Utilizing Principal Component Analysis to Assess the Effects of Complex Foliar Fertilizers Regarding Maize (Zea mays L.) Productivity
Abstract
:1. Introduction
2. Materials and Methods
2.1. Natural Framework of Experimentation: Relief, Climate, and Soil
2.2. Sowing and Fertilization, Experimental Scheme
- Semi-late hybrid (FAO 450) with extraordinary production potential proven every year.
- It loses water very quickly at maturity.
- Very strong roots and stem.
- Intensive hybrid intended for farmers who apply cutting-edge technology.
- It is recommended for arid and semi-arid lowland areas in the south and west of the country.
- Non-irrigated: 60,000–65,000 harvestable plants/ha.
- Irrigated: 68,000–75,000 harvestable plants/ha
- number of rows per tin: 16–18
- number of seeds per row: 47–50
- MMB: 365–400 g.
2.3. Chemical Analysis and Statistical Data Processing Methods
- Humidity (%): this was performed by drying the samples at 110 °C in an electric oven, POL-EKO-equipment, Nitech-Romania, SR EN ISO 712:2010 [41].
- Ash (%): this was made by burning at 5500 °C using electric furnace equipment (Lenton Thermal Design, England), SR ISO 2171:2002 [42].
- Crude protein (%): this was classically determined by the Kjeldahl method, using a VELP kit (DK20 heating digestion and UDK 149 distillation unit) [43].
- Dietary fiber (%) extraction was carried out by the FOSS Fibertec device and method 2010&M6 [46].
- The mineralization of the samples from the maize grains was carried out by calcination followed by a wet mineralization of the ash using concentrated HNO3+HCl in the ratio 1:3 (aqua regia), followed by dilution. From the properly diluted solutions, total phosphorus content was determined colorimetrically using a CINTRA spectrophotometer (GBC Australia). Metal nutrients were determined by FAAS method (air-acetylene flame), using a fast sequential atomic absorption spectrometer VARIAN AA 240 FS (Australia) [47,48].
- The percentage content of carbohydrates was calculated by subtracting from 100 the sum of the other macronutrients.
- Groups of variables: Based on the “loadings” (weights assigned to original variables in the principal components), PCA can group related quality parameters (e.g., protein and lipids).
- Groups of samples: Based on the “scores” (values of each sample on the principal components), PCA can group grain maize samples that respond similarly to the treatments (e.g., samples with high yield and similar protein content).
- PCA-nutrient: This model explores the interactions between treatments and key maize quality aspects (yield, moisture, protein, lipids, fibers, carbohydrates).
- PCA-mineral: This model focuses on the interactions between treatments and mineral content (yield, ash, macronutrients, and micronutrients).
3. Results and Discussions
3.1. Impact of FF on Production and Humidity of Maize Grain
3.2. The Influence of Foliar Fertilizers on the Protein, Lipid, Carbohydrate, and Fiber Content of Maize Grains
In summary, foliar fertilization with various mineral combinations, particularly those containing nitrogen and micronutrients such as zinc, can significantly influence the qualitative parameters of crop production. While it tends to increase protein content, it often leads to a decrease in lipid content, especially when nitrogen predominates. Carbohydrate content may also be affected, particularly by nitrogen fertilization. These findings underscore the importance of carefully balancing nutrient inputs to optimize both yield and quality in crop production.
- ✓
- Distinct Groupings:
- ✓
- Effects of foliar fertilization:
3.3. The Effect of Foliar Fertilization on Essential Macroelement Contents in Maize Seeds
3.3.1. The Impact on Macronutrient Content of Maize Grains
3.3.2. The Influence of FF on the Content of Some Micronutrients in Maize Grains
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Measured Parameters | Grain Yield | Humidity (Relative to the Fresh Mass) | |
---|---|---|---|
Units | kg ha−1 | % (Regarding Mt) | % |
Variants | Mean/SD | Mean/SD | |
V1 | 10,147 *** 146 | 134 | 13.10 NS 0.05 |
V2 | 9997 *** 50 | 132 | 12.91 NS 0.06 |
V3 | 10,987 *** 81 | 146 | 12.97 NS 0.14 |
V4 | 10,723 *** 219 | 142 | 11.99 ** 0.17 |
V5 | 11,433 *** 416 | 151 | 11.71 *** 0.20 |
V6 | 10,777 *** 108 | 143 | 11.51 *** 0.23 |
V7 | 10,813 *** 96 | 143 | 11.71 ** 0.27 |
Mt | 7550 60 | 100 | 13.15 0.21 |
Analyzed Nutrients | Proteins | Lipids | Carbohydrates | Fibers |
---|---|---|---|---|
Variants/Unit | %, Mean/SD | |||
V1 | 9.40 *** 0.02 | 3.93 *** 0.03 | 72.36 ** 0.05 | 7.17 * 0.02 |
V2 (S) | 9.18 *** 0.03 | 3.95 ** 0.10 | 72.76 NS 0.10 | 7.10 NS 0.01 |
V3 (B) | 9.46 *** 0.11 | 4.45 NS 0.04 | 71.91 ** 0.28 | 7.10 NS 0.02 |
V4 (S+B) | 10.97 *** 0.08 | 3.72 *** 0.12 | 72.09 ** 0.17 | 7.21 * 0.04 |
V5 (Zn) | 11.62 *** 0.11 | 4.54 NS 0.10 | 70.91 ** 0.37 | 7.30 *** 0.02 |
V6 (Zn) | 11.38 *** 0.02 | 4.65 NS 0.02 | 71.21 *** 0.23 | 7.27 ** 0.02 |
V7 (B) | 10.81 *** 0.03 | 4.68 NS 0.07 | 71.58 ** 0.29 | 7.24 * 0.06 |
Mt | 8.29 0.17 | 4.57 0.11 | 72.82 0.12 | 7.09 0.04 |
Minerals | Ash | Ca | Mg | K | Na | P |
---|---|---|---|---|---|---|
Units | % | mg kg−1 | ||||
Variants | Mean/SD | |||||
V1 | 1.21 NS 0.01 | 7.10 * 0.03 | 128.07 *** 0.31 | 278.50 *** 0.10 | 34.67 * 0.15 | 211.07 NS 0.21 |
V2 | 1.21 NS 0.00 | 7.08 * 0.03 | 127.97 ** 0.21 | 278.37 *** 0.06 | 34.73 * 0.21 | 211.70 * 0.40 |
V3 | 1.22 NS 0.01 | 7.13 ** 0.02 | 127.37 * 0.12 | 278.83 *** 0.06 | 35.17 NS 0.15 | 212.53 *** 0.15 |
V4 | 1.24 * 0.01 | 7.16 ** 0.01 | 127.70 ** 0.20 | 288.07 ** 0.06 | 34.63 ** 0.06 | 213.57 *** 0.32 |
V5 | 1.22 NS 0.01 | 7.20 ** 0.02 | 128.40 *** 0.10 | 288.70 ** 0.20 | 35.20 NS 0.10 | 214.63 *** 0.21 |
V6 | 1.24 NS 0.03 | 7.18 ** 0.01 | 128.23 *** 0.12 | 288.57 ** 0.21 | 35.40 * 0.10 | 214.57 *** 0.12 |
V7 | 1.21 NS 0.02 | 7.16 ** 0.02 | 127.73 * 0.40 | 288.33 ** 0.15 | 35.23 NS 0.06 | 213.63 *** 0.25 |
Mt | 1.17 0.04 | 6.96 0.05 | 126.77 0.25 | 286.8. 0.35 | 35.10 0.09 | 210.40 0.36 |
Minerals | Fe | Zn | Cu | Mn |
---|---|---|---|---|
Unit | mg kg−1 | |||
Variants | Mean/SD | |||
V1 | 2.66 NS 0.06 | 2.34 * 0.01 | 0.33 * 0.00 | 0.48 NS 0.00 |
V2 | 2.72 NS 0.02 | 2.29 NS 0.02 | 0.33 ** 0.00 | 0.49 * 0.00 |
V3 | 2.76 NS 0.01 | 2.21 NS 0.06 | 0.32 NS 0.00 | 0.49 ** 0.00 |
V4 | 2.71 NS 0.01 | 2.19 NS 0.02 | 0.32 NS 0.00 | 0.48 NS 0.00 |
V5 | 2.66 NS 0.04 | 2.22 NS 0.02 | 0.31 NS 0.00 | 0.49 * 0.00 |
V6 | 2.70 NS 0.02 | 2.28 NS 0.01 | 0.31 NS 0.00 | 0.48 * 0.00 |
V7 | 2.70 NS 0.04 | 2.28 NS 0.03 | 0.33 ** 0.00 | 0.48 NS 0.00 |
Mt | 2.72 0.03 | 2.25 0.04 | 0.31 0.00 | 0.48 0.00 |
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Crista, L.; Radulov, I.; Crista, F.; Imbrea, F.; Manea, D.N.; Boldea, M.; Gergen, I.; Ienciu, A.A.; Lațo, A. Utilizing Principal Component Analysis to Assess the Effects of Complex Foliar Fertilizers Regarding Maize (Zea mays L.) Productivity. Agriculture 2024, 14, 1428. https://doi.org/10.3390/agriculture14081428
Crista L, Radulov I, Crista F, Imbrea F, Manea DN, Boldea M, Gergen I, Ienciu AA, Lațo A. Utilizing Principal Component Analysis to Assess the Effects of Complex Foliar Fertilizers Regarding Maize (Zea mays L.) Productivity. Agriculture. 2024; 14(8):1428. https://doi.org/10.3390/agriculture14081428
Chicago/Turabian StyleCrista, Laura, Isidora Radulov, Florin Crista, Florinel Imbrea, Dan Nicolae Manea, Marius Boldea, Iosif Gergen, Anișoara Aurelia Ienciu, and Alina Lațo. 2024. "Utilizing Principal Component Analysis to Assess the Effects of Complex Foliar Fertilizers Regarding Maize (Zea mays L.) Productivity" Agriculture 14, no. 8: 1428. https://doi.org/10.3390/agriculture14081428