Exploring the Impact of Metal-Based Nanofertilizers: A Case Study on Sunflower Pollen Morphology and Yield in Field Conditions
Abstract
:1. Introduction
2. Materials and Methods
2.1. Origin and Characterization of Metal-Related Nanoparticles Applied on Sunflower
2.2. Plant Material
2.3. Site Description
2.4. Monitoring Climate-Seasonal Variation
2.5. Field Experimental Setup
2.6. Assessment of Sunflower Yield
2.7. Examination of Critical Pollen Morphological Attributes Using Scanning Electron Microscopy
3. Results
3.1. Analysis of Sunflower Pollen Morphology under the Influence of Nanoparticle Application in Field Conditions
3.2. Evaluation of Sunflower Yield with Metal-Based Nanoparticle Application
4. Discussion
4.1. The Effect of ZnO-NPs and Conventional Agrochemical ZnSO4 on Sunflower Pollen Morphology and Their Impact on Yield
4.2. The Impact of Iron Nanoparticles on Pollen Morphology and Their Associations with Sunflower Yield
4.3. The Examined Impact of AuSi NPs on Pollen Morphology and Their Correlation with Sunflower Crop Yield
4.4. The Effect of TiO2-NPs Treatment on Sunflower Pollen Morphology and Yield
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Sunflower Hybrid | Applied Nanoparticles and Agrochemical | |||||
---|---|---|---|---|---|---|
ZnO-NPs | TiO2-NPs | Fe3O4-NPs | AuSi-NPs | ZnSO4 | Control | |
Vegetation season 2019 | ||||||
Neostar | + | - | + | + | - | + |
Vegetation season 2020 | ||||||
Neostar | + | + | - | - | + | + |
Vegetation season 2021 | ||||||
Edison | + | + | - | + | - | + |
Month | Normal Precipitation 1991–2020 [mm] | Vegetation Season | |||||
---|---|---|---|---|---|---|---|
2019 | 2020 | 2021 | |||||
Precipitation [mm] | Month Characteristic According to Normal 1991–2020 | Precipitation [mm] | Month Characteristic According to Normal 1991–2020 | Precipitation [mm] | Month Characteristic According to Normal 1991–2020 | ||
April | 36 | 21.4 | dry | 7 | extraordinary dry | 36.4 | normal |
May | 59 | 134.8 | extraordinary wet | 54 | normal | 113.2 | very wet |
June | 59 | 29 | very dry | 67 | normal | 17.9 | very dry |
July | 65 | 21 | very dry | 38 | dry | 42.3 | dry |
August | 55 | 83.7 | very wet | 74 | wet | 128.2 | extraordinary wet |
September | 58 | 60.3 | normal | 96 | very wet | 36.1 | dry |
Month | Normal Temperature 1991–2020 [°C] | Vegetation Season | |||||
---|---|---|---|---|---|---|---|
2019 | 2020 | 2021 | |||||
Temperature [°C] | Month Characteristic According to Normal 1991–2020 | Temperature [°C] | Month Characteristic According to Normal 1991–2020 | Temperature [°C] | Month Characteristic According to Normal 1991–2020 | ||
April | 11.4 | 9.4 | cold | 7.8 | extraordinary cold | 7.4 | extraordinary cold |
May | 16 | 9.3 | extraordinary cold | 10.1 | extraordinary cold | 11.9 | extraordinary cold |
June | 19.6 | 18.7 | normal | 18.1 | cold | 21 | warm |
July | 21.7 | 21.9 | normal | 19.5 | very cold | 22.4 | normal |
August | 21.1 | 22.3 | warm | 20.9 | normal | 18.4 | very cold |
September | 15.9 | 16.2 | normal | 15.4 | normal | 15.4 | normal |
Feature Variant | P | E | P/E | ||||||
---|---|---|---|---|---|---|---|---|---|
Min–Max | Mean | CV (%) | Min–Max | Mean | CV (%) | Min–Max | Mean | CV (%) | |
Control | 29.33–37.55 | 33.60 ± 1.77 a | 5.28 | 25.56–32.90 | 29.19 ± 1.49 a | 5.12 | 0.98–1.32 | 1.15 ± 0.07 | 6.12 |
AuSi-NPs | 29.54–38.29 | 34.09 ± 1.70 a | 4.97 | 26.11–33.26 | 30.21 ± 1.29 b | 4.28 | 0.93–1.25 | 1.13 ± 0.06 | 5.79 |
Fe3O4-NPs | 31.51–37.65 | 33.87 ± 1.40 a | 4.12 | 25.40–32.40 | 29.68 ± 1.22 ab | 4.10 | 1.01–1.28 | 1.14 ± 0.05 | 5.03 |
ZnO-NPs | 28.06–37.30 | 33.87 ± 1.60 a | 4.71 | 26.64–33.78 | 29.75 ± 1.44 ab | 4.82 | 1.01–1.39 | 1.14 ± 0.07 | 6.77 |
Feature Variant | APG a | APG b | S | ||||||
Min–Max | Mean | CV (%) | Min–Max | Mean | CV (%) | Min–Max | Mean | CV (%) | |
Control | 658.61–954.04 | 789.888 ± 66.58 a | 8.43 | 1019.87–1498.85 | 1268.26 ± 110.55 a | 8.72 | 3.51–6.95 | 4.94 ± 0.51 b | 10.39 |
AuSi-NPs | 548.66–1017.48 | 788.39 ± 77.69 a | 9.85 | 962.43–1557.04 | 1243.40 ± 122.67 a | 9.87 | 3.67–6.84 | 4.97 ± 0.50 b | 9.99 |
Fe3O4-NPs | 577.36–949.74 | 775.40 ± 66.86 a | 8.62 | 936.04–1475.79 | 1228.80 ± 97.03 a | 7.90 | 3.47–6.92 | 4.80 ± 0.47 a | 9.82 |
ZnO-NPs | 525.83–987.31 | 766.66 ± 67.56 a | 8.81 | 921.09–1465.86 | 1237.91 ± 87.96 a | 7.11 | 3.00–6.48 | 4.83 ± 0.50 a | 10.37 |
Feature Variant | P | E | P/E | ||||||
---|---|---|---|---|---|---|---|---|---|
Min–Max | Mean | CV (%) | Min–Max | Mean | CV (%) | Min–Max | Mean | CV (%) | |
Control | 29.29–38.53 | 33.51 ± 1.79 ab | 5.35 | 25.03–33.69 | 29.25 ± 1.81 a | 6.19 | 1.02–1.39 | 1.15 ± 0.07 | 2.47 |
TiO2 | 25.92–39.21 | 33.13 ± 2.18 a | 6.59 | 23.43–32.33 | 27.94 ± 1.72 b | 6.17 | 1.03–1.38 | 1.19 ± 0.08 | 6.43 |
ZnO | 29.66–38.37 | 33.89 ± 1.91 bc | 5.63 | 25.62–32.55 | 29.40 ± 1.57 a | 5.35 | 1.00–1.40 | 1.15 ± 0.08 | 6.71 |
ZnSO4 | 28.99–38.74 | 34.39 ± 2.05 c | 5.96 | 24.03–36.34 | 30.16 ± 1.87 c | 6.19 | 0.97–1.42 | 1.14 ± 0.08 | 7.23 |
Feature Variant | APG a | APG b | S | ||||||
Min–Max | Mean | CV (%) | Min–Max | Mean | CV (%) | Min–Max | Mean | CV (%) | |
Control | 609.83–989.52 | 790.11 ± 77.88 a | 9.86 | 977.01–1598.89 | 1295.61 ± 132.89 a | 10.26 | 2.60–6.64 | 4.58 ± 0.35 b | 7.66 |
TiO2 | 532.41–1013.12 | 741.75 ± 78.77 c | 10.62 | 578.08–1667.10 | 1303.09 ± 138.12 a | 10.60 | 2.97–7.09 | 4.79 ± 0.58 d | 12.19 |
ZnO | 529.70–1022.93 | 804.21 ± 87.11 ab | 10.83 | 750.02–1652.17 | 1288.66 ± 152.25 a | 11.81 | 3.01–7.12 | 4.66 ± 0.60 c | 12.98 |
ZnSO4 | 577.82–1022.27 | 829.82 ± 81.35 b | 9.80 | 653.97–1606.79 | 1312.15 ± 163.39 a | 12.45 | 2.63–6.21 | 4.49 ± 0.57 a | 12.62 |
Feature Variant | P | E | P/E | ||||||
---|---|---|---|---|---|---|---|---|---|
Min–Max | Mean | CV (%) | Min–Max | Mean | CV (%) | Min–Max | Mean | CV (%) | |
Control | 30.76–41.70 | 36.91 ± 2.09 a | 5.65 | 26.22–33.59 | 29.26 ± 1.26 b | 4.31 | 1.08–1.42 | 1.26 ± 0.07 | 5.71 |
AuSi2 | 31.57–41.72 | 36.65 ± 1.95 a | 5.31 | 25.32–34.11 | 29.07 ± 1.40 ab | 4.80 | 1.1–1.54 | 1.26 ± 0.08 | 6.17 |
TiO2 | 29.58–40.51 | 35.55 ± 2.15 b | 6.04 | 25.14–33.24 | 28.80 ± 1.44 a | 5.02 | 1.05–1.42 | 1.24 ± 0.08 | 6.17 |
ZnO | 30.40–41.74 | 36.29 ± 2.08 a | 5.71 | 24.99–32.91 | 28.97 ± 1.40 ab | 4.84 | 1.04–1.44 | 1.25 ± 0.08 | 6.17 |
Feature Variant | APG a | APG b | S | ||||||
Min–Max | Mean | CV (%) | Min–Max | Mean | CV (%) | Min–Max | Mean | CV (%) | |
Control | 616.00–1059.08 | 838.86 ± 83.09 a | 9.90 | 1120.69–1724.06 | 1408.19 ± 114.30 a | 8.12 | 3.05–6.99 | 4.93 ± 0.56 a | 11.33 |
AuSi2 | 639.57–1036.16 | 822.15 ± 72.08 a | 8.77 | 945.75–1821.89 | 1385.46 ± 71.92 a | 5.19 | 3.27–7.34 | 4.84 ± 0.52 b | 10.23 |
TiO2 | 633.69–1035.38 | 822.33 ± 72.69 a | 8.84 | 963.99–1821.53 | 1397.37 ± 129.81 a | 9.29 | 3.31–7.21 | 5.06 ± 0.57 c | 11.20 |
ZnO | 611.66–1006.45 | 821.74 ± 80.33 a | 9.76 | 523.64–1797.06 | 1379.61 ± 147.59 a | 10.70 | 3.23–7.26 | 4.92 ± 0.56 a | 11.43 |
Variant/Year | Pollen Shape Class | Prolate | ||
---|---|---|---|---|
Oblate Spheroidal | Prolate Spheroidal | Subprolate | ||
2019 | ||||
Control | 3.39 | 33.9 | 62.71 | |
AuSi-NPs | 3.45 | 60.34 | 36.21 | |
Fe3O4-NPs | 53.23 | 46.77 | ||
ZnO-NPs | 58.73 | 39.68 | 1.59 | |
2020 | ||||
Control | 50.69 | 48.63 | 0.68 | |
TiO2-NPs | 31.46 | 65.73 | 2.81 | |
ZnO-NPs | 49.59 | 47.93 | 2.48 | |
ZnSO4 | 2.73 | 50 | 44.54 | 2.73 |
2021 | ||||
Control | 4.28 | 77.01 | 18.72 | |
AuSi-NPs | 5.52 | 77.9 | 16.58 | |
TiO2-NPs | 12.56 | 75.82 | 11.62 | |
ZnO-NPs | 6.7 | 79.9 | 13.4 |
AuSi-NPs | ZnO-NPs | Fe3O4-NPs | TiO2-NPs | ZnSO4 | Control | |
---|---|---|---|---|---|---|
Vegetation season 2019 | ||||||
Grain yield (t ha−1) | 2.75 ± 0.11 ns | 3.29 ± 0.18 ** | 2.71 ± 0.13 ns | - | - | 2.47 ± 0.17 |
Vegetation season 2020 | ||||||
Grain yield (t ha−1) | - | 2.78 ± 0.10 ns | - | 3.43 ± 0.14 ** | 3.09 ± 0.18 ** | 2.60 ± 0.17 |
Vegetation season 2021 | ||||||
Grain yield (t ha−1) | 2.83 ± 0.10 ** | 2.77 ± 0.16 * | - | 3.10 ± 0.05 ** | - | 2.50 ± 0.11 |
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Ďurišová, Ľ.; Ďúranová, H.; Kšiňan, S.; Ernst, D.; Šebesta, M.; Žitniak Čurná, V.; Eliáš, P.; Qian, Y.; Straka, V.; Feng, H.; et al. Exploring the Impact of Metal-Based Nanofertilizers: A Case Study on Sunflower Pollen Morphology and Yield in Field Conditions. Agronomy 2023, 13, 2922. https://doi.org/10.3390/agronomy13122922
Ďurišová Ľ, Ďúranová H, Kšiňan S, Ernst D, Šebesta M, Žitniak Čurná V, Eliáš P, Qian Y, Straka V, Feng H, et al. Exploring the Impact of Metal-Based Nanofertilizers: A Case Study on Sunflower Pollen Morphology and Yield in Field Conditions. Agronomy. 2023; 13(12):2922. https://doi.org/10.3390/agronomy13122922
Chicago/Turabian StyleĎurišová, Ľuba, Hana Ďúranová, Samuel Kšiňan, Dávid Ernst, Martin Šebesta, Veronika Žitniak Čurná, Pavol Eliáš, Yu Qian, Viktor Straka, Huan Feng, and et al. 2023. "Exploring the Impact of Metal-Based Nanofertilizers: A Case Study on Sunflower Pollen Morphology and Yield in Field Conditions" Agronomy 13, no. 12: 2922. https://doi.org/10.3390/agronomy13122922
APA StyleĎurišová, Ľ., Ďúranová, H., Kšiňan, S., Ernst, D., Šebesta, M., Žitniak Čurná, V., Eliáš, P., Qian, Y., Straka, V., Feng, H., Tomovičová, L., Kotlárová, N., Kratošová, G., & Kolenčík, M. (2023). Exploring the Impact of Metal-Based Nanofertilizers: A Case Study on Sunflower Pollen Morphology and Yield in Field Conditions. Agronomy, 13(12), 2922. https://doi.org/10.3390/agronomy13122922