense {Piper} Stapf, [...] Read more.
Cover crops protecting soil erosion during the summer fallow in the monsoon weather may enhance dryland winter wheat yield and N relations. We examined the effects of four summer cover crops (soybean (Glycine max L., SB), sudangrass (Sorghum sudanense {Piper} Stapf, SG), soybean and sudangrass mixture (SS), and no cover crop (CK)) and three N fertilization rates (0, 60, and 120 kg N ha−1) on winter wheat yield, quality, and N relations from 2017–2018 to 2020–2021 in the Loess Plateau of China. Cover crop biomass and N accumulation, soil mineral N, and winter wheat yield, protein concentration, and N uptake were greater for SB and SS than other cover crops at most N fertilization rates and years. The N fertilization rate had variable effects on these parameters. Winter wheat aboveground biomass and grain N productivities were greater for CK than other cover crops at all N fertilization rates and years. Nitrogen balance was greater for SS than other cover crops at 60 and 120 kg N ha−1 in all years. The SS with 120 kg N ha−1 can enhance soil mineral N, winter wheat yield and quality, and N balance compared to CK and SG with or without N fertilization rates. Full article
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<p>Cover crop biomass and N accumulation as affected by cover crop species and N fertilization rate from 2017–2018 to 2020–2021. SB denotes soybean; SG, sudangrass; and SS, a mixture of soybean and sudangrass. Markers followed by different letters are significantly different among cover crops at a N fertilization rate at <span class="html-italic">p</span> ≤ 0.05 by the least square means test.</p>
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<p>Soil mineral N at winter wheat planting and harvest as affected by cover crop species and N fertilization rate from 2017–2018 to 2020–2021. SB denotes soybean; SG, sudangrass; and SS, a mixture of soybean and sudangrass. Markers followed by different letters are significantly different among cover crops at a N fertilization rate at <span class="html-italic">p</span> ≤ 0.05 by the least square means test.</p>
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<p>Winter wheat aboveground biomass and grain yield as affected by cover crop species and N fertilization rate from 2017–2018 to 2020–2021. SB denotes soybean; SG, sudangrass; and SS, a mixture of soybean and sudangrass. Markers followed by different letters are significantly different among cover crops at a N fertilization rate at <span class="html-italic">p</span> ≤ 0.05 by the least square means test.</p>
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<p>Winter wheat 1000-seed weight and harvest index as affected by cover crop species and N fertilization rate from 2017–2018 to 2020–2021. SB denotes soybean; SG, sudangrass; and SS, a mixture of soybean and sudangrass. Markers followed by different letters are significantly different among cover crops at a N fertilization rate at <span class="html-italic">p</span> ≤ 0.05 by the least square means test.</p>
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<p>Winter wheat grain protein concentration and N balance as affected by cover crop species and N fertilization rate from 2017–2018 to 2020–2021. SB denotes soybean; SG, sudangrass; and SS, a mixture of soybean and sudangrass. Markers followed by different letters are significantly different among cover crops at a N fertilization rate at <span class="html-italic">p</span> ≤ 0.05 by the least square means test.</p>
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<p>Winter wheat aboveground biomass and grain N uptake as affected by cover crop species and N fertilization rate from 2017–2018 to 2020–2021. SB denotes soybean, SG; sudangrass; and SS, a mixture of soybean and sudangrass. Markers followed by different letters are significantly different among cover crops at a N fertilization rate at <span class="html-italic">p</span> ≤ 0.05 by the least square means test.</p>
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<p>Winter wheat aboveground biomass and grain N productivities (BNP and GNP, respectively) as affected by cover crop species and N fertilization rate from 2017–2018 to 2020–2021. SB denotes soybean; SG, sudangrass; and SS, a mixture of soybean and sudangrass. Markers followed by different letters among cover crops at a N fertilization rate are significantly different at <span class="html-italic">p</span> ≤ 0.05 by the least square means test.</p>
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<p>Winter wheat aboveground biomass and grain N recovery indices (BNRI and GNRI, respectively) as affected by cover crop species and N fertilization rate from 2017–2018 to 2020–2021. SB denotes soybean; SG, sudangrass; and SS, a mixture of soybean and sudangrass. Markers followed by different letters are significantly different among cover crops at a N fertilization rate at <span class="html-italic">p</span> ≤ 0.05 by the least square means test.</p>
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14 pages, 2055 KiB  
Article
Morpho-Physiological and Biochemical Responses in Prosopis laevigata Seedlings to Varied Nitrogen Sources
by Erickson Basave-Villalobos, Luis Manuel Valenzuela-Núñez, José Leonardo García-Rodríguez, Homero Sarmiento-López, José Luis García-Pérez, Celi Gloria Calixto-Valencia and José A. Sigala
Nitrogen 2024, 5(4), 857-870; https://doi.org/10.3390/nitrogen5040055 - 28 Sep 2024
Viewed by 883
Abstract
Nitrogen (N) fertilization promotes morphofunctional attributes that enhance plant performance under stress conditions, but the amount and form supplied modify the magnitude of plant responses. We assessed several morpho-physiological and biochemical responses of Prosopis laevigata seedlings to a high supply of N, provided [...] Read more.
Nitrogen (N) fertilization promotes morphofunctional attributes that enhance plant performance under stress conditions, but the amount and form supplied modify the magnitude of plant responses. We assessed several morpho-physiological and biochemical responses of Prosopis laevigata seedlings to a high supply of N, provided as either inorganic (NH4NO3) or organic (amino acids). Such N treatments were applied on four-month-old seedlings as a supplement of 90 mg N to a regular supply of 274 mg N plant−1. Nitrogen supply modified biomass allocation patterns between leaves and roots regardless of N form. Increased N input decreased photosynthetic capacity, even when plants had high internal N reserves. Organic N fertilization reduced the N use efficiency, but increased leaf and root amino acid concentrations. Proteins accumulated in stems in plants receiving inorganic N, while the organic N increased leaf proteins. High N supply promoted root starch accumulation irrespective of N form. Nitrogen supply did not directly influence plants’ regrowth capacity. Still, resprouting was correlated to initial root-to-shoot ratios and root starch, confirming the importance of roots as storage reserves of starch for recovering biomass after browsing. These findings have practical implications for designing nutritional management strategies in nurseries to improve seedling performance in afforestation efforts. Full article
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<p>Changes in biomass allocation patterns in <span class="html-italic">Prosopis laevigata</span> seedlings fertilized with additional N from two sources, inorganic N (NH<sub>4</sub>NO<sub>3</sub>) and organic N (amino acids), relative to control plants with regular N supply. Values presented are means with 95% confidence intervals. Symbols on each mean indicate the statistical comparison of fertilization treatments against the control (ns = not significant, ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Nitrogen concentration (<b>A</b>), N content (<b>B</b>), and N use efficiency (<b>C</b>) in <span class="html-italic">Prosopis laevigata</span> seedlings in response to inorganic (NH<sub>4</sub>NO<sub>3</sub>) and organic (amino acids) nitrogen fertilization. Data are means ± 1 SE. Horizontal square brackets indicate the statistical comparison of fertilization treatments against the control (ns = non-significant, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Amino acid concentration (<b>A</b>), proteins (<b>B</b>), starch (<b>C</b>), and soluble total sugars (<b>D</b>) in leaf, stem, and root tissues in <span class="html-italic">Prosopis laevigata</span> seedlings in response to inorganic (NH<sub>4</sub>NO<sub>3</sub>) and organic (amino acids) nitrogen fertilization. Data are means ± 1 SE. Horizontal square brackets indicate a comparison between treatments and control. Statistical significance: ns = non-significant, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001) fertilization treatments to control.</p>
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<p>Length of the dominant shoot after pruning in plants of <span class="html-italic">Prosopis laevigata</span> in response to inorganic (NH<sub>4</sub>NO<sub>3</sub>) and organic (amino acids) nitrogen fertilization. Data are means ± 1 SE.</p>
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<p>Relationship of sprout mass with the above-grown mass prior to pruning (<b>A</b>) and root-to-shoot ratio (<b>B</b>), and relationship of the number of new sprouts with starch concentration in the shoot (<b>C</b>) and root (<b>D</b>) in plants of <span class="html-italic">Prosopis laevigata</span> fertilized with inorganic (NH<sub>4</sub>NO<sub>3</sub>) and organic (amino acids) nitrogen. Data are means. Lines and shades area represent predicted values and a confident Interval of 95%. Values of independent variables (<span class="html-italic">x</span>-axis) were measured before pruning.</p>
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29 pages, 1326 KiB  
Review
Site-Specific Nitrogen Fertilizer Management Using Canopy Reflectance Sensors, Chlorophyll Meters and Leaf Color Charts: A Review
by Ali M. Ali, Haytham M. Salem and Bijay-Singh
Nitrogen 2024, 5(4), 828-856; https://doi.org/10.3390/nitrogen5040054 - 27 Sep 2024
Viewed by 1380
Abstract
The efficient management of nitrogen (N) on a site-specific basis is critical for the improvement of crop yield and the reduction of environmental impacts. This review examines the application of three primary technologies—canopy reflectance sensors, chlorophyll meters, and leaf color charts—in the context [...] Read more.
The efficient management of nitrogen (N) on a site-specific basis is critical for the improvement of crop yield and the reduction of environmental impacts. This review examines the application of three primary technologies—canopy reflectance sensors, chlorophyll meters, and leaf color charts—in the context of site-specific N fertilizer management. It delves into the development and effectiveness of these tools in assessing and managing crop N status. Reflectance sensors, which measure the reflection of light at specific wavelengths, provide valuable data on plant N stress and variability. The advent of innovative sensor technology, exemplified by the GreenSeeker, Crop Circle sensors, and Yara N-Sensor, has facilitated real-time monitoring and precise adjustments in fertilizer N application. Chlorophyll meters, including the SPAD meter and the atLeaf meter, quantify chlorophyll content and thereby estimate leaf N levels. This indirect yet effective method of managing N fertilization is based on the principle that the concentration of chlorophyll in leaves is proportional to the N content. These meters have become an indispensable component of precision agriculture due to their accuracy and ease of use. Leaf color charts, while less sophisticated, offer a cost-effective and straightforward approach to visual N assessment, particularly in developing regions. This review synthesizes research on the implementation of these technologies, emphasizing their benefits, constraints, and practical implications. Additionally, it explores integration strategies for combining these tools to enhance N use efficiency and sustainability in agriculture. The review culminates with recommendations for future research and development to further refine the precision and efficacy of N management practices. Full article
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<p>Global consumption of agricultural fertilizer from 1965 to 2021, disaggregated by nutrient. Data source: <a href="https://www.statista.com" target="_blank">https://www.statista.com</a> (accessed 26 July 2024).</p>
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<p>Illustrative example of normalized difference vegetation index (NDVI) measurement and analysis using a hand-held optical sensor.</p>
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<p>Example of chlorophyll index measurement and analysis using a SPAD meter.</p>
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<p>Illustration of an LCC showing a series of color panels representing varying levels of leaf greenness. The leaf in the example corresponds to panel number 3 on the LCC.</p>
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20 pages, 5107 KiB  
Article
Nitrate Removal by Floating Treatment Wetlands under Aerated and Unaerated Conditions: Field and Laboratory Results
by Jenna McCoy, Matt Chaffee, Aaron Mittelstet, Tiffany Messer and Steve Comfort
Nitrogen 2024, 5(4), 808-827; https://doi.org/10.3390/nitrogen5040053 - 25 Sep 2024
Viewed by 856
Abstract
Urban and storm water retention ponds eventually become eutrophic after years of receiving runoff water. In 2020, a novel biological and chemical treatment was initiated to remove accumulated nutrients from an urban retention pond that had severe algae and weed growth. Our approach [...] Read more.
Urban and storm water retention ponds eventually become eutrophic after years of receiving runoff water. In 2020, a novel biological and chemical treatment was initiated to remove accumulated nutrients from an urban retention pond that had severe algae and weed growth. Our approach installed two 6.1 m × 6.1 m floating treatment wetlands (FTWs) and two airlift pumps that contained slow-release lanthanum composites, which facilitated phosphate precipitation. Four years of treatment (2020–2023) resulted in median nitrate-N concentrations decreasing from 23 µg L−1 in 2020 to 1.3 µg L−1 in 2023, while PO4-P decreased from 42 µg L−1 to 19 µg L−1. The removal of N and P from the water column coincided with less algae, weeds, and pond muck (sediment), and greater dissolved oxygen (DO) concentrations and water clarity. To quantify the sustainability of this bio-chemical approach, we focused on quantifying nitrate removal rates beneath FTWs. By enclosing quarter sections (3.05 × 3.05 m) of the field-scale FTWs inside vinyl pool liners, nitrate removal rates were measured by spiking nitrate into the enclosed root zone. The first field experiment showed that DO concentrations inside the pool liners were well below the ambient values of the pond (<0.5 mg/L) and nitrate was quickly removed. The second field experiment quantified nitrate loss under a greater range of DO values (<0.5–7 mg/L) by including aeration as a treatment. Nitrate removal beneath FTWs was roughly one-third less when aerated versus unaerated. Extrapolating experimental removal rates to two full-sized FTWs installed in the pond, we estimate between 0.64 to 3.73 kg of nitrate-N could be removed over a growing season (May–September). Complementary laboratory mesocosm experiments using similar treatments to field experiments also exhibited varying nitrate removal rates that were dependent on DO concentrations. Using an average annual removal rate of 1.8 kg nitrate-N, we estimate the two full-size FTWs could counter 14 to 56% of the annual incoming nitrate load from the contributing watershed. Full article
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<p>Photos and schematics of biological and chemical approach used by McKercher et al. [<a href="#B2-nitrogen-05-00053" class="html-bibr">2</a>] to restore eutrophic ponds. Photos are of Densmore Pond (Lincoln, NE, USA).</p>
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<p>Photographs of field experimental units (pools) used for Experiment 1 and Experiment 2.</p>
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<p>Schematic and photograph of lab-scale mesocosms.</p>
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<p>Photographs of Densmore Pond from 2020 to 2023.</p>
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<p>Temporal changes in PO<sub>4</sub>-P, NO<sub>3</sub>-N, and dissolved oxygen concentrations in Densmore Pond from 2020 through 2023. Error bars on symbols represent standard errors; where absent, bars fall within symbols.</p>
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<p>Field Experiment 1. Top: Temporal changes in dissolved oxygen concentrations inside treatment pools and outside (ambient). Bottom: Temporal changes in NO<sub>3</sub>-N concentrations in aerated, unaerated, and control pools. Error bars on symbols represent standard errors; where absent, bars fall within symbols.</p>
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<p>Mesocosm Experiment 2. Top: Temporal changes in dissolved oxygen concentrations inside treatment pools and outside (ambient). Bottom: Temporal changes in NO<sub>3</sub>-N concentrations in aerated, unaerated, and control pools. Error bars on symbols represent standard errors; where absent, bars fall within symbols.</p>
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<p>(<b>A</b>,<b>B</b>) Photographs of FTW1 and FTW2 (above water). (<b>C</b>) Underwater photographs of rooting system of FTW1.</p>
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<p>(<b>Top Left</b>): Diurnal fluctuations in dissolved oxygen concentrations beneath FTW 1 (red star). (<b>Bottom Left</b>): Diurnal fluctuations in dissolved oxygen concentrations outside of FTW1 (yellow star). (<b>Right</b>): Schematic of MS-5 Sensor deployments.</p>
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<p>Laboratory Experiment 1. Top: Temporal changes in dissolved oxygen concentrations in aerated and unaerated mesocosms. Bottom: Temporal changes in NO<sub>3</sub>-N concentrations in aerated, unaerated mesocosms. Error bars on symbols represent standard errors; where absent, bars fall within symbols.</p>
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<p>Laboratory Experiment 2. Top: Temporal changes in dissolved oxygen concentrations in aerated and unaerated mesocosms. Bottom: Temporal changes in NO<sub>3</sub>-N concentrations in aerated and unaerated mesocosms. Error bars on symbols represent standard errors; where absent, bars fall within symbols.</p>
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