3.1. Meteorological Conditions
Annual precipitation in Racot in 2021 was equal to 539.5 mm. Monthly precipitation and average air temperatures are shown in
Table 2. The month with the highest precipitation was July (76.8 mm), which was also the warmest month in the study period (average monthly temperature of 20.9 °C). Conversely, the lowest average monthly temperature occurred in January at −0.3 °C, and the lowest precipitation was recorded in March (19.9 mm).
The 2021 monthly precipitation totals measured at the Racot research area were compared with monthly precipitation totals for the years 1991–2020 from the meteorological station in Kościan, located 5 km from the Racot research plots (
Table 2). These data were obtained from the Institute of Meteorology and Water Management National Research Institute (IMGW-PIB). Analyzing the monthly precipitation totals in 2021 against the thirty-year period, it can be seen that in February, March, June, and September, the precipitation was lower than the average for Kościan. On the other hand, in July 2021, the total rainfall was exactly the same as the average value for 1991–2020. However, it should be noted that the precipitation total in 2021 was higher for many months than in earlier years. This is particularly noticeable in the case of August, where as much as 19 mm more precipitation was recorded than for the 1991–2020 average.
The beginning of the growing season for the study area in 2021 was determined to be 24 March 2021, and the end was set for 22 November 2021. Thus, the growing season lasted for 244 days. In order to better illustrate the conditions in the study area during the growing season, a Gaussen–Walter climate diagram was made with the modification proposed by Łukasiewicz [
31]. Using the climate diagram makes identifying the periods with a precipitation deficit easy. In
Figure 1, it can be noticed that there were two periods of precipitation deficit. The first occurred from the end of May 2021 to the end of July 2021, and the second period of negative climatic water balance values occurred from the end of September to mid-October.
The characterization of pluvio-thermal conditions carried out using the Sielianinov hydrothermal coefficient
k coincides with the results obtained from the Gaussen–Walter diagram with Łukasiewicz modification (
Figure 1). At the beginning of the growing season (from March to May), the
k coefficient was 1.4–1.7, which was equivalent to optimal and even quite wet conditions in May (
Table 3).
The following months, mainly covering the second period of plant growth (June and July), experienced rainfall deficits and were classified as dry and quite dry (
Figure 1). On the other hand, September, with a total of 21 mm of precipitation, was very dry. This volume was 18 mm lower than the monthly average for 1991–2020 (
Table 2). Thus, it can be concluded that weather conditions for vegetation development were favorable only at the beginning of the growing season (March–May), whereas later, they deteriorated markedly due to precipitation deficits (June–July, September–October). The exception is August, which was classified as optimum. It is worth noting that in November, conditions were also improved (wet); however, this was after the last meadow harvest of that year had already been completed.
3.3. Biodiversity
During the study period, 18 taxa (including monocotyledonous and dicotyledonous species) were identified on the analyzed grass sward in all plots containing three repetitions. The number of species depended on the cut. Definitely, the first cut was characterized by the smallest number of identified taxa. Between the second and third cuts, the difference was small in quantity and quality (
Table 6). This proves a species rotation in the examined plots during the growing season. However, it should be remembered that the number of species refers to the amount of different plant species present in the cuts, whereas biodiversity takes into account both the number of species and their relative uniformity.
Differences in the number of taxa were observed in individual plots. The largest number of species was recorded in the first cut in the LWL plot. In this plot, the lowest number of species was recorded in the second cut. The quantitative assessment shows that changes in the number of species in the growing season in the analyzed plots ranged from one to three species, with the third cut being the most uniform in this respect (
Table 7).
Comparing the qualitative assessment with the quantitative one, we can see that the number of species does not correlate with the species composition. Despite the largest number of species recorded in the first cut, the total number of identified taxa in plots was the smallest (
Table 6). This proves a greater species similarity between some of the plots in the first cut and greater species diversity in combinations in the second and third cuts.
It can be seen that the addition of silicon, together with increased soil moisture, increased the number of species. This can be seen when we compare HWL and HWL_Si plots. The number of species in the first cut for plots with silicon (HWL_Si and LWL_Si) was also higher than the number of species in second and third cuts for these combinations. It can therefore be concluded that the combination of silicon with humidity increases the number of meadow plant species.
When analyzing biodiversity based on the Shannon–Wiener index (H′), it is noted that the following plots, respectively, characterized the greatest species variability: HWL_Si in first cut, LWL_Si in second cut, and LWL in the third cut (
Table 8).
Species inventory and valorization using the Shannon–Wiener index can be used for meadow plants [
52,
53,
54,
55]. Studies by Magurran [
56] on meadow biodiversity in various grassland types indicate that in a eutrophic meadow, the number of registered plant species can reach up to 42, and the value of H′ reaches 4.82. In an oligotrophic meadow, the author of the abovementioned publication identified a maximum of 25 plant species, and the value of H′ was 3.97. The meadow analyzed in the study was eutrophic, but the number of registered species and biodiversity was much lower than in the eutrophic and even oligotrophic meadows studied by Magurran [
56].
Research indicates that biodiversity in highland and lowland meadows can vary due to various environmental factors such as sunlight, temperature, humidity, soil composition, and nutrient availability. Highland meadows, which are often less urbanized and less intensively used, show higher biodiversity than lowland meadows, which are often intensively used and dominated by plant monocultures [
53,
57,
58]. The value of the H′ index for mountain meadows may be around 3–3.5. The values of this indicator may vary significantly depending on the region, period, weather conditions, or soil and climatic conditions [
59].
The Shannon–Wiener index is an important tool for assessing biodiversity, but it is not the only one. It should be used in conjunction with other assessment methods to obtain a complete picture of biodiversity in permanent grasslands. Therefore, the study also calculated the Simpson’s index (D), which describes the biodiversity of various plant communities, including forests, deserts, steppes, sea coasts, lakes, and rivers [
60,
61]. In forests, the Simpson index can range from 0.1 to 0.9, depending on the number of species of trees, fungi, and other organisms that are present. In deserts, the Simpson index may be lower, ranging from 0.1 to 0.5, due to the harsh environmental conditions and a limited number of species. The Simpson index may be higher in the steppes, ranging from 0.5 to 0.9, due to more favorable environmental conditions and more species of grasses and other plants. On the other hand, on sea coasts, the value of Simpson’s index can vary from 0.1 to 0.9, depending on the type of coast, water depth, and other factors.
The results using the Simpson’s index (D) confirmed the highest biodiversity of the HWL_Si plot in the first cut (
Table 9). In the second cut, the highest biodiversity was recorded in the LWL_Si plot, although, apart from the HWL, in which the lowest biodiversity was recorded, in other cases, the biodiversity was quite even. In the third cut, the highest biodiversity was again observed in the HWL_Si plot. The biggest difference in biodiversity (between HWL_Si and HWL plots) was also recorded here, amounting to 0.1665.
Biodiversity analysis using the Shannon–Wiener Index (H′) and Simpson’s Index (D) indicates a tendency to increase biodiversity in conditions of increased soil moisture in combination with the silicon used.
In this paper, research was also carried out to determine the total species frequency of occurrences (repetitions) of identified species using the iNEXT online tool (iNterpolation and EXTrapolation) [
45]. The results of species frequency for individual research plots are presented in
Figure 4. In this chart, two groups of plots are visible: the first includes LWL and LWL_Si, and the second includes HWL and HWL_Si. It can therefore be concluded that the species frequency on LWL and LWL_Si was comparable. A similar situation occurred concerning HWL and HWL_Si. It is worth noting that the groundwater level was a factor shaping the division of the combination into two groups. This confirms the analysis using the Shannon–Wiener index (H′) and Simpson’s index (D). A higher frequency of occurrences of identified species of about 14 was noted on LWL and LWL_Si plots. On the other hand, for HWL and HWL_Si, it was lower and reached about 11. However, it should be remembered that overlapping confidence intervals indicate no evidence of significant differences in species frequency between plots. Thus, only a tendency toward forming two groups within the study plots can be observed.
Analyses made using the iNEXT tool indicate that the groundwater level, which translates into soil moisture conditions, is of great importance on the occurrence of specific species and their frequency, because the division into two designated groups is clearly based on the moisture parameter.
To assess the similarity of the studied plots with the grass sward, the Sørensen’s similarity index was also calculated, which is often used in ecological studies, including in relation to meadow habitats [
62,
63,
64,
65]. The values of the Sørensen coefficient for meadows may vary depending on the region and habitat characteristics. The values of this coefficient range from 0 to 1, where 1 means full similarity, and 0 means no similarity.
In the case of the analyzed plots, their differentiation can be noticed. The results confirm the analyses using the Shannon–Wiener index H′ and Simpson’s index D indices. It can be seen that the values of the Sørensen coefficient depend on many factors, such as the diversity of plant species, topography and weather, and climatic and soil conditions. In addition, these values are influenced by factors such as irrigation, the degree of fertilization, or the preparations used that affect the growth and development of plants. On average, in three cuts, the plots HWL_Si:HWL, HWL_Si:LWL, and HWL_Si:LWL_Si showed the greatest similarities. This was especially true for the first and third cuts. Nevertheless, differences in similarities between plots depending on the cut are visible (
Table 10,
Table 11 and
Table 12).
To sum up, the analyses of the Sørensen coefficient value, as well as the analyses made with the use of iNterpolation and EXTrapolation, indicate that the groundwater level is more important for shaping the diversity indices than fertilization with silicon at the dose adopted in this study.
3.4. Plant Parameters
The final average plant heights obtained for each plot on the cut days is shown in
Table 13. In the first cut, the highest value was obtained in the plot with a lower water level (LWL) of 59.9 cm. The lower values on the HWL were most likely contributed by too high a groundwater level and too much soil moisture, which caused plant inhibition. Scientists state that excess water can contribute to a reduction in oxygen content in the soil and thus limit plant growth [
66,
67]. It should be noted, however, that in the silicon plot with lower groundwater (LWL_Si), the average plant height was lower than at LWL, at 55.5 cm. A similar relationship occurred on the site with a higher water level where a value of (HWL) 54.6 cm was obtained, and the one with silicon (HWL_Si) was 49.5 cm. Thus, it can be seen that in the first cut, the Si application contributed to a decrease in plant height. This trend also occurs in the next cut, but only in the site with a lower groundwater level, where the plot with the antitranspirant (LWL_Si) achieved an average of 4.5 cm lower grass height than LWL (45.1 cm). HWL_Si recorded vegetation 0.5 cm higher than on HWL. During the third cut, the average height was also greater on HWL_Si (36.3 cm) than on HWL (35.4 cm). Thus, the opposite trend from that during the first cut is noticeable. On the other hand, the exact value of 33.9 cm was recorded within the site with lower water levels for both plots (LWL and LWL_Si). Thus, it was concluded that, based on the results obtained, the relationship between plant height and application of the silicon product could not be determined. This is also confirmed by the statistical analyses performed in Statistica (version 13). The obtained measurement results were subjected to a two-way ANOVA analysis of variance to evaluate the effect of a higher water level and silicon application on plant height. The analysis showed that the main factors tested had no significant effect (at α = 0.05) on the results obtained, and the interaction between them was not statistically significant. Therefore, it can be concluded that neither silicon application nor a higher water level significantly affected plant heights during the growing season.
However, when analyzing the final results for individual cuts of the meadow, a noticeable trend shows that the highest average plant height was achieved during the first cut, and the lowest was reached during the third cut for each plot studied. Regardless of the groundwater level and whether it was a plot with or without silicon, the highest result within each combination was recorded during the first period of plant growth, and the lowest was recorded during the last cut.
For further analysis of plant heights for individual plots, heat maps were made in R Studio, considering all measurements taken during the growing season. The measured plant heights were normalized, resulting in a uniform scale from 0 to 1. On the dendrogram (
Figure 5), it is noticeable that the most similar plots regarding plant height are HWL and HWL_Si. The second pair with close results is LWL and LWL_Si, although these are less similar to each other than the previous pair. These results are in line with those obtained concerning biodiversity (
Figure 4), when it was also noted that the plots form two distinct groups, depending on the groundwater level (higher/lower). It can also be inferred from
Figure 5 that high plant height values were common in the HWL_Si and LWL plots. However, if we look at the dates of the measurements, it can be seen that high values for LWL were recorded only in May, June, and early July, that is, during the first and early second growth of the meadow. It is worth noting that during the first growth of the plants, the water table was relatively shallow below ground level (
Figure 2) compared to the rest of the growing season. This is why the LWL plot achieved such high heights. At the same time, the lowest values were recorded on HWL_Si among all the plots. This is most likely because the groundwater table and soil moisture were too high, and there was a reduction in the oxygen content of the soil, which hindered the development of vegetation [
66,
67]. Simultaneously, silicon contributed to a decrease in plant height at this time. It should also be noted that the values obtained depend on the plant species present within the plot and their growth rates during the growing season. In the later part of the growing season (July, August, September), a decrease in the groundwater level was observed, and thus higher values of plant heights were obtained in the HWL plot, where water was maintained throughout the season thanks to a closed valve on the ditch. The LWL_Si plot had lower plant height values, as shown by the dark colors on the heat map.
The NDVI values obtained from field measurements for individual plots were also analyzed. Again, the results were normalized and presented as a heat map (
Figure 6). Considering these values, it is noticeable that the most similar plots regarding NDVI values are HWL and HWL_Si. The second similar pair is LWL and LWL_Si; however, as in the case of plant height, they are less similar to each other than the pair HWL with HWL_Si. These results coincide with the plant heights and the results obtained in the biodiversity analyses (
Figure 4), where the formation of two distinct groups was observed within the plots studied. In the case of NDVI values, it can be seen that the highest results were achieved in the HWL plot, where the yellow color on the heat map dominates (
Figure 6). Only during the measurement on 19 July 2021 were low values recorded. However, it should be noted that this measurement was made only five days after the sward was cut, hence the NDVI results were lower than on other dates. Looking at the normalized heat map comprehensively, the predominance of darker colors on the LWL and LWL_Si plots can also be seen. Thus, in most cases, the NDVI results obtained on these plots were lower than those on higher groundwater-level plots. This indicates a trend that the groundwater level can positively affect NDVI values. This is consistent with an earlier study by Marín [
68], which showed that NDVI values are higher with full turfgrass irrigation than with deficit irrigation. However, it is worth noting that the tendency seen in the heat map was not statistically significant. A two-way ANOVA analysis conducted in Statistica software to highlight the effect of higher groundwater levels and silicon application on the NDVI index did not show a significant effect of the main factors or their interaction on NDVI values at α = 0.05.
In summary, the studies on plant height and NDVI index showed no significant effect of silicon application and higher groundwater levels in this meadow.
3.5. Yield
Due to the nature of the study area, yield evaluation was carried out three times during the growing season. Therefore, the meadow was cut on 31 May 2021, 14 July 2021, and 30 September 2021. The dry matter results based on which the yield of the meadow was evaluated for each of the four tested plots (HWL, HWL_Si, LWL, LWL_Si) are shown in
Figure 7.
Looking at the dry matter values obtained from each cut, it is possible to see a tendency that the area where silicon was applied received lower yields than the sward area without its application. This relationship is evident in each of the cuts. For example, in the first cutting, the dry matter value achieved from the HWL plot was 4365.39 kg·ha−1, whereas that from HWL_Si was 3489.42 kg·ha−1. The plot fertilized with silicon yielded 20% less (875.97 kg·ha−1). Analyzing the results for the site with lower groundwater levels, a similar relationship can be observed. The dry weight for the LWL plot was 4587.14 kg·ha−1, whereas for LWL_Si, it was 3842.25 kg·ha−1 (16% lower yield). The same trend was noted for the second cut; the difference between HWL and HWL_Si was 621.69 kg·ha−1, and between LWL and LWL_Si, it was 582.21 kg·ha−1. In contrast, the dry matter value obtained from the HWL plot in the third September cut was 3706.08 kg·ha−1, and from the HWL_Si plot, it was 2939.83 kg·ha−1. In the site with a lower groundwater level, silicon also decreased dry matter from 3467.47 kg·ha−1 (LWL) to 3111.19 kg·ha−1 (LWL_Si). Thus, it can be concluded that regardless of the mowing season (first, second, third cut) silicon caused a reduction in yields. This trend is noticeable regardless of the groundwater level.
Analyzing dry matter values across all cuts shows that for each of the four plots, the lowest yields were obtained during the third last cut. This trend is consistent with the results of meadow yields in Poland, where the first cut, regardless of the region, was characterized by the highest values and the last cut by the lowest values [
69]. The highest results in the first cut obtained in Racot were influenced by the favorable meteorological conditions in the first months of the growing season. At that time, there were no periods of precipitation deficits (
Figure 1), which facilitated vegetation development. The higher dry matter values obtained in the first cut on the site with a lower water level (LWL) than the site with a higher water level (HWL) are most likely the result of too much water on the irrigated plot. During the first half of the first grass growth period, soil moisture values on the LWL were close to the value corresponding to the field water capacity (FWC) (
Figure 3). In contrast, on the HWL site, the values were much higher than FWC throughout the first grass growth. The researchers note that at a moisture condition exceeding the PPW of the soil (pF is less than 2.0), there can be excess water and, consequently, insufficient oxygen necessary for proper plant development. It has also been found that soil air in the root zone should constitute 6–8% of the soil volume for meadow plants. Oxygen deficiency contributes to a decrease in the production of growth regulators in the roots and limits their development. There is also a disruption in the nutrient uptake by the roots [
67]. Moreover, oxygen deficiency in the soil can cause plants to stunt development and growth, causing yellowing, wilting, and even dying [
66]. Szajda [
70] notes that the high moisture content of the root layer corresponding to the field water capacity minus 6% of the volume contributes to reduced grass yield and excess water consumption for evapotranspiration [
70,
71].
During the second grass regrowth (1–15 June), there were precipitation deficits, and conditions in these two months were classified as dry and quite dry. This had the effect of reducing yields compared to the first cut on LWL. The problem of precipitation deficits during the second growth of vegetation in three-cut meadows in Poland, which can limit their productivity, was noted earlier in a study by Dembek et al. [
72]. During this growth, it can also be noted that moisture conditions were more favorable in the site with higher water levels, resulting in higher dry matter compared to the LWL site. Despite the relatively unfavorable meteorological conditions, the damming of water in the ditch contributed to an increase in the groundwater level, and the beneficial effect of subirrigation on yield was evident during this period. The results obtained in this cut align with the research of Jurczuk [
6], who found that meadow yield increases under the influence of subirrigation are more correlated with soil water conditions than meteorological conditions. Moreover, he notes that the positive effect of subirrigation on yields is particularly pronounced in dry and very dry years.
Analyzing the values obtained in the third cut, it can be seen that, regardless of the combination, they were the lowest of all three cuts in the growing season. These results are consistent with previously published results, which clearly show that the lowest yields characterize the third cut. During the third period of sward growth, the groundwater table was deep below the subsurface, reaching the lowest value over the entire season of 1.09 mbgl for LWL and 0.92 mbgl for HWL (
Figure 2). The pluvio-thermal conditions in the meadow’s third growth were also unfavorable. In the initial phase of growth (July), they were classified as quite dry, and in September, they were classified as very dry (
Table 3). At the same time, September was a month with high precipitation deficits (
Figure 1). Low groundwater levels and unfavorable pluvio-thermal conditions contributed to low grass growth and low yields during the third period of sward growth.
Looking at the total yields obtained from the entire year, it can be seen that the highest value was obtained from the high groundwater level (HWL) plot and amounted to 12.69 Mg·ha
−1. (
Figure 8). From the high groundwater level + silicon (HWL_Si) plot, the annual dry matter volume was 10.43 Mg·ha
−1. Comparing these two plots, it can be noted that the Si application caused a 17.8% decrease in yield. Concerning the site with lower groundwater levels, silicon application resulted in a 14% reduction in dry matter (from 12.05 to 10.36 Mg·ha
−1). Considering only the groundwater level on an annual basis, the higher water level (HWL) contributed to higher yields on both plots without and with Si application. This is because the dry matter with HWL was 0.64 Mg·ha
−1 higher than LWL. A similar trend was also noted in an earlier study by Jurczuk [
6,
73], which showed that it is possible to improve grassland yields due to subirrigation.
The positive effect of water management based on controlling water supply using renewed irrigation facilities in a system of subirrigation on the yield from grasslands for the area was also observed by Napierała et al. [
74]. However, their results of actual yield values estimated for the area in question using the model are much lower (3.7–7.9 Mg·ha
−1) than those obtained in the present study.
To identify significant differences between the yields of each plot in this study, the obtained data on the amount of dry matter from each cutting were subjected to statistical analysis. With the assumptions met (normality of distribution and homogeneity of variance), a two-way ANOVA was performed to analyze the effect of higher groundwater level and silicon application on dry matter. The analysis showed that the interaction between the studied factors was not significant. Therefore, a two-factor analysis was conducted for the main factors. Statistically significant differences (at the α = 0.05 level) in dry weight were shown for silicon application (
Table 14). These occurred in each of the three cuts studied. Thus, it can be concluded that silicon application contributed to a significant reduction in dry matter in each cut. Moreover, during the second cutting of the meadow, statistically significant differences in the dry matter were also observed concerning the higher groundwater level. In this case, it can be concluded that the higher water level significantly increased yields during the second cut.
Radkowski et al. [
75] also conducted research on silicon application’s effect on the meadow. Species such as
Lolium perenne,
Festuca pratensis,
Dactylis glomerata,
Poa pratensis,
Festuca rubra,
Phleum pratense,
Trifolium pratense L.,
Taraxacum officinale coll., as well as
Achillea millefolium L. dominated the area of their experiment. Radkowski et al. [
75] found no statistically significant effect of foliar silicon application on dry matter yield. However, when we look at their results, a trend is noticeable in that Si caused a decrease in dry matter values. In the control plot, they obtained a value of 5.96 Mg·ha
−1, whereas when silicon fertilizer Optysil was applied at a rate of 0.5 dm
3·ha
−1, the dry matter yield was 5.26 Mg·ha
−1, and at a rate of 0.8 dm
3·ha
−1, it was 5.66 Mg·ha
−1. Thus, a reduction in dry matter yield can be seen, which was also noted in this study. However, Mastalerczuk et al. [
76] obtained different results in their study showing the positive effect of foliar application of fertilizers with silicon on the yield of the grass–clover sward. However, it should be noted that in the study in question, different fertilizers were used than in the present study. In addition, different doses were used, amounting to 4 kg·ha
−1 for Herbagreen fertilizer and 1 l·ha
−1 for Optysil, respectively. Moreover, during each grass sward growth, the fertilizers were applied twice (4 and 2 weeks before each harvesting), whereas in the present experiment in this paper, they were applied once for each grass sward growth. Differences in the results obtained may also be due to the presence of other species. The study by Mastalerczuk et al. [
76] was conducted on a prepared grass–clover mixture with the following composition:
Lolium perenne L., cv. Solen,
Trifolium pratense L., cv. Nike and
Trifolium repens L., cv. Grasslands Huia. The different results may also be due to the different abilities of meadow plants to accumulate silicon. However, in previous studies on the grain yield of
Phleum pratense L., a positive effect of foliar application of fertilizer with silicon (Optysil) was noted, significantly increasing this parameter. It was also reported that the obtained grains were larger and showed a higher germination capacity than the control seeds [
77]. Studies were also conducted on two grass–legume mixtures, consisting of
Dactylis glomerata,
Festulolium braunii, and
Trifolium pratense or
Medicago x varia, and a grass mixture—
Dactylis glomerata,
Festulolium braunii, and
Lolium perenne—including the effect of silicon (Herbagreen fertilizers, Optysil) on botanical composition and nutritional value. They showed that botanical composition changed during the measurements, but mainly due to weather conditions and plant competitiveness. The effect of silicon application on botanical composition was slight [
78]. Moreover, other studies have shown no clear effect of foliar application of these fertilizers on the botanical composition of grass–clover swards containing
Lolium perenne L., cv. Solen,
Trifolium pratense L., cv. Nike,
Trifolium repens L., and cv. Grasslands Huia [
76]. On the other hand, the results obtained by Radkowski et al. [
75] show that the foliar application of Optisil influenced the botanical composition of pasture flora and thus improved the nutritive value of ensiled feed.
Borawska-Jarmułowicz et al. [
78] state that previous measurements of silicon fertilization of grass–legume mixture swards do not provide conclusive results. The present study partially confirms this conclusion, as no unequivocal effect of silicon application was observed on the meadow’s plant height and NDVI value. Statistically significant differences were noted only in the case of yield, in which there was a significant decrease in the amount of dry matter obtained after Si application. However, it should be noted that these results refer to a selected silicon-containing product (
Krzemian) and one specific dose (0.8 l·ha
−1 in each cut), and the work published to date does not indicate a clear trend on this issue. Therefore, it is recommended that further research be continued to obtain broader results for applying different doses and other silicon-enriched products. Tripathi et al. [
10], in their review of silicon, state that very limited information is currently known to determine the optimal amounts of Si needed for better plant growth at particular developmental stages. This topic should also be expanded on in future studies. The author also notes that there is still too little current knowledge to fully understand the role of Si in plant biology. The researchers also state that to date, few studies have been conducted on the effect of silicon fertilization on the nutritive value of individual grass and legume species and the quality of the sward of mixtures applied to grasslands [
78]. The effect of silicon on the quality and nutritive value of grasses is a good direction for measurements in the future, which will enable a better understanding of the interaction of silicon application with plant response. Increasing measurement data in this area and yield experiments will allow for a comprehensive evaluation of silicon’s effect on grasslands. The present study is a step towards expanding the knowledge of the impact of Si application in three-cut meadows and its effect on yield.