Journal Description
Nitrogen
Nitrogen
is an international, peer-reviewed, open access journal on the whole field of nitrogen research published quarterly online by MDPI.
- Open Access—free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within ESCI (Web of Science), Scopus, CAPlus / SciFinder, and other databases.
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 19.3 days after submission; acceptance to publication is undertaken in 4.7 days (median values for papers published in this journal in the first half of 2024).
- Journal Rank: CiteScore - Q2 (Agricultural and Biological Sciences (miscellaneous))
- Recognition of Reviewers: APC discount vouchers, optional signed peer-review and reviewer names published annually in the journal.
Impact Factor:
1.6 (2023);
5-Year Impact Factor:
1.6 (2023)
Latest Articles
Peanut Cake as an Alternative Protein Source to Soybean Meal on Performance, Nitrogen Utilization, and Carcass Traits in Feedlot Lambs
Nitrogen 2024, 5(4), 1092-1106; https://doi.org/10.3390/nitrogen5040070 - 28 Nov 2024
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
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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
Open AccessArticle
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
Abstract
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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
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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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Open AccessSystematic 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
Abstract
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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
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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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Figure 1
<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> Full article ">Figure 2
<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> Full article ">Figure 3
<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> Full article ">Figure 4
<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> Full article ">Figure 5
<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> Full article ">Figure 6
<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> Full article ">Figure 7
<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> Full article ">Figure 8
<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> Full article ">Figure 9
<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> Full article ">Figure 10
<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> Full article ">
<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> Full article ">Figure 2
<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> Full article ">Figure 3
<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> Full article ">Figure 4
<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> Full article ">Figure 5
<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> Full article ">Figure 6
<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> Full article ">Figure 7
<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> Full article ">Figure 8
<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> Full article ">Figure 9
<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> Full article ">Figure 10
<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> Full article ">
Open AccessArticle
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
Abstract
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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
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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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Open AccessArticle
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
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
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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
(This article belongs to the Special Issue Alternatives to Mineral Nitrogen Fertilizers in Agriculture: State of the Art, Challenges and Future Prospects)
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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> Full article ">Figure 2
<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> Full article ">Figure 3
<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> Full article ">Figure 4
<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> Full article ">Figure 5
<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> Full article ">Figure 6
<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> Full article ">Figure 7
<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> Full article ">Figure 8
<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> Full article ">Figure 9
<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> Full article ">Figure 10
<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> Full article ">
<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> Full article ">Figure 2
<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> Full article ">Figure 3
<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> Full article ">Figure 4
<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> Full article ">Figure 5
<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> Full article ">Figure 6
<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> Full article ">Figure 7
<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> Full article ">Figure 8
<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> Full article ">Figure 9
<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> Full article ">Figure 10
<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> Full article ">
Open AccessArticle
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
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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
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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
Figure 1
Figure 1
<p>Elevation map of the study area watershed in eastern North Carolina, USA, showing elevation categories from <10 m to >150 m. The inset map provides the geographic location of the watershed within the broader southeastern U.S. region.</p> Full article ">Figure 2
<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> Full article ">Figure 3
<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> Full article ">Figure 4
<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> Full article ">Figure 5
<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> Full article ">Figure 6
<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> Full article ">Figure 7
<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> Full article ">Figure 8
<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> Full article ">Figure 9
<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> Full article ">
<p>Elevation map of the study area watershed in eastern North Carolina, USA, showing elevation categories from <10 m to >150 m. The inset map provides the geographic location of the watershed within the broader southeastern U.S. region.</p> Full article ">Figure 2
<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> Full article ">Figure 3
<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> Full article ">Figure 4
<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> Full article ">Figure 5
<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> Full article ">Figure 6
<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> Full article ">Figure 7
<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> Full article ">Figure 8
<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> Full article ">Figure 9
<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> Full article ">
Open AccessArticle
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
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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
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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> Full article ">Figure 2
<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> Full article ">Figure 3
<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> Full article ">Figure 4
<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> Full article ">Figure 5
<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> Full article ">
<p>Weather conditions during the growing seasons.</p> Full article ">Figure 2
<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> Full article ">Figure 3
<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> Full article ">Figure 4
<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> Full article ">Figure 5
<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> Full article ">
Open AccessArticle
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
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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
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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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<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> Full article ">
<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> Full article ">
Open AccessArticle
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
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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
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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> Full article ">Figure 2
<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> Full article ">
<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> Full article ">Figure 2
<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> Full article ">
Open AccessArticle
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
Abstract
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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,
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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.
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Figure 1
Figure 1
<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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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 10
<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> < 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> < 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> < 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 11
<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> Full article ">Figure 12
<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> Full article ">Figure 13
<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> Full article ">Figure 14
<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> Full article ">Figure 15
<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> Full article ">Figure 16
<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> Full article ">
<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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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> < 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 10
<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> < 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> < 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> < 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 11
<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> Full article ">Figure 12
<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> Full article ">Figure 13
<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> Full article ">Figure 14
<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> Full article ">Figure 15
<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> Full article ">Figure 16
<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> Full article ">
Open AccessReview
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
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
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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)
Open AccessArticle
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
Abstract
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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
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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
Figure 1
Figure 1
<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> Full article ">Figure 2
<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> Full article ">Figure 3
<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> Full article ">Figure 4
<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> Full article ">
<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> Full article ">Figure 2
<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> Full article ">Figure 3
<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> Full article ">Figure 4
<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> Full article ">
Open AccessArticle
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
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).
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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> Full article ">Figure 2
<p>Individual plant height over time.</p> Full article ">Figure 3
<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> < 0.05).</p> Full article ">
<p>Scheme of the microcosm TWs under study. PR: TWs with porous river rock, TZ: TWs with tepezil.</p> Full article ">Figure 2
<p>Individual plant height over time.</p> Full article ">Figure 3
<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> < 0.05).</p> Full article ">
Open AccessArticle
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
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
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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
(This article belongs to the Special Issue Alternatives to Mineral Nitrogen Fertilizers in Agriculture: State of the Art, Challenges and Future Prospects)
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Figure 1
<p>Conventional urea and iodine-enriched urea photos.</p> Full article ">Figure 2
<p>A scheme for the nitrogen volatilization study that was carried out.</p> Full article ">Figure 3
<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> > 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> > 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> > 0.05).</p> Full article ">Figure 4
<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> < 0.05). The amount of N added was 150 mg per mini-lysimeter.</p> Full article ">Figure 5
<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> < 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> Full article ">
<p>Conventional urea and iodine-enriched urea photos.</p> Full article ">Figure 2
<p>A scheme for the nitrogen volatilization study that was carried out.</p> Full article ">Figure 3
<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> > 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> > 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> > 0.05).</p> Full article ">Figure 4
<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> < 0.05). The amount of N added was 150 mg per mini-lysimeter.</p> Full article ">Figure 5
<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> < 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> Full article ">
Open AccessArticle
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
Abstract
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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,
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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
Figure 1
Figure 1
<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> Full article ">Figure 2
<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> Full article ">Figure 3
<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> Full article ">Figure 4
<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> Full article ">Figure 5
<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> Full article ">Figure 6
<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> Full article ">Figure 7
<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> Full article ">Figure 8
<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> Full article ">
<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> Full article ">Figure 2
<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> Full article ">Figure 3
<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> Full article ">Figure 4
<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> Full article ">Figure 5
<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> Full article ">Figure 6
<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> Full article ">Figure 7
<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> Full article ">Figure 8
<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> Full article ">
Open AccessArticle
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
Abstract
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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
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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
Figure 1
Figure 1
<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> < 0.01).</p> Full article ">Figure 2
<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> < 0.01, *** <span class="html-italic">p</span> < 0.001).</p> Full article ">Figure 3
<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> < 0.01, *** <span class="html-italic">p</span> < 0.001) fertilization treatments to control.</p> Full article ">Figure 4
<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> Full article ">Figure 5
<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> Full article ">
<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> < 0.01).</p> Full article ">Figure 2
<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> < 0.01, *** <span class="html-italic">p</span> < 0.001).</p> Full article ">Figure 3
<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> < 0.01, *** <span class="html-italic">p</span> < 0.001) fertilization treatments to control.</p> Full article ">Figure 4
<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> Full article ">Figure 5
<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> Full article ">
Open AccessReview
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
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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
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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
Figure 1
Figure 1
<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> Full article ">Figure 2
<p>Illustrative example of normalized difference vegetation index (NDVI) measurement and analysis using a hand-held optical sensor.</p> Full article ">Figure 3
<p>Example of chlorophyll index measurement and analysis using a SPAD meter.</p> Full article ">Figure 4
<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> Full article ">
<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> Full article ">Figure 2
<p>Illustrative example of normalized difference vegetation index (NDVI) measurement and analysis using a hand-held optical sensor.</p> Full article ">Figure 3
<p>Example of chlorophyll index measurement and analysis using a SPAD meter.</p> Full article ">Figure 4
<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> Full article ">
Open AccessArticle
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
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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
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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
Figure 1
Figure 1
<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> Full article ">Figure 2
<p>Photographs of field experimental units (pools) used for Experiment 1 and Experiment 2.</p> Full article ">Figure 3
<p>Schematic and photograph of lab-scale mesocosms.</p> Full article ">Figure 4
<p>Photographs of Densmore Pond from 2020 to 2023.</p> Full article ">Figure 5
<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> Full article ">Figure 6
<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> Full article ">Figure 7
<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> Full article ">Figure 8
<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> Full article ">Figure 9
<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> Full article ">Figure 10
<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> Full article ">Figure 11
<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> Full article ">
<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> Full article ">Figure 2
<p>Photographs of field experimental units (pools) used for Experiment 1 and Experiment 2.</p> Full article ">Figure 3
<p>Schematic and photograph of lab-scale mesocosms.</p> Full article ">Figure 4
<p>Photographs of Densmore Pond from 2020 to 2023.</p> Full article ">Figure 5
<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> Full article ">Figure 6
<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> Full article ">Figure 7
<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> Full article ">Figure 8
<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> Full article ">Figure 9
<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> Full article ">Figure 10
<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> Full article ">Figure 11
<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> Full article ">
Open AccessArticle
Plantago Species Show Germination Improvement as a Function of Nitrate and Temperature
by
António Teixeira, Pietro P. M. Iannetta and Peter E. Toorop
Nitrogen 2024, 5(3), 790-807; https://doi.org/10.3390/nitrogen5030052 - 20 Sep 2024
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At the optimum temperature, which is the ideal range in which seeds germinate most efficiently, seed germination may be lower than expected under favorable conditions, and this is indicative of seed dormancy. Also, germination may be enhanced by additional and interacting factors, such
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At the optimum temperature, which is the ideal range in which seeds germinate most efficiently, seed germination may be lower than expected under favorable conditions, and this is indicative of seed dormancy. Also, germination may be enhanced by additional and interacting factors, such as nitrate and light. However, little is known about the interplay between temperature, nitrate, and seed germination. Using seeds from 22 accessions of four Plantago species that occupy distinct pedoclimates, we applied a factorial experimental design to assess the relationship between exogenously applied nitrate (KNO3) and temperature on germination in a Petri dish experiment. The data explore the relationship between seed germination, temperatures, and seed- and maternal-source soil N content as either nitrite (NO2−), nitrate (NO3−), or ammonium (NH4+). The interpretation also considered the total N and C contents of seeds, and the soil of the maternal plant (of the test seed) sources. Significant interspecific effects of nitrate and temperature on seed germination were observed. The capacity of nitrate to enhance final germination may be diminished substantially at supra-optimum temperatures, e.g., P. lagopus germination at 15 °C was 7% lower than that seen for water-only treatment. In contrast, at sub-optimum and alternating temperatures, nitrate enhanced final germination differentially across the species tested. This suggests a shift to enhanced germination at lower temperatures in the presence of sufficient soil nitrate, facilitating seedling establishment earlier in the growing season. The seeds of some Plantago species showed increased germination as a function of nitrate and temperature, particularly those of P. lagopus. The findings indicate that species (and genotype) responses correlated with the prevailing temperature and rainfall patterns of the locality; such local adaptation would ensure that seed germination and establishment occur during a period when environmental conditions are optimal.
Full article
Figure 1
Figure 1
<p>Map illustrating the Iberian Peninsula’s pedoclimatic regions (adapted from Metzger, Marc J., 2018), where the seed accessions were collected. Each species is represented by a colored circle.</p> Full article ">Figure 2
<p>Seed germination of the 22 accessions of four <span class="html-italic">Plantago</span> species from three different pedoclimates of the Iberian Peninsula. (<b>A</b>) Generalized linear mixed model (GLMM) of seed incubation temperatures and imbibition conditions on germination percentage by species. (<b>B</b>) Violin plots showing percentage germination of water−imbibed seeds; (<b>C</b>) potassium nitrate solution−imbibed seeds (10 mM KNO3); (<b>D</b>) water−imbibed seeds at alternating and constant temperatures; (<b>E</b>) potassium nitrate solution−imbibed seeds at alternating and constant temperatures; (<b>F</b>) water−imbibed seeds by pedoclimate region; (<b>G</b>) nitrate solution−imbibed seeds by pedoclimate region. The species (and number of accessions) used were: <span class="html-italic">P. albicans</span> (5); <span class="html-italic">P. coronopus</span> (5); <span class="html-italic">P. lagopus</span> (5); and <span class="html-italic">P. lanceolata</span> (7). Asterisks indicate significant statistical differences: * <span class="html-italic">p</span> ≤ 0.05; *** <span class="html-italic">p</span> ≤ 0.001.</p> Full article ">Figure 3
<p>Heatmap cluster diagram of the delta variation (<span class="html-italic">Δ</span>) for the percentage of seed germination between constant (const.) and alternating (alter.) temperature incubations in nitrate solution and water (<span class="html-italic">Δ</span>KNO<sub>3</sub>: H<sub>2</sub>O); or the reciprocal percentage germination incubations (<span class="html-italic">Δ</span>alter.:const). Means are the average of three replicates <span class="html-italic">Δ</span>%.</p> Full article ">Figure 4
<p>Factor analysis of mixed data (FAMD) of the final percentage germination of seeds from 22 accessions of <span class="html-italic">Plantago albicans</span>, <span class="html-italic">P. coronopus</span>, <span class="html-italic">P. lagopus</span>, and <span class="html-italic">P. lanceolata</span> (<span class="html-italic">n</span> = 5, 5, 5, and 7, respectively). (<b>A</b>) Water imbibition at constant temperature; (<b>B</b>) water imbibition at alternating temperatures; (<b>C</b>) nitrate solution imbibition (10 mM) at constant temperature; (<b>D</b>) nitrate solution imbibition (10 mM) at alternating temperatures. Each colored ellipse highlights clustering of accessions of the same species, and labels represent the collection region of each accession, where: LUS—Lusitanian; MDS—Mediterranean South; MDN—Mediterranean North. All tests were performed with the means of data calculated from the average of three experimental replicates.</p> Full article ">Figure 5
<p>Generalized linear models testing the effect of soil nitrogen forms and carbon on species germination (based on the second axis of the FAMD). (<b>A</b>) Water imbibition and (<b>B</b>) KNO<sub>3</sub> solution imbibition at constant temperatures. All tests were performed with three replicates for each accession.</p> Full article ">Figure 6
<p>Generalized linear models testing the effects of soil nitrogen forms and carbon on species germination (based on second axis of the FAMD). (<b>A</b>) Water imbibition and (<b>B</b>) KNO<sub>3</sub> solution imbibition at alternating temperatures. All tests were performed with three replicates for each accession.</p> Full article ">Figure 7
<p>Generalized linear models testing the effect of soil and seed nitrogen forms and carbon on species <span class="html-italic">T<sub>b</sub></span> and θ<sub>50</sub> of seeds imbibed in H<sub>2</sub>O and KNO<sub>3</sub> solution (10 mM), incubated at constant and alternating temperatures. All tests were performed with three replicates for each accession. Asterisks indicate significant statistical differences: * <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.001.</p> Full article ">
<p>Map illustrating the Iberian Peninsula’s pedoclimatic regions (adapted from Metzger, Marc J., 2018), where the seed accessions were collected. Each species is represented by a colored circle.</p> Full article ">Figure 2
<p>Seed germination of the 22 accessions of four <span class="html-italic">Plantago</span> species from three different pedoclimates of the Iberian Peninsula. (<b>A</b>) Generalized linear mixed model (GLMM) of seed incubation temperatures and imbibition conditions on germination percentage by species. (<b>B</b>) Violin plots showing percentage germination of water−imbibed seeds; (<b>C</b>) potassium nitrate solution−imbibed seeds (10 mM KNO3); (<b>D</b>) water−imbibed seeds at alternating and constant temperatures; (<b>E</b>) potassium nitrate solution−imbibed seeds at alternating and constant temperatures; (<b>F</b>) water−imbibed seeds by pedoclimate region; (<b>G</b>) nitrate solution−imbibed seeds by pedoclimate region. The species (and number of accessions) used were: <span class="html-italic">P. albicans</span> (5); <span class="html-italic">P. coronopus</span> (5); <span class="html-italic">P. lagopus</span> (5); and <span class="html-italic">P. lanceolata</span> (7). Asterisks indicate significant statistical differences: * <span class="html-italic">p</span> ≤ 0.05; *** <span class="html-italic">p</span> ≤ 0.001.</p> Full article ">Figure 3
<p>Heatmap cluster diagram of the delta variation (<span class="html-italic">Δ</span>) for the percentage of seed germination between constant (const.) and alternating (alter.) temperature incubations in nitrate solution and water (<span class="html-italic">Δ</span>KNO<sub>3</sub>: H<sub>2</sub>O); or the reciprocal percentage germination incubations (<span class="html-italic">Δ</span>alter.:const). Means are the average of three replicates <span class="html-italic">Δ</span>%.</p> Full article ">Figure 4
<p>Factor analysis of mixed data (FAMD) of the final percentage germination of seeds from 22 accessions of <span class="html-italic">Plantago albicans</span>, <span class="html-italic">P. coronopus</span>, <span class="html-italic">P. lagopus</span>, and <span class="html-italic">P. lanceolata</span> (<span class="html-italic">n</span> = 5, 5, 5, and 7, respectively). (<b>A</b>) Water imbibition at constant temperature; (<b>B</b>) water imbibition at alternating temperatures; (<b>C</b>) nitrate solution imbibition (10 mM) at constant temperature; (<b>D</b>) nitrate solution imbibition (10 mM) at alternating temperatures. Each colored ellipse highlights clustering of accessions of the same species, and labels represent the collection region of each accession, where: LUS—Lusitanian; MDS—Mediterranean South; MDN—Mediterranean North. All tests were performed with the means of data calculated from the average of three experimental replicates.</p> Full article ">Figure 5
<p>Generalized linear models testing the effect of soil nitrogen forms and carbon on species germination (based on the second axis of the FAMD). (<b>A</b>) Water imbibition and (<b>B</b>) KNO<sub>3</sub> solution imbibition at constant temperatures. All tests were performed with three replicates for each accession.</p> Full article ">Figure 6
<p>Generalized linear models testing the effects of soil nitrogen forms and carbon on species germination (based on second axis of the FAMD). (<b>A</b>) Water imbibition and (<b>B</b>) KNO<sub>3</sub> solution imbibition at alternating temperatures. All tests were performed with three replicates for each accession.</p> Full article ">Figure 7
<p>Generalized linear models testing the effect of soil and seed nitrogen forms and carbon on species <span class="html-italic">T<sub>b</sub></span> and θ<sub>50</sub> of seeds imbibed in H<sub>2</sub>O and KNO<sub>3</sub> solution (10 mM), incubated at constant and alternating temperatures. All tests were performed with three replicates for each accession. Asterisks indicate significant statistical differences: * <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.001.</p> Full article ">
Open AccessArticle
Litter Decomposition Rates of Four Species of Agroecological Importance in the Peruvian Coast and Andean Highland
by
Tomás Samaniego, Jorge Ramirez and Richard Solórzano
Nitrogen 2024, 5(3), 772-789; https://doi.org/10.3390/nitrogen5030051 - 13 Sep 2024
Abstract
Crop residue decomposition is fundamental for ecosystems, influencing carbon cycling, organic matter accumulation, and promoting plant development through nutrient release. Therefore, this study aimed to ascertain the rate of decomposition of four commonly cultivated crops (alfalfa, maize, avocado, and eucalyptus) along the northern
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Crop residue decomposition is fundamental for ecosystems, influencing carbon cycling, organic matter accumulation, and promoting plant development through nutrient release. Therefore, this study aimed to ascertain the rate of decomposition of four commonly cultivated crops (alfalfa, maize, avocado, and eucalyptus) along the northern coast of Lima (Huaral) and in the Ancash Mountain range (Jangas) areas. Decomposition rates were assessed using mass loss from decomposition bags measuring 15 × 10 cm, filled with 10–15 g of material tailored to each species, and buried at a depth of approximately 5 cm. Sampling occurred every three months over a year, totaling four sampling events with three replicates each, resulting in ninety-six experimental units. The findings demonstrate that the decomposition rates and the release of nutrients were markedly greater in Huaral for maize and avocado. In contrast, these rates were notably elevated in Jangas for alfalfa and eucalyptus. The leaf litter of avocado and eucalyptus (tree) had periods of accumulation and release of heavy metals such as Cd. The initial C/N ratio was one of the main factors related to the nutrient decomposition rate; in contrast, there were no significant relationships with soil properties at the study sites.
Full article
(This article belongs to the Topic Carbon and Nitrogen Cycling in Agro-Ecosystems and Other Anthropogenically Maintained Ecosystems)
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Figure 1
Figure 1
<p>Location of the two study areas: district of “Jangas” and district of “Huaral.”</p> Full article ">Figure 2
<p>Monthly mean temperatures (lines) and rainfall (bars) [<a href="#B24-nitrogen-05-00051" class="html-bibr">24</a>].</p> Full article ">Figure 3
<p>(<b>a</b>) The litter bags that were used; (<b>b</b>) Installation of litter bags in the soil at a 5 cm depth; (<b>c</b>) Installation in the alfalfa plot; (<b>d</b>) Installation in the maize plot; (<b>e</b>) Installation in the avocado plot and (<b>f</b>) Installation in the eucalyptus plot.</p> Full article ">Figure 4
<p>Weight of litter bag remaining (as a percentage of initial weight) for each species and site against time (year fraction), and the fitted exponential decay curves (y = 100 e<sup>−kt</sup>) calculated using nonlinear regression (k values are given in <a href="#nitrogen-05-00051-t003" class="html-table">Table 3</a>).</p> Full article ">Figure 5
<p>(<b>a</b>) C remaining in four crops; (<b>b</b>) N remaining in the four crops; (<b>c</b>) P remaining in the four crops; (<b>d</b>) K remaining in the four crops. Different letters (a,b) indicate significant differences in the Student’s <span class="html-italic">t</span>-test.</p> Full article ">Figure 6
<p>(<b>a</b>) Alfalfa C/N; (<b>b</b>) Maize C/N; (<b>c</b>) Avocado C/N; (<b>d</b>) Eucalyptus C/N ratio. Different letters (a,b,c,d) indicate significant differences.</p> Full article ">Figure 7
<p>(<b>a</b>) Fe remaining in four crops; (<b>b</b>) Cu remaining in the four crops; (<b>c</b>) Zn remaining in the four crops; (<b>d</b>) Cd remaining in the four. Different letters (a,b) indicate significant differences in. Student’s <span class="html-italic">t</span>-test.</p> Full article ">Figure 8
<p>Spearman association between exponential decay model (Kfactor) with other soil and litter parameters. C/Ni: Initial C/N; C/Nf: Final C/N; Da: Bulk density; OM: organic matter; Ci: Initial carbon; Ni: Initial nitrogen; Cf: Final carbon; Nf: Final nitrogen. *** = <span class="html-italic">p</span>-values < 0.001; ** <span class="html-italic">p</span>-values < 0.01; * <span class="html-italic">p</span>-values < 0.05.</p> Full article ">
<p>Location of the two study areas: district of “Jangas” and district of “Huaral.”</p> Full article ">Figure 2
<p>Monthly mean temperatures (lines) and rainfall (bars) [<a href="#B24-nitrogen-05-00051" class="html-bibr">24</a>].</p> Full article ">Figure 3
<p>(<b>a</b>) The litter bags that were used; (<b>b</b>) Installation of litter bags in the soil at a 5 cm depth; (<b>c</b>) Installation in the alfalfa plot; (<b>d</b>) Installation in the maize plot; (<b>e</b>) Installation in the avocado plot and (<b>f</b>) Installation in the eucalyptus plot.</p> Full article ">Figure 4
<p>Weight of litter bag remaining (as a percentage of initial weight) for each species and site against time (year fraction), and the fitted exponential decay curves (y = 100 e<sup>−kt</sup>) calculated using nonlinear regression (k values are given in <a href="#nitrogen-05-00051-t003" class="html-table">Table 3</a>).</p> Full article ">Figure 5
<p>(<b>a</b>) C remaining in four crops; (<b>b</b>) N remaining in the four crops; (<b>c</b>) P remaining in the four crops; (<b>d</b>) K remaining in the four crops. Different letters (a,b) indicate significant differences in the Student’s <span class="html-italic">t</span>-test.</p> Full article ">Figure 6
<p>(<b>a</b>) Alfalfa C/N; (<b>b</b>) Maize C/N; (<b>c</b>) Avocado C/N; (<b>d</b>) Eucalyptus C/N ratio. Different letters (a,b,c,d) indicate significant differences.</p> Full article ">Figure 7
<p>(<b>a</b>) Fe remaining in four crops; (<b>b</b>) Cu remaining in the four crops; (<b>c</b>) Zn remaining in the four crops; (<b>d</b>) Cd remaining in the four. Different letters (a,b) indicate significant differences in. Student’s <span class="html-italic">t</span>-test.</p> Full article ">Figure 8
<p>Spearman association between exponential decay model (Kfactor) with other soil and litter parameters. C/Ni: Initial C/N; C/Nf: Final C/N; Da: Bulk density; OM: organic matter; Ci: Initial carbon; Ni: Initial nitrogen; Cf: Final carbon; Nf: Final nitrogen. *** = <span class="html-italic">p</span>-values < 0.001; ** <span class="html-italic">p</span>-values < 0.01; * <span class="html-italic">p</span>-values < 0.05.</p> Full article ">
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