Next Article in Journal
Metal Content of the Yellowtail Fish (Caesio cuning) Consumed by the Community in Boedingi, North Konawe, Indonesia: An Environmental Health Risk Assessment in the Mining Area
Previous Article in Journal
Decadal Morphological Evolution and Governance Measures of the South Branch, Changjiang Estuary
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 16
Issue 23
10.3390/su162310677
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Silicon as a Predicator of Sustainable Nutrient Management in Maize Cultivation (Zea mays L.)

by
Przemysław Kardasz
1,
Piotr Szulc
2,*,
Krzysztof Górecki
3,
Katarzyna Ambroży-Deręgowska
4 and
Roman Wąsala
3
1
Field Experimental Station in Winna Gora, Institute of Plant Protection—National Research Institute, 63-013 Poznań, Poland
2
Department of Agronomy, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland
3
Department of Entomology and Environmental Protection, Poznań University of Life Sciences, Dąbrowskiego 159, 60-594 Poznań, Poland
4
Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(23), 10677; https://doi.org/10.3390/su162310677
Submission received: 14 October 2024 / Revised: 2 December 2024 / Accepted: 3 December 2024 / Published: 5 December 2024
(This article belongs to the Section Sustainable Agriculture)
Versions Notes

Abstract

Field trials were conducted at the Field Experimental Station in Winna Góra. Weed control after maize sowing increased the grain yield by 15.7% compared to that after herbicide application at the BBCH 14/15 stage. Higher effectiveness of silicon application in maize cultivation can be achieved on plantations free from primary or secondary weed infestation. The application of a 50% NPK dose increased the grain yield by 8.6%, while a 100% dose improved it by 13.9% compared to that of the control object (without mineral fertilization). Furthermore, it was observed that the effectiveness of the silicon increased with higher total precipitation during the maize growing season, as evidenced by the results from 2022. In that year, the difference between the control (without silicon application) and the treatment with silicon applied at the BBCH 15/16 stage was more than 33%. The average mass losses from the green tea bags ranged from 54.9% to 71.9% in the variant of the sowing experiment carried out after spraying with the herbicide and from 69.4% to 72.4% in the variant with herbicide spraying at the BBCH14 stage. The rooibos tea’s mass losses were lower, as expected, and ranged from 18.6% to 36.4% in the first variant and from 30.8% to 38.6% in the second variant. The mass losses of the green tea and rooibos tea were the highest in the variant with herbicide spraying at the BBCH14 stage and the lowest in the variant of the sowing experiment carried out after herbicide spraying. The stabilization factor (S) ranged from 193 × 10−3 to 254 × 10−3 in sowing after herbicide spraying and from 188 × 10−3 to 226 × 10−3 in the variant with herbicide spraying at the BBCH14 stage. The k (decomposition constant) ranged from 7.8 × 10−3 to 11.5 × 10−3 in the first variant and from 7.2 × 10−3 to 13.4 × 10−3 in the variant with herbicide spraying at BBCH14.

1. Introduction

Maize (Zea mays L.) is one of the most important cereal crops in the world [1]. In many countries, it serves as a staple food source [2]. In both developing and developed countries, maize cultivation is an important source of income, while the crop itself plays crucial roles as food, feed, and industrial materials [1,3]. The increasing demand for food production is linked to the growing global population. This phenomenon contributes to the intensification of agricultural practices and increased use of synthetic fertilizers and pesticides, leading to a decline in biodiversity and increased soil erosion and degradation processes [4]. Intensive agriculture can, therefore, contribute to adverse changes in the natural environment and accelerate climate change [5]. In the context of climate change, Poland has recorded 1.5 °C higher average temperatures in recent years compared to the multiyear average from 1981 to 2010. Rising temperatures will negatively impact plants’ water management. The growing water deficit will lead to restrictions in crop production because of both reduced precipitation and its unfavorable distribution, including alternating periods of drought and heavy rainfall. Both situations will result in yield losses [6]. The phenomenon of pest migration is another serious problem in crop cultivation. Both rapidly spreading invasive and native species can cause significant yield losses. Frequently, the only solution for farmers is to implement chemical pest control, which, in turn, leads to a decline in biodiversity. In search of solutions to global climate change issues, the European Union has introduced the concept of the Green Deal. It involves implementing green economics in response to environmental threats [5]. In agriculture, this addresses issues such as the protection of aquatic ecosystems, halting biodiversity loss, and enhancing environmental conservation. In practice, this means that farmers are required to adhere to many obligations, mainly a 50% reduction in the use of plant protection products and a 20% reduction in mineral fertilizers by 2030 [7]. Active substances with a negative environmental impact are also being phased out. In light of the increasingly stringent plant protection regulations, it is essential to implement measures that will allow for obtaining high yields while minimizing the use of fertilizers and pesticides. New products continue to emerge on the market to support proper plant development, serving as partial alternatives to the substances being withdrawn. Increased emphasis is also placed on beneficial elements that increase the production of plant enzymes. Optimal silicon fertilization is one of the methods for limiting the susceptibility of plants to pathogen pressure [8,9,10]. Silicon (Si) is the second most abundant element in Earth’s crust, after oxygen. From the perspectives of plant fertilization and nutrition, it is not classified as a part of any group of nutrients essential for plants [11]. Some plants, mainly monocotyledons, contain significant amounts of this element, comparable to the levels of calcium, magnesium, and phosphorus [12,13]. Silicon has a beneficial effect on plant health by inducing physical, biochemical, and physiological changes [14,15]. Improvements of cellular structures and plant tissues result in higher plant resistance to both biotic stresses, caused, among others, by pathogen pressure, and abiotic stresses, such as UV-B radiation, water deficit, or salinity [13]. Silicon is present in soil solutions as orthosilicic acid and, in this form, can be absorbed by plants. As a polymer of hydrated SiO2, it accumulates on the epidermis of various tissues. Condensation of this acid leads to the formation of branched, and even three-dimensional, structures. In some cases, plants transport silicon directly to infection sites. Experiments have shown that under strong infection pressure, foliar silicon application delayed plant infection by several days compared to the control [12,13]. Our research brings a new aspect to the broadly understood agrotechnics of maize. The main goals of our research were to determine the effect of the herbicide application date on maize cultivation and to check whether the application of silicon can alleviate the effects of biotic and abiotic stresses in the cultivation of this species. For maize, the dynamics of the initial growth are very important because in the juvenile phase of this plant, the generative yield (grain) is already formed. Hence, the later presence of weeds in the juvenile phase can be a stress factor and can negatively affect the nutrition of plants in the juvenile phase. This is new in agrotechnical works, where the herbicide application date was combined with the silicon application date for a differentiated level of fertilization. Rising fertilizer prices and restrictions resulting from the Green Deal strategy compel farmers to manage applied fertilizers prudently and economically. An important aspect is maintaining proper cultivation practices, primarily by avoiding unnecessary vegetation that competes for nutrients and water. Because of the wide-row cultivation of maize and its slow initial growth, it is a plant that poorly competes with weeds; therefore, it is necessary to properly carry out herbicide treatments [16]. Restrictions on the use of mineral fertilizers and plant protection products force farmers to seek out stimulants that support plant growth, enabling cultivation in an economically feasible and environmentally friendly manner [17,18]. Therefore, our research is an answer to the questions asked by agricultural advisors.

2. Materials and Methods

2.1. Experimental Field Characteristics

2.1.1. Experiment I

Field trials were conducted in two growing seasons (2021 and 2022) at the Field Experimental Station in Winna Góra. This area is located on the boundary between the central and southeastern climatic zones for maize cultivation in Poland. The experiment was conducted in a split–split-plot design and included 3 factors with 4 replications. The first-order factor was the timing of herbicide application: A1—before maize sowing; A2—at the 4/5-leaf stage (BBCH 14/15). The second-order factor was the doses of NPK fertilizers: B1—control (0 kg of NPK.ha−1); B2—50% of the NPK dose per hectare (150 kg of Polifoska 6 + 150 kg of Urea 46.ha−1); B3—100% of the NPK dose per hectare (300 kg of Polifoska 6 + 300 kg of Urea 46.ha−1). The third-order factor was the date and methods of silicon application: C1—control (without silicon application); C2—seed treatment before sowing; C3—spraying of the soil before sowing; C4—spraying of plants at the 5/6-leaf stage (BBCH 15/16). The experiment utilized certified seed material of the cultivar Farmpilot, developed by Farmsaat. Farmpilot is a maize cultivar intended for grain or CCM production. It is classified as a medium-early variety with an FAO number of 240/250. Seed treatment with the silicon preparation was carried out on the same day as sowing. The experiment was conducted using a traditional ploughing system. Mineral fertilization treatments were performed 2 and 3 weeks before maize sowing for urea and polyphosphate, respectively. Pre-sowing cultivation was performed to ensure thorough mixing of the fertilizers with the soil. The silicon preparation ZumSil (Perma-Guard Agro) was used in the experiment. It is a growth stimulant preparation containing stabilized orthosilicic acid (H4SiO4). Silicon in this form has higher activity than those of silicon in the form of silicates (potassium, sodium, and calcium), making it readily absorbed by both leaves and roots. The silicon content in the ZumSil preparation is 30% H4SiO4, which corresponds to 8.81% Si (Table 1 and Table 2).

2.1.2. Experiment II

The field trial was conducted in 2023 at the Field Experimental Station in Winna Góra. The experiment was conducted using a split-plot design and included two factors with four replicates. The first-order factor was 2 herbicide application dates: A1—before maize sowing; A2—at the 4/5-leaf stage (BBCH 14/15). The second-order factor was the nitrogen (N) dose: B1—0 kg N.ha−1; B2—40 kg N.ha−1; B3—80 kg N.ha−1; B4—120 kg N.ha−1. PK fertilization was applied at the following doses in all the experimental plots: 80 kg P2O5.ha−1 and 120 kg K2O.ha−1.

2.1.3. Estimation of Decomposition Rates and Litter Stabilization Using Tea Bag Index [19]

We used the TBI method to estimate the effects of fertilization timing and agronomic treatments on litter decomposition in the soil (Keuskamp et al., 2013) [20]. The Tea Bag Index method uses two types of tea bags: green tea (Lipton green tea EAN: 8714100 77,054 2), which contains more labile and faster-decomposing organic material, and rooibos tea (Lipton rooibos tea EAN: 8722700 18,843 8), which contains more decomposition-resistant material. Following a standardized approach, the tea bags were labelled and weighed to the nearest 0.001 g. The tea bags were then buried in three replicates at a depth of approximately 8 cm in the experimental plots. Eighteen pairs of tea bags were buried in each plot, and 36 pairs were installed in both plots at one application rate. The tea bags were buried on 5 June 2023 and then 288 pairs of tea bags were removed from the soil on 6 September (93 days). The tea bags were then dried in an oven for 48 h at 70 °C. After re-weighing the contents of the tea bags, the weight loss, decomposition rate constant (k), and stabilization factor (S) were calculated. The program available on the website http://www.teatime4science.org/calculation_tbi_v2w/ (accessed on 2 December 2024) was used for the calculations.

2.2. Soil Analyses

The experiments were conducted on soils classified as class IIIb, a very good rye complex (wheat–rye). These are light soils classified as sandy clay (gp). The soil pH was slightly acidic, i.e., 5.0 in a 1 M KCl solution. The arable layer had a moderate organic matter content of 1.24%.

2.3. Thermal and Moisture Conditions During the Field Trials

The thermal and humidity conditions during the maize growing seasons are presented in Table 3. In general, it should be stated that the growing seasons, especially 2022 and 2023, were favorable for the growth and development of maize. In 2021, the total precipitation amounted to 161.30 mm, while in 2022, it was 265.50 mm, with significantly better uniformity in the individual months of maize growth and development. In turn, 2023 was characterized by a total precipitation of 223.8 mm. A significantly smaller difference in the maize growing seasons was recorded for the average daily air temperature (Table 3).

2.4. Observations and Measurements

  • Number of grain rows per ear—the total number of grain rows was recorded for each of the 6 measured ears from the plot;
  • Number of grains per row—the number of developed grains in one appropriately representative row was determined in the assessed ears;
  • Number of grains per ear—this trait was determined based on data on the number of grain rows and the number of grains per row; the estimated value was calculated from their product;
  • Thousand-grain weight (g)—to determine this trait, 2–3 samples of 500 grains were randomly collected from the threshed mass of each plot and weighed, and the average mass of 500 grains was calculated. This result was then multiplied by 2; to establish a thousand-grain weight for each combination, the results from 4 plots were summed, and the average thousand-grain weight was calculated;
  • Grain water content (%)—it was determined using a moisture meter in randomly collected samples of 250 g from each plot;
  • Grain yield (t.ha−1)—the following formula was used to determine the yield harvested per hectare:
yield (t.ha−1) = plot yield (t.plot−1) × area at 1 ha/plot

2.5. Statistical Analyses

Statistical analyses, including analysis of variance (ANOVA) and the Tukey HSD (honestly significant difference) test for pairwise comparisons of means, were performed according to the experimental design models: a split–split-plot design for Experiment I and a split-plot design for Experiment II [21]. All the calculations were carried out using the STATISTICA 13.3 software package (2017). Statistical significance was taken as a p-value of <0.05.

3. Results

3.1. Experiment I

In the present study, the thousand-grain weight (TGW) was significantly influenced by the individual years of the study (Table 4). A significantly higher value of this trait was recorded in the first year of the study (2021). The value of this characteristic was also influenced by the level of the NPK mineral fertilization. The highest value was recorded for the 50% NPK dose compared to the control group (without mineral fertilization). The timing of the silicon application did not have a significant impact on the value of this trait. The number of grains per ear was, significantly, the highest in the second year of the study (2022). The conducted research demonstrated a significant impact of the timing of the herbicide application on the number of grains per ear (Table 4). In the plots weeded before emergence, where plants had better growth conditions, the average number of grains amounted to 552.6 grains per ear. This value for the plots where the herbicide was applied at the 4–5-leaf stage was 5.1% lower, averaging 524.2 grains per ear. Considering the NPK fertilization rate, the highest value for this trait was observed for the plots with a dose of 100% NPK, while the lowest was recorded for the plots with 50% NPK. The timing of the silicon application did not have a significant impact on the value of this characteristic. The number of productive ears per unit of area was significantly shaped by the dose of the NPK fertilization (Table 4). The highest value of this trait was found for a dose of 50% NPK, while the lowest was recorded at 100% NPK. In the current field experiment, the number of grains per ear was significantly influenced by the year of the study, the timing of the herbicide application, and the rate of the NPK fertilization (Table 5). Overall, it should be noted that the highest values of the studied trait were recorded in the second year of the research (regardless of factors A and B), while in the first year with herbicide application at the maize’s post-emergence stage (A2), the values were lower compared to those for the pre-emergence herbicide application (Table 5). The number of grains per ear also depended on the date of the herbicide application in relation to the timing of the silicon fertilization (Table 6). In general, it can be concluded that the improved silicon (Si) nutrition of the maize reduced the difference in the number of grains per cob, depending on the date of the herbicide application. The highest grain yield was observed in the first year of the study, and the difference between the two years of the study was 1.99 t ha−1. The post-emergence application of the herbicide at the BBCH 14/15 stage resulted in a lower grain yield of 1.73 t ha−1 compared to that for the pre-emergence date. As the dosage of the NPK mineral fertilization increased, the grain yield showed a linear increase (Table 7). The application of silicon at the BBCH 15/16 stage proved to be the most effective, resulting in the highest grain yield compared to that for the control (without silicon). Overall, it can be concluded that the effectiveness of silicon depends on the timing of its application (Table 7). The grain moisture was significantly affected by the years of the study. Relative to the second year of the study, maize grains in 2021 were characterized by higher dryness, with a difference of 2.79% (Table 7). The grain yield and its moisture content were significantly dependent on the interaction between the study years and the timing of the herbicide application (Table 8). In the first year of the study, the highest grain yield was observed in the plots with pre-emergence herbicide application compared to those where the herbicide was applied after the maize’s emergence. In the second year of the study, the difference between the herbicide application dates was not significant. In terms of the grain moisture, the influences of the experimental factors (Y and A) were inversely related to the grain yield. The effectiveness of the silicon application in maize cultivation varied across the study years (Table 8). Overall, it can be concluded that higher atmospheric precipitation during the maize growing season correlated with the increased effectiveness of the silicon, as demonstrated in 2022. In that year, the difference between the control (without silicon application) and the treatment with silicon applied at the BBCH 15/16 stage was more than 2.59 t ha−1 (Table 8).

3.2. Experiment II

The grain yield in the present study significantly depended on the nitrogen dose (Table 9). The highest generative yield was observed in maize fertilized with a nitrogen dose of 120 kg N ha−1, compared to those for doses ranging from 0 kg N ha−1 to 80 kg N ha−1. It should also be noted that a higher grain yield, though not statistically significant, was observed for the treatment with the pre-emergence herbicide application, compared to the application at the BBCH 14/15 stage. The difference between the two herbicide application dates was 0.59 t ha−1. With respect to the thousand-grain weight (TGW), the highest value was obtained with the application of 120 kg ha−1 of nitrogen, while the lowest was observed for the control plot (without nitrogen use) (Table 9). In contrast, the highest grain moisture content at harvest was observed in maize from the control plot (without nitrogen application), while the lowest moisture was recorded for plots treated with 80 and 120 kg N ha−1. The number of grains per row was significantly dependent on the nitrogen dose (Table 10). The lowest value was observed for the plot without the nitrogen application, while the highest was recorded for a dose of 120 kg N ha−1.

3.3. Tea Bag Index [19]—Decomposition Rate (k) and Litter Stabilization Factor (S)

The average mass losses from the green tea bags ranged from 54.9% to 71.9% in the variant of the sowing experiment carried out after spraying with the herbicide (Table 11) and from 69.4% to 72.4% in the variant with herbicide spraying at the BBCH14 stage (Table 12). The rooibos tea’s mass losses were lower, as expected, and ranged from 18.6% to 36.4% in the first variant and from 30.8% to 38.6% in the second variant. The mass losses of the green tea and rooibos tea were the highest for the variant with herbicide spraying at the BBCH14 stage and the lowest for the variant of the sowing experiment carried out after herbicide spraying. As fertilization levels increased, there was an upward trend in the weight losses of the green and rooibos teas in the first variant of the experiment (Table 11). A downward trend in weight loss with increasing fertilization rate was observed in the second variant of the experiment only for the green tea (Table 12).
The stabilization factors (S) ranged from 193 × 10−3 to 254 × 10−3 for sowing after herbicide spraying (Table 11) and from 188 × 10−3 to 226 × 10−3 for the variant with herbicide spraying at the BBCH14 stage (Table 12). S was the highest in the first variant and the lowest in the second variant of the experiment. Inversely to the weight loss, the values of the S factor increase in the first variant of the experiment, while they decrease in the variant with herbicide spraying at BBCH14. As in the case of the weight loss, S values did not change significantly after the application of the bacterial preparation.
The k values ranged from 7.8 × 10−3 to 11.5 × 10−3 in the first variant and from 7.2 × 10−3 to 13.4 × 10−3 in the variant with herbicide spraying at BBCH14. In both variants, with the timing of the herbicide spraying, slightly positive trends in k values were observed in line with the increase in the fertilization rate.

4. Discussion

4.1. Experiment I and Experiment II

At present, agriculture is increasingly exposed to more frequent and intense climate changes. Plants are highly susceptible to both biotic and abiotic stress factors. In recent years, numerous studies have been conducted to determine the beneficial effects of silicon on plant development and resistance to harmful environmental factors. Our research conducted at the Field Experimental Station in Winna Góra has demonstrated that the use of silicon may have a beneficial effect on the development of maize plants. The experiment showed that silicon, applied as a seed treatment, increased the grain yield compared to that of the control. Similar results were obtained in a study by IUNG-PIB Puławy [22], where the foliar application of silicon resulted in a grain yield increase of up to 28%. Significant differences in yields after silicon application, compared to those of the control groups, were observed in plants exposed to abiotic stresses. In a study conducted by Amin [23], plant growth was evaluated under conditions of adequate irrigation and drought stress. Silicon applied to the soil at a dose of 100 milligrams of calcium silicate per kilogram of soil resulted in a 5% increase in yields for well-watered plots and a 19% increase for plots with water deficits compared to yields for untreated controls. A laboratory experiment by Sacała and Durbajło [24] demonstrated that under salinity conditions (NaCl), seedlings growing on a sodium-silicate-enriched medium had a higher root dry matter content. Sienkiewicz-Cholewa [25] reported positive effects of using silicon, in the form of sodium silicate, on spring wheat plants. Under water deficit conditions, the foliar application of silicon reduced the yield loss by 15% compared to those of control plots (without silicon), while the soil application of silicon resulted in a reduction of up to 22%.
Franco and Damian [26] studied the effectiveness of ZumSil and observed positive impacts of silicon on the height and development of maize plants. Similarly, Kowalska et al. [27] confirmed the effectiveness of the preparation in a study conducted on spring wheat. In plots where silicon was applied, plants demonstrated better development and a higher leaf greenness index, with no significant differences observed between the application methods of the preparation. In an experiment conducted by Krzymińska [28], the energy and germination capacity of winter wheat plants were evaluated after treatment with ZumSil. Compared to the control seeds, the treated seeds had a lower germination energy. Despite the initial slower growth, after 8 weeks, plants from treated seeds were significantly taller than the control plants. Similar correlations were observed in the present study, where plots treated with silicon as a seed treatment showed a tendency for reduced seedling emergence. Despite the demonstrated tendency for increased susceptibility to smut and a lower leaf greenness index, these plots ultimately produced significantly higher grain yields compared to those of the control plots.
Various sources recognize silicon as a crucial element limiting the severity of plant infections caused by pathogens [29]. The absorbed element accumulates in the plant under the epidermis, forming an insoluble silica layer that acts as a mechanical barrier to pathogens [30]. In a study by Krzymińska [28], ZumSil treatment protected spring wheat plants against stem base diseases to a degree comparable to that of a chemical treatment. Ciecierski et al. [31] studied the impact of Optysil on maize plants and found that the mycotoxin content in the grain was significantly reduced after three applications. Zamojska et al. [12] confirmed the effectiveness of the same preparation in the cultivations of grain maize, winter wheat, and winter oilseed rape. Plants that received foliar applications of silicon were characterized by reduced pathogen infection, and this reduction in biotic stress significantly increased crop yields. In experiments with sugar beets, Artyszak [13] found that during a high incidence of Cercospora beticola, infections in plots treated with silicon occurred several days later. The foliar application of silicon additionally allows for the obtainment of higher root yields and biological sugar yields [32].
Given the high nutrient requirements of maize, determining the optimal fertilizer dosage is a key consideration [33,34]. In the conducted experiment, the highest grain yield was obtained with the application of a 100% fertilizer dose (300 kg/ha of Polifoska and 6 and 300 kg/ha of Urea 46%), supplying 156 kg of nitrogen, 60 kg of phosphorus, and 90 kg of potassium per hectare to the soil. The production potential of maize is closely associated with its high nutritional requirements; thus, reducing the fertilizer dose limits the plant’s ability to achieve its full yielding potential [35]. In a study conducted by Grzelak and Żarski [36], an 11% increase in the maize grain yield was observed when comparing plants fertilized with 75 kg N.ha−1 vs. 150 kg N.ha−1. Based on the results of the present study conducted in Winna Góra, it can be concluded that under well-executed agronomic practices, the difference in the yield between the applications of 50% and 100% of the recommended fertilizer dose averaged 3.2%. Silicon is a factor contributing to a reduction in grain yield differences between fertilizer doses. When nutrient availability is limited (50% of the fertilizer dose), the application of silicon enables the achievement of up to a 39% higher grain yield compared to that of the control (without Si addition). In plots where 100% of the fertilizer dose was applied, silicon did not exert a positive effect on the grain yield.
Nitrogen, which influences the proper growth habit of the plant, is the most crucial nutrient determining yields [4]. Its deficiency leads, among others, to reduced chlorophyll content in the leaves, resulting in less efficient photosynthesis, reduced plant health, as well as lower numbers of inflorescences and fertile flowers [37]. Similar observations were reported by Lepiarczyk [38], who described the positive impacts of nitrogen fertilization on the kernel number and thousand-grain weight. The experiment showed similar results for nitrogen fertilization at rates of 120 and 160 kg.ha−1, with significant differences compared to the control plots.
The factor that most significantly influenced the results of this study was the timing of the herbicide application [39]. The effectiveness of herbicide treatments is determined by environmental conditions, the state and type of weed infestation, and the developmental stage of the weeds. These factors affect herbicide uptake, movement, and distribution within plant tissues, which, in turn, determine the herbicidal effect [40]. In the present experiment, maize herbicide treatments were applied at two different dates: on the day of the sowing and 3.5 weeks later. Because of harsh weather conditions and drought occurring from the last third of May, the herbicide treatment applied at the later date was ineffective. The weeds present in the plots competed for water, light, and nutrients with the maize plants, which was reflected in a more than 25% reduction in the grain yield compared to those of properly weeded plots. Similar results were obtained by Gołębiowska [41] in her study on the effectiveness of herbicides on weeds in maize, depending on the applied dose and timing. In plots where the effectiveness of the herbicide treatments was determined to be 92%, the grain yields were 250–280% higher compared to those of the control plots.
Proper development of maize plants in this study allowed for the achievement of a number of desirable characteristics in the properly weeded plots. The plants produced longer ears with a higher number of rows, resulting in 9% more kernels per ear compared to those in plants from plots with a high weed pressure. A beneficial effect was also observed on the thousand-grain weight, with kernels from weeded plots being, on average, 5.2% heavier. In a study by Gołębiowska [41], the difference in the thousand-grain weight was up to 30%. Sekutowski and Rola [42], in an experiment on the effectiveness of herbicides in monoculture maize cultivation, determined that grain yields in the control plots were 58% lower compared to those in weeded plots in traditional cultivation and 98% lower in simplified cultivation.
Based on the conducted research and the cited studies, it can be confirmed that implementing specific agronomic practices is essential for achieving economic efficiency in crop cultivation. The present study demonstrated the benefits of the proper fertilization of maize crops, effective herbicide applications, and the use of silicon as a less-known method for protecting plants against stress. Considering the broad and beneficial impacts on crop development and yield of this element, it should be widely utilized as a supporting agent in accordance with integrated crop protection practices [43].

4.2. Herbicide and Mineral Nitrogen Application vs. Litter Decomposition

In our study, we recorded greater weight losses of both tea types tested when the herbicide spray was carried out at the BBCH14 growth stage, i.e., shortly before the application of the tea bags. The higher concentration of the herbicide’s active ingredient in the soil probably stimulated litter decomposition more by increasing the feeding activity of the soil’s bacteria. An increase in the feeding activity in the soil after the herbicide application and, thus, increased decomposition of the soil organic matter have been reported in several studies [44]. It was observed that the applications of paraquat, glyphosate, and a mixture of glyphosate and terbuthylazine on different soil surfaces increased the feeding activity of the soil’s microorganisms [45]. However, other studies have noted that herbicides have an effect on the stabilization factor of the litter in soils. Glyphosate caused an increase in this coefficient by creating compounds that were more resistant to decomposition [46]. In our study, on average, higher S-factor values were recorded in the experiment where the herbicide was applied immediately after sowing. This may suggest that the effects of herbicides on the formation of more resistant compounds are noticeable after some time.
We also noticed that in plots where the herbicide was applied at the BBCH14 growth stage, the rate of litter decomposition decreased with increasing N fertilization. This was particularly evident in the case of the green tea. This is consistent with several publications in which it has been found that N fertilization slows down the soil’s microbial respiration by inhibiting oxidative enzymes involved in the decomposition of organic compounds [47,48,49,50]. The opposite trend was noted in plots where herbicide was applied after maize sowing. As the N dose increased, the decomposition rate of the litter increased. It is probable that the early herbicide application prevented other plants from developing by changing the C:N ratio in the soil. The C:N ratio in the soil’s microbial biomass varies between 60:7 and 42:6 [51,52]. When feeding on a substrate with a stoichiometry unfavorable to their biomass, the soil’s microorganisms must expend more energy to acquire the missing elements. Manzoni et al. [53] estimated that during the decomposition of litter with a low C:N ratio, the soil’s microbial carbon use efficiency (CUE) will be twice as high as that when decomposing litter with a stoichiometry similar to that of the microbial biomass. CUE is defined as the ratio of the organic C allocated to growth over the organic C taken up. This suggests that herbicide application before crop emergence reduces the amount of soil organic matter and, thus, increases CO2 emissions to the atmosphere.

5. Conclusions

Silicon application positively influenced maize development by enhancing resistance to both biotic and abiotic stresses, thereby helping to mitigate the negative effects of environmental conditions. The use of silicon preparations in maize cultivation resulted in higher grain yields, which translated into higher economic profitability of the crop. The application of a 50% NPK dose increased the grain yield by 8.6%, while a 100% dose increased the grain yield by 13.9% compared to that of the control object (without mineral fertilization). The level of mineral fertilization significantly differentiated (positively) the individual yield components of the maize grains. Proper herbicide application reduced the presence of competitive plants, creating optimal conditions for maize growth and development. Weed control after maize sowing increased the grain yield by 15.7% compared to that for herbicide application at the BBCH 14/15 stage. Higher effectiveness of silicon application in maize cultivation can be achieved on plantations free from primary or secondary weed infestation. Weed control, in maize cultivation, after sowing is a predictor of proper nutrient uptake at the juvenile stage of the maize and the simultaneous development of grain yield structural components. The average mass losses from the green tea bags ranged from 54.9% to 71.9% in the variant of the sowing experiment carried out after spraying with the herbicide and from 69.4% to 72.4% in the variant with herbicide spraying at the BBCH14 stage. The rooibos tea’s mass losses were lower, as expected, and ranged from 18.6% to 36.4% in the first variant and from 30.8% to 38.6% in the second variant. The mass losses of the green tea and rooibos tea were the highest for the variant with herbicide spraying at the BBCH14 stage and the lowest for the variant of the sowing experiment carried out after herbicide spraying. The stabilization factors (S) ranged from 193 × 10−3 to 254 × 10−3 for sowing after herbicide spraying and from 188 × 10−3 to 226 × 10−3 for the variant with herbicide spraying at the BBCH14 stage. The k (decomposition constant) values ranged from 7.8 × 10−3 to 11.5 × 10−3 in the first variant and from 7.2 × 10−3 to 13.4 × 10−3 in the variant with herbicide spraying at BBCH14. In both variants, with the timing of the herbicide spraying, slightly positive trends in k values were observed in line with the increase in the fertilization rate.

Author Contributions

Conceptualization, P.S., P.K., K.G. and R.W.; methodology, P.S. and K.G.; software, K.A.-D. and P.K.; validation, P.S., K.G. and R.W.; formal analysis, K.A.-D.; investigation, P.S. and R.W.; resources, K.G. and R.W.; data curation, P.K., P.S. and K.G.; writing—original draft preparation, P.S., P.K. and K.G.; writing—review and editing, P.K., P.S. and K.G.; visualization, K.A.-D. and R.W.; supervision, P.K. and R.W.; project administration, P.S. and K.G.; funding acquisition, P.K., P.S., K.G., K.A.-D. and R.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Poznan University of Life Sciences, Department of Agronomy.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Farooq, A.; Farooq, N.; Akbar, H.; Hassan, Z.U.; Gheewala, S.H. A Critical Review of Climate Change Impact at a Global Scale on Cereal Crop Production. Agronomy 2023, 13, 162. [Google Scholar] [CrossRef]
  2. Albahri, G.; Alyamani, A.A.; Badran, A.; Hijazi, A.; Nasser, M.; Maresca, M.; Baydoun, E. Enhancing Essential Grains Yield for Sustainable Food Security and Bio-Safe Agriculture through Latest Innovative Approaches. Agronomy 2023, 13, 1709. [Google Scholar] [CrossRef]
  3. Ngoune Tandzi, L.; Mutengwa, C.S. Estimation of Maize (Zea mays L.) Yield Per Harvest Area: Appropriate Methods. Agronomy 2020, 10, 29. [Google Scholar] [CrossRef]
  4. Szulc, P.; Ambroży-Deręgowska, K.; Mejza, I.; Grześ, S.; Zielewicz, W.; Stachowiak, B.; Kardasz, P. Evaluation of Nitrogen Yield-Forming Efficiency in the Cultivation of Maize (Zea mays L.) under Different Nutrient Management Systems. Sustainability 2021, 13, 10917. [Google Scholar] [CrossRef]
  5. Musiał, K.; Szumiec, A. Istota Zielonego Ładu we Wspólnej Polityce Rolnej 2021–2027—Wyzwania dla rolnictwa w aspekcie ochrony środowiska i przyrody. Wiadomości Zootech. 2021, 3, 3–14. [Google Scholar]
  6. Żarski, J.; Kuśmierek-Tomaszewska, R.; Dudek, S. Zmienność czasowa i przestrzenna susz w regionie Pomorza i Kujaw w okresie wzmożonych potrzeb wodnych kukurydzy. Pol. Acad. Sci. 2018, No II/1, 407–419. [Google Scholar]
  7. Wrzaszcz, W.; Prandecki, K. Rolnictwo a Europejski Zielony Ład. Probl. Agric. Econ. 2020, 4, 156–179. [Google Scholar]
  8. Podleśna, A. Czynniki kształtujące pobieranie i wykorzystanie fosforu przez rośliny oraz jego straty z gleb uprawnych. Inst. Uprawy Nawożenia I Glebozn. Państwowy Inst. Badaw. W Puławach 2019, 59, 59–76. [Google Scholar]
  9. Ning, D.; Qin, A.; Liu, Z.; Duan, A.; Xiao, J.; Zhang, J.; Liu, Z.; Zhao, B.; Liu, Z. Silicon-Mediated Physiological and Agronomic Responses of Maize to Drought Stress Imposed at the Vegetative and Reproductive Stages. Agronomy 2020, 10, 1136. [Google Scholar] [CrossRef]
  10. Djalovic, I.; Prasad, P.V.V.; Akhtar, K.; Paunović, A.; Riaz, M.; Dugalic, M.; Katanski, S.; Zaheer, S. Nitrogen Fertilization and Cultivar Interactions Determine Maize Yield and Grain Mineral Composition in Calcareous Soil under Semiarid Conditions. Plants 2024, 13, 844. [Google Scholar] [CrossRef]
  11. Galindo-Gutiérrez, N.F.; Garcés-Gómez, Y.A. Effects of Silicon Application on Yield, Spectral Index, and Fall Armyworm (Spodoptera frugiperda) Infestation on Maize (Zea mays) Crop. AgriEngineering 2023, 5, 2112–2122. [Google Scholar] [CrossRef]
  12. Zamojska, J.; Danielewicz, J.; Jajor, E.; Wilk, R.; Horoszkiewicz-Janka, J.; Dworzańska, D.; Węgorek, P.; Korbas, M.; Bubniewicz, P.; Ciecierski, W.; et al. The influence of foliar application of silicon on insect damage and disease occurrence in field trials. Fresenius Environ. Bull. 2018, 27, 3300–3305. [Google Scholar]
  13. Artyszak, A. Krzem w Nawożeniu Roślin Rolniczych; APRA: Sydney, Australia, 2019; ISBN 978-83-948962-6-3. [Google Scholar]
  14. El-Naggar, M.E.; Abdelsalam, N.R.; Fouda, M.M.G.; Mackled, M.I.; Al-Jaddadi, M.A.M.; Ali, H.M.; Siddiqui, M.H.; Kandil, E.E. Soil Application of Nano Silica on Maize Yield and Its Insecticidal Activity Against Some Stored Insects After the Post-Harvest. Nanomaterials 2020, 10, 739. [Google Scholar] [CrossRef] [PubMed]
  15. Djalovic, I.; Riaz, M.; Akhtar, K.; Bekavac, G.; Paunovic, A.; Pejanovic, V.; Zaheer, S.; Prasad, P.V.V. Yield and Grain Quality of Divergent Maize Cultivars under Inorganic N Fertilizer Regimes and Zn Application Depend on Climatic Conditions in Calcareous Soil. Agronomy 2022, 12, 2705. [Google Scholar] [CrossRef]
  16. Idziak, R.; Woźnica, Z.; Sobczak, A.; Naskrent, B. Wpływ adiuwantów doglebowych i RSM na skuteczność działania mieszaniny tienkarbazonu metylu z izoksaflutolem stosowanej w kukurydzy. Fragm. Agron. 2019, 36, 35–44. [Google Scholar]
  17. Jagła, M.; Sobiech, Ł.; Szulc, P.; Nowosad, K.; Bocianowski, J.; Grzanka, M. Sensitivity Assessment of Varieties, Effectiveness of Weed Control by Selected Herbicides, and Infection of the Fusarium in Maize (Zea mays L.) Cultivation. Agronomy 2020, 10, 1115. [Google Scholar] [CrossRef]
  18. Zhang, W.; Niu, S.; Yao, J.; Zhang, Y.; Li, X.; Dong, H.; Si, S.; Li, H.; Li, X.; Ren, J.; et al. Responses of Physiological Traits and Grain Yield to Short Heat Stress during Different Grain-Filling Stages in Summer Maize. Agronomy 2023, 13, 2126. [Google Scholar] [CrossRef]
  19. Tea Bag Index. Available online: http://www.teatime4science.org/calculation_tbi_v2w/ (accessed on 9 August 2024).
  20. Keuskamp, J.A.; Dingemans, B.J.; Lehtinen, T.; Sarneel, J.M.; Hefting, M.M. Tea Bag index: A novel approach to collect uniform decomposition data across ecosystems. Methods Ecol. Evol. 2013, 4, 1070–1075. [Google Scholar] [CrossRef]
  21. Szulc, P.; Ambroży-Deręgowska, K.; Kobus-Cisowska, J.; Kardasz, P.; Nowaczyk, R. The role of maize variety (Zea mays L.) in shaping the grain vitamin content. Biom. Lett. 2022, 59, 171–177. [Google Scholar] [CrossRef]
  22. IUNG-PIB Puławy. Sprawozdanie dla Ciech Sarzyna S.A; IUNG-PIB Puławy: Puławy, Poland, 2017. [Google Scholar]
  23. Amin, M. Effect of different nitrogen sources on growth, yield and quality of fodder maize (Zea mays L.). J. Saudi Soc. Agric. Sci. 2010, 10, 17–23. [Google Scholar] [CrossRef]
  24. Sacała, E.; Durbajło, W. Oddziaływanie krzemianu sodu na kukurydzę rosnącą w warunkach stresowych. Wrocław Univ. Environ. Life Sci. 2012, 91, 949–951. [Google Scholar]
  25. Sienkiewicz-Cholewa, U. Reakcja pszenicy jarej rosnącej w stresie suszy na dolistne i doglebowe stosowanie krzemu. Prog. Plant Prot. 2021, 61, 207–213. [Google Scholar]
  26. Franco, J.; Damian, E. Evaluación de Dosis y Fuentes de Silicio Líquido Aplicado Foliarmente en el Cultivo de Maíz (Zea mays L.). Trabajo de Titulación Para Optar el Título de Ingeniero Agrónomo; Repositorio Istitucional de la Universidad de Guayaquil: Guayaquil, Ecuador, 2016; p. 48. [Google Scholar]
  27. Kowalska, J.; Jakubowska, M.; Nowaczyk, R. Wpływ stosowania krzemu na wzrost pszenicy jarej w systemie rolnictwa ekologicznego. Institute of Plant Protection—NRI. J. Res. Appl. Agric. Eng. 2018, 63. [Google Scholar]
  28. Krzymińska, J. Ocena przydatności mikroorganizmów i krzemu do zaprawiania ziarna pszenicy z przeznaczeniem dla rolnictwa ekologicznego. Prog. Plant Prot. 2021, 61, 62–67. [Google Scholar]
  29. Zimba, K.J.; Read, Q.D.; Haseeb, M.; Meagher, R.L.; Legaspi, J.C. Potential of Silicon to Improve Biological Control of Fall Armyworm, Spodoptera frugiperda on Maize. Agriculture 2022, 12, 1432. [Google Scholar] [CrossRef]
  30. Sienkiewicz-Cholewa, U.; Zajączkowska, A. Rola i plonotwórcze efekty stosowania krzemu na przykładzie światowych badań. Prog. Plant Prot. 2020, 60, 313–319. [Google Scholar]
  31. Ciecierski, W.; Korbas, M.; Horoszkiewicz-Janka, J. Effectiveness of silicon application on mycotoxins reduction in maize. In Proceedings of the 7th International Conference on Silicon in Agriculture, Bengaluru, India, 24–28 October 2017; p. 96. [Google Scholar]
  32. Artyszak, A. Możliwości Wykorzystania Krzemu do Dokarmiania Dolistnego Buraka Cukrowego; Wieś Jutra Sp. z o.o.: Warszawa, Poland, 2017; ISBN 978-83-62815-39-5. [Google Scholar]
  33. Gaj, R.; Szulc, P.; Siatkowski, I.; Waligóra, H. Assessment of the Effect of the Mineral Fertilization System on the Nutritional Status of Maize Plants and Grain Yield Prediction. Agriculture 2020, 10, 404. [Google Scholar] [CrossRef]
  34. Szulc, P.; Krauklis, D.; Ambroży-Deręgowska, K.; Wróbel, B.; Zielewicz, W.; Niedbała, G.; Kardasz, P.; Niazian, M. Evaluation of the Effect of Conventional and Stabilized Nitrogen Fertilizers on the Nutritional Status of Several Maize Cultivars (Zea mays L.) in Critical Growth Stages Using Plant Analysis. Agronomy 2023, 13, 480. [Google Scholar] [CrossRef]
  35. Księżak, J.; Bojarszczuk, J.; Staniak, M. Produkcyjność kukurydzy i sorga w zależności od poziomu nawożenia azotem. Zakład Uprawy Roślin Pastewnych, Instytut Uprawy Nawożenia i Gleboznawstwa—Państwowy Instytut Badawczy w Puławach. Pol. J. Agron. 2012, 8, 20–28. [Google Scholar]
  36. Grzelak, B.; Żarski, J. Wpływ nawadniania kroplowego i nawożenia azotem na plonowanie dwóch odmian kukurydzy na glebie bardzo lekkiej. Pol. Akad. Nauk. Oddział W Krakowie 2009, 6, 141–149. [Google Scholar]
  37. Błaszczyk, K. Wymagania Siedliskowe i Pokarmowe Pszenicy Ozimej; Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy: Bydgoszcz, Poland, 2019; DSS UPWR; ISBN 978-83-948516-2-0. [Google Scholar]
  38. Lepiarczyk, A.; Filipek-Mazur, B.; Tabak, M.; Joniec, A. Wpływ nawożenia azotem i siarką na plonowanie i skład chemiczny ziarna kukurydzy cz. I. Wielkość i komponenty plonu ziarba kukurydzy. Katedra Agrotechniki i Ekologii Rolniczej, Katedra Chemii Rolnej i Środowiskowej, Uniwersytet Rolniczy w Krakowie. Fragm. Agron. 2013, 30, 115–122. [Google Scholar]
  39. Idziak, R.; Waligóra, H.; Majchrzak, L.; Szulc, P. Multifunctional adjuvants affect sulfonylureas with synthetic auxin mixture in weed and maize grain yield. Plants 2024, 13, 1480. [Google Scholar] [CrossRef]
  40. Kieloch, R.; Domaradzki, K. Wpływ współdziałania wilgotności gleby i fazy rozwojowej chwastów na skuteczność herbicydów stosowanych w zróżnicowanych dawkach. Prog. Plant Prot. 2015, 55, 58–63. [Google Scholar]
  41. Gołębiowska, H. Wpływ herbicydów na plonowanie i wybrane wskaźniki architektury łanu kukurydzy w zależności od systemu uprawy roli. Zakład Herbologii i Technik Uprawy Roli, Instytut Uprawy Nawożenia i Gleboznawstwa w Puławach. Fragm. Agron. 2013, 30, 64–69. [Google Scholar]
  42. Sekutowski, T.; Rola, H. Wpływ monokultury i systemu uprawy roli oraz ochrony herbicydowej na agrofitocenozę kukurydzy. Instytut Uprawy Nawożenia i Gleboznawstwa—Państwowy Instytut Badawczy w Puławach. Fragm. Agron. 2010, 27, 128–140. [Google Scholar]
  43. Artyszak, A. Effect of Silicon Fertilization on Crop Yield Quantity and Quality—A Literature Review in Europe. Plants 2018, 7, 54. [Google Scholar] [CrossRef]
  44. Brüh, C.A.; Zaller, J.G. Indirect herbicide effects on biodiversity, ecosystem functions, and interactions with global changes. In Herbicides. Chemistry, Efficacy, Toxicology, and Environmental Impacts; Mesnage, R., Zaller, J.G., Eds.; Elsevier: Amsterdam, The Netherlands, 2021. [Google Scholar] [CrossRef]
  45. Reinecke, A.J.; Helling, B.; Louw, K.; Fourie, J.; Reinecke, S.A. The impact of different herbicides and cover crops on soil biological activity in vineyards in the Western Cape, South Africa. Pedobiologia 2002, 46, 5. [Google Scholar] [CrossRef]
  46. Gaupp-Berghausen, M.; Hofer, M.; Rewald, B.; Zaller, J.G. Glyphosate-based herbicides reduce the activity and reproduction of earthworms and lead to increased soil nutrient concentrations. Sci. Rep. 2015, 5, 12886. [Google Scholar] [CrossRef]
  47. Janssens, A.; Dieleman, W.; Luyssaert, S.; Subke, J.-A.; Reichstein, M.; Ceulemans, R.; Ciais, P.; Dolman, A.J.; Grace, J.; Matteucci, G.; et al. Reduction of forest soil respiration in response to nitrogen deposition. Nat. Geosci. 2010, 3, 315e322. [Google Scholar] [CrossRef]
  48. Rousk, J.; Brookes, P.C.; Bååth, E. Fungal and bacterial growth responses to N fertilization and pH in the 150-year ‘Park Grass’ Uk grassland experiment. FEMS Microbiol. Ecol. 2011, 76, 89–99. [Google Scholar] [CrossRef]
  49. Riggs, C.E.; Hobbie, S.E.; Bach, E.M.; Hofmockel, K.S.; Kazanski, C.E. Nitrogen additin changes grassland soil organic matter decomposition. Biogeochemistry 2015, 125, 203–219. [Google Scholar] [CrossRef]
  50. Spohn, M.; Pötsch, E.M.; Eichorst, S.A.; Woebken, D.; Wanek, W.; Richter, A. Soil microbial carbon use efficiency and biomass turnover in a long-term fertilization experiment in a temperate grassland. Soil Biol. Biochem. 2016, 97, 168–175. [Google Scholar] [CrossRef]
  51. Cleveland, C.C.; Liptzin, D. C: N: P stoichiometry in soil: Is there a “Redfield ratio” for the microbial biomass? Biogeochemistry 2007, 85, 235–252. [Google Scholar] [CrossRef]
  52. Xu, X.; Thornton, P.E.; Post, W.M. A global analysis of soil microbial biomass carbon, nitrogen and phosphorus in terrestrial ecosystems. Glob. Ecol. Biogeogr. 2013, 22, 737–749. [Google Scholar] [CrossRef]
  53. Manzoni, S.; Trofymow, J.A.; Jackson, R.B.; Porporato, A. Stoichiometric controls dynamics on carbon, nitrogen, and phosphorus in decomposing litter. Ecol. Monogr. 2010, 80, 89–106. [Google Scholar] [CrossRef]
Table 1. Cultivation practices and dates of their implementation.
Table 1. Cultivation practices and dates of their implementation.
Experiment IExperiment IIType of Cultivation Procedure
25.11.202017.11.202110.11.2022Pre-winter ploughing
16.03.202115.03.202217.03.2023Leavers + toothed harrow
18.03.202118.03.202206.04.2023Fertilization (Polifoska 6)
24.04.202119.04.202220.04.2023Fertilization (Urea 46%)
27.04.202122.04.202202.05.2023Pre-sowing cultivation
06.05.202106.05.2022-Application of silicon to the soil
06.05.202106.05.2022-Seed dressing
06.05.202106.05.202204.05.2023Maize sowing
06.05.202106.05.202204.05.2023Herbicide application
16.06.202116.06.2022-Application of silicon in phase BBCH 15/16
07.10.20214.10.202210.10.2023Maize harvest
Table 2. List of plant protection products used in Experiment I.
Table 2. List of plant protection products used in Experiment I.
DateTypeTrade NameDose (L; kg.ha−1)
06.05.2021HerbicideWing P 462.5 EC4.0 L/ha
06.05.2022
04.05.2023
01.06.2021HerbicideMocarz 75 WG + Nitya 040 OD + Atpolan Bio0.2 kg.ha−1 + 1.0 L.ha−1 + 1.0 L.ha−1
01.06.2022
Table 3. Average monthly air temperature and monthly total precipitation for the maize growing seasons.
Table 3. Average monthly air temperature and monthly total precipitation for the maize growing seasons.
Temperature (°C)—2021
VVIVIIVIIIIXXAverage
12.8420.6221.2617.6215.5010.4216.37
Precipitation (mm)—2021
VVIVIIVIIIIXXTotal
60.9016.6019.6030.409.4024.40161.30
Temperature (°C)—2022
VVIVIIVIIIIXXAverage
14.8419.8119.8221.7513.1811.9516.89
Precipitation (mm)—2022
VVIVIIVIIIIXXTotal
23.1051.2060.6029.8051.4049.70265.5
Temperature (°C)—2023
VVIVIIVIIIIXXAverage
13.3519.0620.3820.1218.6611.3317.15
Precipitation (mm)—2023
VVIVIIVIIIIXXTotal
28.4043.1038.8065.0011.9036.60223.8
Table 4. Average values of grain yield components for years (Y), herbicide application date (A), NPK mineral fertilizer dosage (B), and silicon application date (C).
Table 4. Average values of grain yield components for years (Y), herbicide application date (A), NPK mineral fertilizer dosage (B), and silicon application date (C).
FactorFactor LevelTGW (g)Number of Grains per Ear (pcs.)Number of Ears (pcs.)
Y2021349.04 a517.66 b6.85 ns
2022289.96 b559.22 a6.92 ns
AA1319.51 ns552.63 a6.81 ns
A2319.49 ns524.26 b6.96 ns
BB1314.85 b533.37 ab6.88 ab
B2322.52 a529.52 b6.95 a
B3321.12 ab552.44 a6.82 b
CC1323.45 ns537.09 ns6.87 ns
C2320.14 ns534.40 ns6.93 ns
C3316.16 ns541.57 ns6.82 ns
C4318.24 ns540.71 ns6.93 ns
Values in columns marked with at least one same letter are not significantly different; ns—non-significant differences.
Table 5. Average values of the number of grains per ear for the Y × A × B combination.
Table 5. Average values of the number of grains per ear for the Y × A × B combination.
YABNumber of Grains per Ear (pcs.)
2021A1B1535.21 abc
B2538.24 ab
B3551.32 ab
A2B1481.34 cd
B2479.92 d
B3519.94 bcd
2022A1B1540.27 ab
B2561.65 ab
B3589.07 a
A2B1576.65 a
B2538.28 ab
B3549.42 ab
Values in columns marked with at least one same letter are not significantly different.
Table 6. Average values of the number of grains per ear for the A × C combination.
Table 6. Average values of the number of grains per ear for the A × C combination.
Silicon Application Date
(C)		Herbicide Application Date (A)Number of Grains per Ear (pcs.)
C1A1546.17 abc
A2528.01 bc
C2A1551.87 ab
A2516.93 c
C3A1566.28 a
A2516.85 c
C4A1546.18 abc
A2535.24 bc
Values in columns marked with at least one same letter are not significantly different.
Table 7. Average values of yield and grain moisture content for years (Y), herbicide application date (A), NPK mineral fertilizer dosage (B), and silicon application date (C).
Table 7. Average values of yield and grain moisture content for years (Y), herbicide application date (A), NPK mineral fertilizer dosage (B), and silicon application date (C).
FactorFactor LevelGrain Yield (t.ha−1)Grain Moisture (%)
Y202111.00 a27.87 b
20229.01 b30.66 a
AA110.87 a29.34 ns
A29.14 b29.20 ns
BB19.35 b29.33 ns
B210.10 ab29.49 ns
B310.57 a28.98 ns
CC19.44 b29.55 ns
C210.18 ab29.29 ns
C39.99 ab29.15 ns
C410.41 a29.08 ns
Values in columns marked with at least one same letter are not significantly different; ns—non-significant differences.
Table 8. Average values of yield and grain moisture for Y × A and Y × C combinations.
Table 8. Average values of yield and grain moisture for Y × A and Y × C combinations.
Year (Y)Herbicide Application Date (A)Grain Yield (t.ha−1)Grain Moisture (%)
2021A112.58 a28.55 b
A29.41 b27.20 c
2022A19.17 b30.13 a
A28.86 b31.20 a
Year (Y)Silicon Application Date (C)Grain Yield
(t.ha−1)		Grain Moisture (%)
2021C110.98 a28.20 ns
C211.54 a27.78 ns
C311.14 a27.60 ns
C410.33 ab27.90 ns
2022C17.90 c30.89 ns
C28.82 c30.79 ns
C38.85 bc30.70 ns
C410.49 a30.26 ns
Values in columns marked with at least one same letter are not significantly different; ns—non-significant differences.
Table 9. Average values of grain yield, TGW, and grain moisture content for herbicide application dates (A) and nitrogen doses (B) (2023).
Table 9. Average values of grain yield, TGW, and grain moisture content for herbicide application dates (A) and nitrogen doses (B) (2023).
FactorFactor LevelGrain Yield (t.ha−1)TGW (g)Grain Moisture (%)
AA19.24 ns334.17 ns23.88 ns
A28.65 ns340.61 ns23.93 ns
BB18.24 b314.66 c24.69 a
B28.79 b 325.24 bc24.36 ab
B38.60 b347.88 ab23.18 c
B410.14 a361.80 a23.40 bc
Values in columns marked with at least one same letter are not significantly different; ns—non-significant differences.
Table 10. Average values of the numbers of grains per ear, rows per ear, and grains per row for herbicide application dates (A) and nitrogen doses (B) (2023).
Table 10. Average values of the numbers of grains per ear, rows per ear, and grains per row for herbicide application dates (A) and nitrogen doses (B) (2023).
FactorFactor LevelNumber Of Grains per EarNumber of Rows (pcs.)Number of Grains per Row (pcs.)
AA1474.05 ns17.31 ns27.34 ns
A2461.39 ns16.65 ns27.69 ns
BB1448.35 ns17.08 ns26.24 b
B2463.60 ns17.20 ns26.95 ab
B3457.57 ns16.50 ns27.71 ab
B4501.35 ns17.15 ns29.18 a
Values in columns marked with at least one same letter are not significantly different; ns—non-significant differences.
Table 11. Means and standard deviations of the mass losses from the tea bags, calculated stabilization factor (S), and decomposition constant (k) for the variant of the sowing experiment carried out after herbicide spraying.
Table 11. Means and standard deviations of the mass losses from the tea bags, calculated stabilization factor (S), and decomposition constant (k) for the variant of the sowing experiment carried out after herbicide spraying.
Fertilization Level (N kg × ha−1)0 (B1)40 (B2)80 (B3)120 (B4)
Plot12345678
Mass loss (%)
Type of tea
Green tea56.0 ± 3.154.9 ± 2.854.9 ± 3.356.1 ± 4.057.9 ± 2.670.5 ± 1.971.9 ± 1.071.9 ± 1.0
Rooibos tea22.5 ± 4.718.6 ± 3.218.9 ± 5.723.2 ± 5.023.3 ± 5.532.1 ± 3.734.1 ± 2.434.1 ± 2.4
Stabilization factor (S) (×10−3)
240 ± 41254 ± 38252 ± 43242 ± 48216 ± 35214 ± 26193 ± 14219 ± 41
Decomposition constant (k) (×10−3 day−1)
10.6 ± 3.87.8 ± 1.78.3 ± 3.910.9 ± 3.311.1 ± 5.78.6 ± 2.59.3 ± 1.811.5 ± 1.9
Table 12. Means and standard deviations of the mass losses from the tea bags, calculated stabilization factor (S), and decomposition constant (k) for the variant of the herbicide spraying experiment carried out at the BBCH14 stage.
Table 12. Means and standard deviations of the mass losses from the tea bags, calculated stabilization factor (S), and decomposition constant (k) for the variant of the herbicide spraying experiment carried out at the BBCH14 stage.
Fertilization Level (N kg × ha−1)0 (B1)40 (B2)80 (B3)120 (B4)
Plot910111213141516
Mass loss (%)
Type of tea
Green tea71.6 ± 1.972.4 ± 2.170.9 ± 1.670.3 ± 2.670.5 ± 3.970.6 ± 3.869.4 ± 1.869.4 ± 1.8
Rooibos tea36.2 ± 4.035.6 ± 5.730.8 ± 2.731.9 ± 4.635.3 ± 5.833.5 ± 4.738.6 ± 5.238.6 ± 5.2
Stabilization factor (S) (×10−3)
199 ± 0188 ± 30206 ± 21216 ± 36213 ± 52211 ± 51226 ± 24224 ± 28
Decomposition constant (k) (×10−3 day−1)
11.2 ± 3.511.7 ± 7.27.5 ± 1.48.5 ± 2.911.3 ± 6.09.6 ± 3.413.4 ± 7.810.4 ± 5.1
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kardasz, P.; Szulc, P.; Górecki, K.; Ambroży-Deręgowska, K.; Wąsala, R. Silicon as a Predicator of Sustainable Nutrient Management in Maize Cultivation (Zea mays L.). Sustainability 2024, 16, 10677. https://doi.org/10.3390/su162310677

AMA Style

Kardasz P, Szulc P, Górecki K, Ambroży-Deręgowska K, Wąsala R. Silicon as a Predicator of Sustainable Nutrient Management in Maize Cultivation (Zea mays L.). Sustainability. 2024; 16(23):10677. https://doi.org/10.3390/su162310677

Chicago/Turabian Style

Kardasz, Przemysław, Piotr Szulc, Krzysztof Górecki, Katarzyna Ambroży-Deręgowska, and Roman Wąsala. 2024. "Silicon as a Predicator of Sustainable Nutrient Management in Maize Cultivation (Zea mays L.)" Sustainability 16, no. 23: 10677. https://doi.org/10.3390/su162310677

APA Style

Kardasz, P., Szulc, P., Górecki, K., Ambroży-Deręgowska, K., & Wąsala, R. (2024). Silicon as a Predicator of Sustainable Nutrient Management in Maize Cultivation (Zea mays L.). Sustainability, 16(23), 10677. https://doi.org/10.3390/su162310677

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop