The effect of different mulch materials with varying thicknesses on weed control and yield in eggplant cultivation

The research was conducted at the Agricultural Application and Research Center of Iğdır University. In the first year, eggplant seedlings were planted on 7 May 2021, and in the second year on 29 April 2022, with row spacing of 70 cm and plant spacing of 50 cm. The seedlings were planted in tilled soil with two-thirds of the seedling’s height below the soil and one-third above the soil. Before planting, 45 kg · da⁻¹ of NPK fertiliser (Toros Classical Fertilizers) was mixed into the soil. After planting, a drip irrigation system with drippers spaced 20 cm apart was installed, and the first irrigation was carried out immediately. The system utilised drippers with a flow rate of 2 L · hr⁻¹ to ensure efficient water distribution. Subsequent irrigations were performed weekly, with the duration adjusted based on rainfall, soil moisture levels and the plant’s water requirements. Given the 20 cm dripper spacing, each irrigation session lasted for approximately 1–2 hr, depending on the environmental conditions and soil absorption capacity. This study was designed using a randomised block design with 11 treatments (3 mulch materials: CP, WS and mowed grass, and 3 different mulch thicknesses: 5 cm, 10 cm and 15 cm, with or without weeds), replicated 4 times, resulting in a total of 44 plots. Plotting was done after the eggplant planting and before applying the mulch materials. The experimental layout was designed with plots measuring 4.0 m × 1.5 m, where 16 seedlings were planted in each 6 m² plot. A 0.5 m strip of bare soil was left between the plots and blocks. The total experimental area covered 376.25 m². For plot division, stakes were fixed to the ground, and strings were used within the strips. Mulch materials were applied on 25 June 2021 in the first year and on 10 June 2022 in the second year. Care was taken to ensure that the eggplant seedlings were not covered by mulch. In the weed-free plots, weeding by hoeing and manual removal was carried out according to the weed emergence status.

To identify the weed species and their densities in the experimental area, a 1 m² wooden frame was randomly placed, and the weeds within the frame were counted. The weed density was determined based on the arithmetic mean. The weed density (plants · m⁻²) was calculated by dividing the total number of plants counted in the survey by the number of surveys conducted, with the density of each species determined individually (Odum, 1971): Density (plants ×m−2 ) =BM $${\rm{Density}}\,\left( {{\rm{plants}}\, \times {{\rm{m}}^{ - 2}}\,} \right)\, = {{\rm{B}} \over {\rm{M}}}$$

B is the total density of each species in the surveyed areas (number of plants), and M is the total surveyed area (m²).

Additionally, the weed densities of the species found in the experimental area were rated using the density scale (Table 3) proposed by Üstüner and Güncan (2002).

After mulching materials were applied in both years, weed emergence was determined by counting the weed density in m² once a month. A total of three counts were made during the study. The weed density was counted for both the total density in the plots and the species-specific density. For the plots with mulching materials, only the emerged weeds were counted, and these were recorded separately for each species. After evaluating the species, the total weed density in m² was determined for each plot. In plots with mulching materials, the weed species Sorghum halepense, Convolvulus arvensis and Xanthium strumarium were evaluated separately due to their homogeneous emergence. Other weeds that did not show a homogeneous emergence were evaluated as a whole.

To determine the total weed density for all treatments, 1 m² frames were used for sampling. The density values for each count date were calculated using the formula proposed by Odum (1971), as mentioned earlier. According to this method, the total number of weeds was divided by the total surveyed area to determine the density for each treatment.

In both years of the study, before the final harvest, the weeds present in all plots were cut at the soil surface, placed in paper bags and transported to the Herbology Laboratory. In the laboratory, the samples were dried in an oven at 70°C for 72 hr and then weighed to determine their dry weight accurately. These data were recorded, and the dry weight of weeds per m² was calculated. Additionally, the percentage effect on weed dry weight compared with the weed-infested control plots was determined.

The eggplant harvest was carried out between 23 July and 12 October 2021 in the first year and between 28 July and 5 October 2022 in the second year. Several parameters were measured to assess the yield components and the total yield, including plant height (cm), number of fruits per plant (count), fruit weight (g), yield per plant (kg) and total yield (t · ha⁻¹). Plant height was measured from the base of the plant to the final point of growth using a tape measure or ruler for accuracy. Fruit weight was determined using a digital precision scale (±0.01 g accuracy) to ensure accurate measurements. Yield per plant and total yield were calculated from the total number of fruits harvested and their recorded weights, providing a comprehensive assessment of productivity. Yield and yield components were compared for mulched plots against weed-infested control and weed-free control plots (using hoeing and manual weeding).

The weed densities, weed dry weights, and eggplant yield and yield components were evaluated based on three different counts made during the study. The relevant data were subjected to one-way analysis of variance (ANOVA). Means were compared using the Duncan multiple range test (p<0.05). Statistical analysis was performed using SPSS 20 (IBM, 2011). Additionally, a series of statistical analyses were performed to relate the findings of the study. After data transformation/normalisation, correlation analysis was performed using JASP (version 0.16.4.0, December 2020) to determine the degree of relationship between the variables and to identify the strong and weak correlations. Heatmap clustering (SRplot) was performed to visualise data density and identify similar groups. Hierarchical clustering (SRplot) was used to group the treatments based on similarities. Network analysis (PAST Software, 2023) was conducted to determine and visualise the relationships between variables. Principal Component Analysis (PAST Software) was also applied to reduce multivariate data to fewer dimensions and identify the significant variables.

A total of 17 weed species from 8 families were identified. The families, scientific names, common names and life cycles of the weed species found in the experimental area are given in Table 4.

According to the study results, S. halepense was the most abundant species in both years (14.5 plants · m⁻² in 2021 and 10.6 plants · m⁻² in 2022). When compared with the literature, species such as C. arvensis, Chenopodium album, Portulaca oleracea and S. halepense were commonly identified in both this study and prior research conducted by Tepe (2022), Gürbüz et al. (2021) and Kaya (2011). Similarly, Cynodon dactylon and Solanum nigrum, identified in studies by Aliyu and Lagoke (1995) and Altinok (2013), were consistent with the findings of this research. Marques et al. (2017) also reported major weed species such as Eleusine indica, Portulaca oleracea and Cyperus rotundus in the experimental areas. In summary, the diversity and prevalence of weed species identified in the eggplant planting area align with findings from previous studies, highlighting the widespread nature of these species in agricultural fields.

The three periodic counts showed a significant difference in the density of the weeds. These differences varied depending on the mulching material used. Statistical analysis showed that the effects of different mulching materials on weed density in eggplant fields were significant at the 1% level (p<0.01) for both years and all counts. The average values of weed density and the groupings formed are presented in Figure 1. These results underscore the effectiveness of mulching in reducing weed populations and its potential to influence specific weed species.

Figure 1.

Effect of mulching on weed population (CP, CTG and WS). Lowercase letters indicate groupings among the treatments, while uppercase letters indicate groupings among evaluations within each treatment. Differences between means indicated by the same letter are not significant at the 0.05 level. CP, shredded paper; CTG, grass clippings; WS, wheat straw.

In the evaluations of both years, statistically significant differences were observed among applications in terms of weed density. Although weed densities increased during the evaluation periods, all mulch applications achieved lower density values compared with the weedy control group. In both years, the lowest weed density in the final evaluation was achieved with the CTG-15 mulch material (3.37 plants · m⁻² in 2021 and 3 plants · m⁻² in 2022). The highest weed density was observed in the weedy control plots, with values of 67 plants · m⁻² in 2021 and 75.5 plants · m⁻² in 2022. Overall, it was seen that mulch materials successfully controlled weeds compared with the control plots and that weed density further decreased as mulch thickness increased. These findings support previous studies such as those of Kaya (2011) and Jodaugiene et al. (2006). Kaya (2011), in a study where different plants were used as mulch material, indicated that mulching reduced weed density. Jodaugiene et al. (2006) expressed that increasing mulch thickness enhanced weed control, especially by preventing the emergence of annual weeds. Additionally, Ahmed et al. (2016) and El-Semellawy and El-Koumy (2015) emphasised that mulching suppresses weeds similarly. These results demonstrate that mulch material and thickness are important factors in weed control and that mulching is particularly effective in controlling annual weeds. Furthermore, Gradila et al. (2023) stated that mulching effectively controlled weeds, which is consistent with our study.

In this study, it was seen that the effects of the mulch materials on weed species varied. Specifically, S. halepense, C. arvensis and X. strumarium were prioritised because they were homogeneous and prevalent across the mulch materials. Additionally, other weed species were observed in the plots with mulch materials, but when their emergence was not homogeneous, they were evaluated as a whole in terms of the weed species present in the experimental area (Figure 1). The effect of mulching on S. halepense is given in Table 5, on C. arvensis in Table 6, on X. strumarium in Table 7 and on other weeds in Table 8.

Evaluations conducted on the weed species in the trial area revealed that the effects of different mulching applications and mulch thicknesses varied across weed species. Particularly, the S. halepense species, which was observed at high densities, persisted under mulch materials applied at a thickness of 5 cm and was able to maintain its high density even under some 10 cm mulch applications. S. halepense, compared with other weed species, was less affected by the protective impact of the mulch and continued to grow despite the presence of thick mulch layers. By contrast, observations of other dominant species, such as X. strumarium and C. arvensis, indicated that mulch thickness had a more pronounced effect on their density. A slight reduction in the density of these species was observed with mulch applied at 5 cm thickness, but this reduction remained limited. However, mulch applications at thicknesses of ≥10 cm significantly reduced the density of these species, providing effective control at these levels. Similar effects were observed on other weed species in the trial area, with weed densities decreasing significantly as mulch thickness increased. Overall, all mulch materials proved to be effective methods for suppressing weed growth. Furthermore, thicker mulch applications resulted in more effective weed suppression, leading to greater success in weed control. Compared with the control plots, weed densities in the mulched plots were significantly lower, demonstrating that mulch applications are a successful method for weed management (Tables 5–8).

Previous studies, such as those by Kaya (2011) and Jodaugiene et al. (2006), have shown that mulch thickness reduces weed density, with particularly greater effects on annual weeds. Similarly, Kosterna (2014) reported that higher mulch doses enhance weed control, findings consistent with our study. Kaya (2011) and Döring et al. (2005) also emphasised that straw and other mulch types are effective in controlling weeds, but straw mulch may have limited effects, especially when applied in low amounts. In this study, while S. halepense was able to maintain high density at even <5 cm mulch applications, the effect of mulch thickness was more pronounced on species such as X. strumarium and C. arvensis. Significant reductions in weed density were observed, especially with mulch thicknesses of ≥10 cm. Moreover, studies by Genger et al. (2018) and Marble et al. (2019) confirmed the ability of straw mulch to suppress broadleaf weeds.

Significant differences were observed in the dry weights of weeds in eggplant fields during both years (F = 116.782, p<0.01 in 2021; F = 155.467, p<0.01 in 2022). Weed dry weights varied depending on the type and thickness of the mulch materials (Table 9).

According to the data, significant differences were observed among mulch material types and thicknesses in terms of weed control efficacy. It was found that various mulch applications significantly reduced weed dry weight over the 2 years, and these effects were found to be statistically significant (p<0.001). When considering the average of both years, the lowest weed dry weights and the highest efficacy rates compared with the weedy control plots were obtained with the Paper 3 application (45.13 g · m⁻² weed dry weight and 86.34% efficacy rate). Based on the average weed control efficacy, the most effective applications were CP 15 cm, CP 10 cm and WS 15 cm, respectively. This indicates that thicker paper and straw mulches were particularly successful in controlling weeds (Table 9).

The findings align with the study by Gómez De Barreda et al. (2023), which identified three factors influencing the effectiveness of mulch management: mulch rate, installation timing and replacement rate. In this study, mulch thickness was found to be a significant factor affecting weed control. Thicker mulch applications were more effective in suppressing weed growth. This result parallels the findings of Jodaugiene et al. (2006) and Kaya (2011), which also demonstrated the effectiveness of thicker mulch layers. The impact of different mulch materials on weed control, as observed in Grassbaugh et al. (2004), is consistent with our findings. Hammermeister (2016) also noted that mulch can fully or partially control weeds in agricultural fields. In this study, weeds were predominantly suppressed. Moreover, the studies by Ateş and Uygur (2013) and Gürbüz et al. (2021) emphasised the positive effects of mulch on weed control, while highlighting that this effect can vary depending on the type and thickness of the mulch material. Similar to the findings of Kaya (2011) and Döring et al. (2005), this study observed limited effects of straw mulch at lower amounts. This aligns with the literature indicating that mulch was less effective against S. halepense but more effective against other weed species. Finally, studies by Genger et al. (2018) and Marble et al. (2019) confirmed the ability of straw mulch to suppress broadleaf weeds. Accordingly, this study found that straw mulch could be effective under certain conditions, with its effect becoming more pronounced with increased thickness.

Significant differences were found among all parameters evaluated in the study (plant height, fruit count per plant, fruit weight, yield per plant and overall yield) across the applications (p < 0.001), indicating that each application had different effects on plant performance (Table 10).

Yield differences were also observed according to mulch types and thicknesses. While grass applications generally provided higher yields than other mulch types, Paper and Straw applications also provided higher yield results than the control plot. The highest yield in the study was obtained in the CTG-15 cm (83.20 t · ha⁻¹) application in 2021. This value is approximately 75% higher than the control plot. These results are consistent with previous studies emphasising the weed-suppressive effects of mulch materials (Jodaugiene et al., 2006; Kaya, 2011; Genger et al., 2018). The eggplant yield was higher in 2021. The slightly higher temperature in 2021 compared with 2022 (Table 1) may explain this difference. This observation is consistent with the findings of Adamczewska-Sowińska et al. (2016), who state that eggplant growth and yield are significantly influenced by air temperature. When the average values are considered, CTG-15 cm application provided the highest yield with 80.86 t · ha⁻¹. When compared with the control (weedy) application, a yield increase of approximately 75% was achieved in Grass 3 application, which had the highest yield. These findings align with previous research on mulch-based weed control and yield enhancement (Hussain et al., 2022; Tarrant et al., 2024). In addition, Paper and Straw applications generally provided higher yields than the control application. For example, the average yield in the WS 10 cm application was 74.17 t · ha⁻¹, which is approximately 60% higher than the control application. As a result, it was clearly seen in the study that mulch applications had a positive effect on yield. In addition, increasing the mulch thickness further increased the yield and revealed that mulch applications positively affected plant development. These findings show that mulches such as grass, paper and straw are an effective strategy, especially for weed control and increasing yield (Table 10). Hashimi et al. (2019) reported that mulching had positive effects on eggplant yield. Pirboneh et al. (2012) stated that straw mulch provided the highest fruit yield and these findings were similar to the current study. Gürbüz et al. (2021) also stated that mowed weed wastes provided positive effects on yield when used as mulch and observed yield increases. In addition, Ateş (2007) and other researchers emphasised that straw mulch is effective in increasing yield. In the study, it was observed that plant development and yield increased with increasing mulch thickness. Studies conducted by Genger et al. (2018) and Marble et al. (2019) also stated that straw mulch suppressed broadleaf weeds and increased yield. Syed Shahnaz (2023) emphasised the potential of mulches to increase plant yield while providing cool and moist soil. Another study showing that yield losses are caused by weeds was conducted by Marques et al. (2017), and in this study, weeds were found to reduce eggplant yield by 78%. Similarly, Almhemed and Ustuner (2022) stated that they increased the yield loss caused by weeds by up to 92.52% compared with the control plot. Mulching creates a physical barrier that prevents weed emergence by blocking sunlight and reducing temperature fluctuations on the soil surface (Iqbal et al., 2020). Organic mulches such as CTG and WS provide additional benefits by improving soil moisture retention, increasing nutrient availability through decomposition, and promoting microbial diversity (Neal et al., 2015; Omidvar et al., 2023). However, the effectiveness of mulching is affected by the type and thickness of mulch used. In this study, the most effective treatment was 15 cm thick CTG mulch (CTG-15). Thicker mulch layers provide a more effective barrier to light penetration, which provides success in weed control. This finding is consistent with previous studies (Jodaugiene et al., 2006). CP and WS applications were also found to be effective, but their performances were relatively lower than CTG. This may be due to differences in the material structure, decomposition rate and moisture retention capacity. Similar results were reported by Genger et al. (2018) and Marble et al. (2019), who reported that straw mulch in particular was less effective at low thickness levels. Interestingly, this study showed that S. halepense maintained its high densities even with mulch applications of 15 cm thickness, highlighting the species’ resistance and aggressive growth habit. This result is consistent with previous studies indicating that perennial weeds are generally more difficult to control with mulch than annual species (Kaya, 2011; Marques et al., 2017). By contrast, X. strumarium and C. arvensis were effectively suppressed with mulch thicknesses of ≥5 cm. This finding suggests that the effectiveness of mulching may vary depending on the weed species present. The findings also show that CTG (CTG-15) are particularly effective in increasing eggplant yield. This can be explained by the fact that CTG retain soil moisture during the decomposition process and increase nutrient release. It is also supported by previous studies that organic mulches contribute to soil health and productivity over time (Gürbüz et al., 2021; Syed Shahnaz, 2023). In addition, the positive correlation between mulch thickness and yield increase suggests that thicker layers are beneficial for increasing productivity. Comparative studies on plastic and organic mulches generally emphasise the superior performance of plastic mulches in changing soil temperature and moisture retention (Tarara, 2000; Sabatino et al., 2018). However, the environmental disadvantages of plastic mulches, especially disposal and degradation issues, necessitate the search for sustainable alternatives (Steinmetz et al., 2016; Di Miceli et al., 2024). The findings of this study indicate that organic mulches, especially CTG, offer a viable alternative to plastic mulches by providing effective weed control and yield increase without adverse environmental impacts. These data clearly show the great effect of weed control on yield and how mulch applications can prevent these losses.

A range of significant differences were observed in all fruit quality parameters given evaluation, including fruit length, fruit diameter, fruit firmness, marketable yield and unmarketable yield across the different mulch applications (p<0.001). These findings suggest that the type and thickness of mulch have substantial effects on fruit quality and productivity (Table 11).

Mulching had a significant impact on the quality of eggplant fruit and the resulting marketable yield. The longest fruits were recorded in the CTG-15 cm treatment, with a mean length of 22 cm, whereas the shortest fruits were observed in the weedy control, measuring 12 cm. In a similar manner, the fruit diameter was found to be greatest in the CTG-15 cm (10 cm) group, and least in the weedy control (4 cm). Furthermore, the mulching treatments, specifically CTG-15 cm, CP-15 cm and WS-15 cm, exhibited the highest levels of fruit firmness (4.5 kg · cm⁻²). By contrast, the weedy control treatment demonstrated the lowest levels of firmness (2.0 kg · cm⁻²). With regard to marketable yield, the WS-15 cm treatment yielded the highest (71.62 t · ha⁻¹), followed by CTG-15 cm (71.01 t · ha⁻¹) and CP-15 cm (70.26 t · ha⁻¹). This suggests that thicker mulch layers are conducive to enhanced productivity. Conversely, the weedy control treatment resulted in the lowest marketable yield (29.39 t · ha⁻¹), demonstrating significant yield losses due to weed competition. Unmarketable yield exhibited a comparable trend, with the maximum losses recorded in the weedy control (10.75 t · ha⁻¹), while the WS-15 cm treatment exhibited the lowest (6.00 t · ha⁻¹), underscoring the efficacy of mulching in mitigating fruit defects. The findings of this study demonstrate that increasing mulch thickness has a dual impact on fruit yield. On the one hand, it enhances fruit size and firmness, and on the other, it reduces unmarketable yield by minimising the adverse effects of weed competition and environmental stress. The utilisation of CTG, CP and WS, particularly at a thickness of 15 cm, has been demonstrated to be the most efficacious in enhancing the overall quality and yield of eggplants. The present study’s findings on the positive impact of mulching on eggplant yield and quality are consistent with the existing literature. For instance, Carter and Johnson’s (1988) study observed that various mulching materials significantly enhanced eggplant growth and development. A similar outcome was reported in a study by Memon et al. (2017), which found that mulching brinjal (eggplant) with materials such as transparent foil and polyethylene film resulted in substantial yield increases. Furthermore, research conducted by Carter and Johnson (1988) demonstrated that the use of different mulching materials had a positive influence on eggplant production. The findings of the present study demonstrate that mulch application is a successful strategy for enhancing eggplant yield and quality. Specifically, the results highlight the significant impact of mulch on weed suppression and yield improvement, with grass, paper and straw mulches promoting plant growth and increasing productivity. Additionally, mulch thickness was found to play a crucial role, as thicker mulch layers further suppressed weeds and contributed to higher yields. The findings emphasise the necessity of selecting appropriate mulch materials and optimising thickness to ensure the maximisation of both weed control and agricultural productivity.

There is a strong positive relationship between weed dry weight and weed density (r = 0.934, p<0.001). However, weed dry weight and density show negative relationships with eggplant yield and yield components. Similarly, with weed density, the highest negative relationship was observed with yield per plant (r = –0.745, p = 0.005), and the lowest negative relationship was with plant height (r = –0.707, p = 0.015). For weed dry weight, the highest negative relationship with weed dry weight was found between yield per plant (r = –0.764, p = 0.006), and the lowest negative relationship was found with fruit number per plant (r = –0.674, p = 0.023). These results indicate that an increase in weed density and dry weight negatively affects eggplant growth and yield (Figure 2).

Figure 2.

Correlations of weed density and dry weight with other parameters. WD, weed density; WDW, weed dry weights; PH, plant height; NFPP, number of fruits per; FW, fruit weight; YPP, yield per plant; Y, yield.

Heat map clustering clearly distinguished dependent/independent variables by separating them into two main clusters with a colour range (+1.5 to –1.5; from red to blue) indicating the obtained values (Figure 3). Among the main clusters, there were CP 5 cm, WS 5 cm and weedy control plot. Although clustering differed according to the mulch materials, especially plots with high thickness were in a cluster among themselves. The results obtained from heat map clustering revealed that although the mulch materials used in the study differed, they were effective in combating weeds, especially as the thickness increased.

Figure 3.

Heat map of the parameters corresponding to the applications (CP, CTG and WS). CP, shredded paper; CTG, grass clippings; WS, wheat straw.

As a result of the hierarchical analysis, applications were collected in two main groups. In the first group, the weedy control was located alone, while in the second group, applications with mulch materials spread in 5 cm thickness formed a group (Figure 4). In the network graph analysis, the thickness between the lines shows the strength of the relationships. Thinner or lighter lines indicate weaker relationships, and thicker lines indicate stronger relationships. Consistent with the heatmap clustering and hierarchical clustering, a clear distinction emerged. In this analysis, the weedy control group was only slightly related to WS 5 cm. The other treatments were related to each other to some extent (Figure 4). The main findings from these analyses indicate that different treatments had different degrees of impact on eggplant yield and yield components, and that weed control in particular played an important role.

Figure 4.

Applications (A) hierarchical clustering and (B) network graph analysis. CP, shredded paper; CTG, grass clippings; WS, wheat straw.

Principal component analysis was performed. In order to explain the variation rate, agronomic traits of eggplant and weed dry weight and density were distributed on a biplot pair (Figure 5). Accordingly, the first two components (PC1: 86.69% and PC2: 9.24%) explained 95.63% of the variability of the original data. Such a high explained variance clearly shows that principal component analysis can be successfully used to evaluate the effect of the estimated parameters together with the applications. The first component (PC1) was negatively correlated with weedy control (with a score of –5.57), CP-5 cm (with a score of –2.01), WS-5 cm (with a score of –1.89) and CTG-5 cm (with a score of –1.51), while it was positively correlated with other treatments (Figure 5).

Figure 5.

Principal component analysis of parameters and applications.

Advanced analyses such as correlation, heat map clustering, hierarchical clustering, network plot analysis and principal component analysis that we conducted on the mean values of the variables in the study support variance analysis, and can be considered to be very powerful in reducing the size of the variables considered for analysis by clustering and correlating the findings. In general, the effects and relationships between the applications and parameters are clearly stated.