SJA_41_4_1507-1522

Introduction

Sugarcane is a crop that thrives in tropical and subtropical climates and is propagated vegetatively. It contributes approximately 80% to global sugar production and 40% to global biofuel production (Budeguer et al., 2021). Sugarcane is the primary commodity for sugar production, a basic necessity for the Indonesian population (Abror et al., 2023). As a staple commodity in the agricultural sector, sugarcane plays a vital role in the national economy of Indonesia. However, the low productivity of sugarcane plantations has hindered the Indonesian government’s efforts to meet the high domestic demand for sugar, and according to data from Central Bureau of Indonesian Statistics (2024), sugar production in 2023 reached only 2.23 million tons, which represents a 7% decrease from 2.39 million tons in 2022.

To compensate for the production deficit, sugar imports remain a key government strategy to meet national consumption needs. In 2023, Thailand contributed 46.8% (2.37 million tons), Brazil 28.96% (2.15 million tons), Australia 17.6% (893 thousand tons), India 6.14% (311 thousand tons), and Vietnam 0.24% (11.99 thousand tons) (Central Bureau of Indonesian Statistics, 2024). Furthermore, based on data from Central Bureau of Indonesian Statistics (2024), a significant gap exists between sugar demand and production in Indonesia. The total demand for white crystal sugar (WCS) reached 3.20 million tons, while domestic production was considerably lower. This disparity is primarily attributed to the suboptimal productivity of sugarcane crops. Regarding regional production, South Sulawesi Province contributed only 6% to the national total. The trend in sugar production over the past five years aligns with this pattern, with an average annual production of 2.27 million tons.

Poor crop management practices, such as the use of low-yielding varieties and inappropriate fertilization, have been identified as major contributing factors to sugarcane’s low productivity (Desalegn et al., 2023). Consequently, the Indonesian government has maintained its import policy to meet industrial and household sugar demands (Inayaturrohmah et al., 2023).Improving on-farm practices can enhance domestic sugar production in addition to imports. One critical on-farm challenge is sugarcane’s low tolerance to abiotic stresses such as elevated temperatures, which increase evapotranspiration rates. These conditions adversely affect the plant’s stress response, leading to decreased photosynthetic efficiency, slower growth rates, reduced productivity, and ultimately lower sugar yields.

Improving sugarcane productivity can be achieved through appropriate fertilization strategies that align with the plant’s nutrient requirements. Fertilization practices at the farm level typically focus on supplying macronutrients such as nitrogen (N), phosphorus (P), and potassium (K). However, beneficial nutrients like silica (Si), which can significantly improve plant health, are often overlooked. According to Sari and Hariyono (2021), sugarcane is a silica-accumulating plant with a high absorption capacity for this element, making silica an important nutrient for sugarcane. The plant can absorb between 500 to 700 kg of silica per hectare. This amount far exceeds the uptake of essential nutrients such as nitrogen at 500 kg·ha-1, phosphorus at 40–80 kg·ha-1¹, and potassium at 300 kg·ha-1.

Crop productivity is also influenced by the variety used. Superior sugarcane varieties are characterized by high yield potential (Kartika and Supriyanto, 2019). Commonly used varieties with high yield and sugar content include Bululawang and PS 862. According to varietal descriptions published by the Indonesian Ministry of Agriculture, PS 862 can yield a minimum of 74.68 tons·ha-1. Mutahdi (2024) reported that the Bululawang variety achieves productivity levels of up to 86.9 tons·ha-1 with a stem height of 2.33 meters. Research by Sudarto (2020) on estimated sugar yield showed significant differences between Bululawang and PS 862, with Bululawang achieving 8.08% and PS 862 reaching 9.06%.

In addition to their genetic potential, productivity of sugarcane varieties is significantly influenced by their growing environment (Riajaya, 2022). Distinct from PS 862 and Bululawang, the Tolangohula 2 (TLH02) variety is a relatively new cultivar originating from Kendari. It was introduced by Mr. Huang Chung Wu under the clone code CW 2012. Based on official correspondence from the Ministry of Agriculture (2010), TLH02 exhibits a production potential ranging from 70 to 110 tons·ha-1 and a sugar yield content potential between 9.20% and 10.41%. One promising innovation to improve sugarcane varieties’ productivity and brix content is the application of silica fertilizers. Research by Suciaty et al. (2019) found that the best combination of nanosilica concentration and rice husk was 1.25 ml·L-1, although this study was conducted on soybean plants.

The effects of silica fertilization on sugarcane productivity have been previously investigated (Li et al., 2020; Amin et al., 2023). As a beneficial element, silica has also been proven to increase sugarcane resistance to drought stress (Camargo et al., 2019). However, a notable lack of research remains focused on the independent application of nanosilica fertilizers across different sugarcane genotypes, particularly in Takalar Regency, South Sulawesi. Therefore, this study was conducted to evaluate effects of nanosilica fertilization on sugarcane productivity and brix content, using correlation and path analysis to identify traits with direct contributions to these agronomic parameters.

Materials and Methods

The research was conducted from May 2023 to May 2024 at the Sugarcane Plantation Unit of Takalar Estate, PT Perkebunan Nusantara I Regional 8, located in Timbuseng Village, North Polombangkeng District, Takalar Regency, South Sulawesi. The study employed a Split-Plot Design (SPD), which incorporates two experimental factors. The main plot factor was the concentration of liquid nanosilica fertilizer (P), and the sub-plot factor was the sugarcane variety (V). Nanosilica fertilizer treatments consisted of four levels: control (P0), 1.0 ml·L-1 (P1), 3.5 ml·L-1 (P2), and 5.5 ml·L-1 (P3). Sugarcane varieties tested included Bululawang (V1), TLH02 (V2), and PS 862 (V3). A total of 12 treatment combinations were established, each replicated three times, resulting in 36 experimental plots. Each plot measured 7 × 9 meters, giving a total experimental area of 2,268 m².

Seedling preparation was carried out 9–10 months in advance at the seed propagation nursery. Seed canes were sourced from this nursery using a harvesting-to-planting ratio of 1:4. After approximately 9 months of growth, seed canes were harvested and transported to the experimental site. Seed canes were stripped of their leaves and cut into 3–4 node segments. These segments were then planted in furrows using a 50% overlapping method. The planting distance between rows (center to center) was maintained at 135 cm. Planting was immediately followed by the application of basal fertilizer (Nitrogen–phosphorus–potassium fertilizer). Planting distance from center to center is presented in Figure 1 below.

The application of liquid nanosilica fertilizer treatments was carried out every 14 days for a duration of three months, starting when the sugarcane was 30 days after planting (DAP). The nanosilica fertilizer was applied according to the following treatment dosages: P0: No application of liquid nanosilica fertilizer (Control); P1: 1.0 ml·L-1 of liquid nanosilica fertilizer, equivalent to 16 ml per tank; P2: 3.5 ml·L-1 of liquid nanosilica fertilizer, equivalent to 56 ml per tank; P3: 5.5 ml·L-1 of liquid nanosilica fertilizer, equivalent to 88 ml per tank.

The measurement procedures in this study were conducted through a one-time sampling approach for each parameter, mainly at the harvest time of sugarcanes, focusing on five representative samples per row across experimental plots. Plant height, stem diameter, number of internodes, and leaf angle were recorded prior to harvest using a measuring stick, digital caliper, and protractor, respectively. Leaf length and width were assessed post-harvest using a ruler on five randomly selected leaves per row. Shoot weight was determined by harvesting five canes per row, trimming non-structural parts, and weighing the biomass. Root measurements (weight and length) were taken from three sugarcane clumps per plot, excavated to a 40 cm depth, and cleaned prior to digital weighing. Tillers were counted per 3 sampled rows, normalized per meter to estimate stalk population. Leaf greenness was evaluated with the Leaf Color Chart (LCC) on the fifth leaf from the top, while chlorophyll content (CCI) was assessed using the CCM-200 Plus with 10 readings per leaf averaged across five leaves per row.

Brix content was measured using a refractometer on juice extracted from three stalk sections (top, middle, base) of five harvested canes per plot. Productivity was calculated by multiplying the average stalk weight by the estimated stalk population per hectare. The stalk population per hectare was estimated by multiplying the average number of tillers per meter by the total effective row length per hectare. The total row length was calculated based on row spacing and field dimensions, where the number of rows per hectare was determined by dividing 100 meters by the inter-row spacing of 1.35 meters, resulting in approximately 76 rows. Thus, the total row length per hectare was 100 meters × 76 rows = 7,600 meters. Consequently, the stalk population per hectare was obtained by multiplying the average tiller density (tillers per meter) by the total row length (7,600 m). Final productivity (tons·ha-1) was then computed by multiplying the average stalk weight (kg) by the estimated stalk population per hectare. All measurements followed standardized field protocols to ensure consistency and data reliability.

Sugarcane was harvested at 11–13 months after planting, when more than 80% of the plant population had developed inflorescences. The data collected from field observations were analyzed using a fixed-model analysis of variance (ANOVA) based on a Split-Plot Design, utilizing STAR 2.1 statistical software. Correlation and path analyses were initially performed using Microsoft Excel 365 to assess the relationships among measured traits. When significant differences among treatments were detected, further comparisons were conducted using the Least Significant Difference (LSD) test at the 0.05 significance level (LSD₀.₀₅). Polynomial regression analysis was applied using OriginPro 2022b to explore nonlinear trends and fit response curves. The correlation analysis was subsequently followed by path analysis to identify both direct and indirect effects of the observed variables. The path coefficients and structural relationships were visualized using Python version 3.10.

UAV image acquisition and processing

Image acquisition was conducted using a DJI Phantom 4 Pro UAV equipped with a multispectral camera. Flights were conducted over sugarcane fields to obtain surface reflectance data. A total of 409 TIFF-formatted images were captured during the flight and stored on an SD card, then transferred to a computer for further processing. The collected imagery was processed using Agisoft Metashape software to produce an orthomosaic. Processing stages included aligning photos, creating mesh models, textures, tile models, point clouds, and orthomosaics. The resulting orthomosaic reflects an accurate spatial view of the study area and is used as the basis for further analysis. The orthomosaic that has been created is then used to calculate the NDVI value. This was done by applying the Raster Calculator in ArcGIS, using the standard NDVI formula:

NIR (Near Infrared) and Red (red) are from the spectral bands captured by the UAV sensor. Orthomosaic NDVI was imported into ArcGIS software for image segmentation. This process divides the image into homogeneous spatial units based on the NDVI value. It then performs feature extraction to obtain spatial and statistical information from each segment. Vector data from the segmentation was converted to raster, then to point, and NDVI values were extracted using the Extract Multivalues to Points function for further statistical analysis.

Results and Discussion

The observation revealed differential responses in agronomic traits, field brix content, and productivity after applying liquid nanosilica fertilizer, as presented in Tables 1 and 2.

Tables 1 and 2 showed that significant differences were detected across several parameters, including plant height, stalk diameter, leaf width, leaf length, number of internodes, internode length, leaf angle, number of tillers, shoot weight, root weight, root length, leaf color, CCM index, brix content, and productivity. Parameters showing statistically significant differences were further analyzed using the Least Significant Difference (LSD) test at the 5% significance level (LSD₀.₀₅). Table 1, compared to the control (P0), the application of nanosilica fertilizer resulted in significant improvements across multiple growth traits in both sugarcane varieties, as validated by LSD₀.₀₅. The highest values for plant height, stalk diameter, leaf width, internode length, leaf angle, number of tillers, shoot weight, root weight, leaf color, and productivity were observed in the treatment combination of 5.5 ml·L-1 nanosilica fertilizer (P3) with the Bululawang variety (V1). The highest values for leaf length, number of internodes, and CCM index were recorded in the treatment combination of 5.5 ml·L-1 (P3) with the TLH02 variety (V2). The longest root length was observed under the treatment of 3.5 ml·L-1 (P2) combined with the TLH02 variety (V2). A polynomial contrast analysis was conducted to evaluate the effects of varying treatment concentrations on sugarcane performance. The polynomial contrast analysis was particularly applied to assess treatment effects on parameters such as plant height and stalk weight in Figure 1 those agronomic traits showing showed significant response patterns. The resulting second-degree polynomial models for each sugarcane variety captured distinct trends, as illustrated in Figure 1 below, highlighting the dose-dependent influence of liquid nanosilica fertilizer on physiological and yield parameters in sugarcane.

The resulting second-degree polynomial models for NDVI and yield productivity captured distinct trends, as illustrated in Figure 2. This highlights the dose-dependent influence of liquid nanosilica fertilizer on physiological and yield parameters in sugarcane.

Table 1: Post-hoc test results for parameters from plant height to shoot weight

Table 2: Post-hoc test results for parameters from root weight to productivity

Variety	Concentration	Root weight (g)	Root length (cm)	Leaf color	CCM (CCI Unit)	NDVI	NDGRI	VARI	Field Brix (%)	Yield Productivity (ton.ha-1)
Bululawang (V1)	Control (P0)	1,278ar	26.00aq	1.33ar	7.69ap	0.36aq	0.057cq	0,059br	18.85apq	64,21br
	1 ml.L-1 (P1)	1,625aqr	29.09bq	1.67br	8.63ap	0.37aq	0.027cr	0,053cs	16.77bq	73,40ar
	3.5 ml.L-1 (P2)	1,967aq	39.10ap	2.67aq	8.46ap	0.51ap	0.086bp	0,147bp	18.17bq	140,37aq
	5.5 ml.L-1 (P3)	2,501ap	28.50aq	3.67ap	8.69bp	0.49ap	0.023cs	0,075bq	20.63bp	178,06ap
TLH02	Control (P0)	950aq	28.07ar	1.33aq	8.53aq	0.34aq	0.059bq	0,079ar	19.42aq	76,67aq
	1 ml.L-1 (P1)	1,266abq	34.89aq	3.00ap	9.68apq	0.43ap	0.031bs	0,280ap	22.32ap	57,81bq
	3.5 ml.L-1 (P2)	897cq	41.00ap	3.00ap	8.51aq	0.42bp	0.045cr	0,086cq	23.33ap	87,29bpq
	5.5 ml.L-1 (P3)	2,341ap	25.60ar	3.33ap	10.52ap	0.45ap	0.060ap	0,058cs	22.57abp	102,45bp
PS 862	Control (P0)	461br	16.10bs	1.00ar	6.20bpq	0.26ar	0.107aq	0,044cs	18.50aq	45,92cq
	1 ml.L-1 (P1)	1,095bq	31.29abq	1.33br	5.28bq	0.43aq	0.111ap	0,067br	24.00ap	51,84bq
	3.5 ml.L-1 (P2)	1,433bq	37.52ap	3.00ap	7.10bp	0.47abpq	0.103ar	0,185ap	24.00ap	86,10bp
	5.5 ml.L-1 (P3)	2,166ap	24.62ar	2.00bq	6.48cp	0.51ap	0.032bs	0,140aq	24.00ap	93,98bp

Description: Values followed by different letters in rows (a, b, c) and columns (p, q, r, s) indicate significant differences based on the least significant difference (LSD) test at the 0.05 significance level, NDVI: Normalized difference vegetation index, NDGRI: Normalized difference green red index, VARI: Visible atmospherically resistant index.

The application of different treatment concentrations had a statistically significant effect on Plant Height (cm) and Stalk Weight (kg), as revealed by polynomial contrast analysis (p < 0.05). These effects were further explored through second-degree polynomial response curves fitted for each sugarcane variety. Both varieties demonstrated a positive quadratic trend with increasing concentration for Plant Height. Bululawang (V1) showed a marked increase from the control (P0) to the highest concentration (P3), with a peak height at P3. TLH02 exhibited a similar upward trend but with a more moderate curvature, indicating a consistent response across treatments. In the case of Stalk Weight, a similar concentration-dependent pattern was observed. The Bululawang (V1) polynomial response curve revealed a substantial increase in stalk biomass, particularly at the highest application rate. Meanwhile, TLH02 also displayed a positive trend, although the increase was less pronounced compared to Bululawang. These results suggest that Bululawang (V1) is more responsive to treatment concentration in terms of both height and biomass accumulation.

Table 2 showed that in the Bululawang (V1) variety, progressive increases in nanosilica fertilizer concentration led to notable improvements across several parameters when compared to the control (P0). Root weight increased from 1.278 g in the control to 1.625 g at 1 ml·L-1 (P1), 1.967 g at 3.5 ml·L-1 (P2), and peaked at 2.501 g under the 5.5 ml·Lv treatment (P3). Root length similarly extended from 26.00 cm in the control to 29.09 cm, 39.10 cm, and 28.50 cm under P1, P2, and P3, respectively. Leaf color intensity improved from 1.33 in the control to 1.67, 2.67, and 3.67, while NDVI values rose from 0.36 to 0.37, 0.51, and 0.49 across the same treatments. Most notably, yield productivity showed a substantial increase from 64.21 ton·ha-1 under control conditions to 73.40, 140.37, and 178.06 ton·ha-1 at P1, P2, and P3, respectively. These results highlight a strong, dose-responsive improvement in physiological and agronomic performance under nanosilica application in Bululawang.

Polynomial contrast analysis (Figure 2.) identified NDVI and yield productivity as the only parameters exhibiting statistically significant responses (p < 0.05) to increasing concentrations of nanosilica fertilizer. Second-degree polynomial regression curves were fitted to model the dose–response patterns across treatment levels (P0–P3) for each sugarcane variety. Both traits displayed clear nonlinear trends, with the highest values observed under the 5.5 ml·L-1 application (P3). In particular, Bululawang (V1) demonstrated a sharper increase in yield productivity compared to TLH02, suggesting a stronger varietal responsiveness to nanosilica supplementation. These results highlight the effectiveness of polynomial modeling in capturing the biological variability of key agronomic traits and reinforce the potential of nanosilica in enhancing sugarcane performance when applied at optimal concentrations.

Figure 2. also showed that for the NDVI trait, based on aerial observations using drones, the highest values were recorded in the treatment combinations of 3.5 ml·L-1 (P2) with the Bululawang variety (V1) and 5.5 ml·L-1 (P3) with the PS 862 variety (V3). Meanwhile, the highest values for the NDGRI trait were observed in the combinations of 1.0 ml·L-1 (P1) and the control treatment (P0) with the PS 862 variety (V3). The highest brix content was found across all nanosilica treatments (P1, P2, and P3), except for the control, in the PS 862 variety (V3). Lastly, the highest VARI (Visible Atmospherically Resistant Index) value was recorded in the combination of 1.0 ml·L-1 (P1) with the TLH02 variety (V2).

Table 3: Pearson correlation analysis of sugarcane productivity

Diagram (Figure 3) illustrates an integrated workflow for acquiring and analyzing UAV imagery to assess vegetation health in a sugarcane field using NDVI (Normalized Difference Vegetation Index). A DJI Phantom 4 Pro drone captures 409 TIF images, which are stored on an SD card and transferred to a computer for processing. These images are aligned and processed in Agisoft Metashape through steps including mesh construction, texture building, point cloud generation, and orthomosaic creation. The resulting orthomosaic is used to compute NDVI values, which indicate crop vigor and canopy health. The data are then imported into ArcGIS for image segmentation and feature extraction. Spatial analysis steps such as polygon-to-raster conversion, raster-to-point transformation, and value extraction are applied to associate NDVI data with specific field locations. This workflow supports precision agriculture by enabling high-resolution monitoring of crop conditions and facilitating data-driven decision-making. Additionally, Pearson correlation analysis of sugarcane productivity is presented in Table 3.

Table 3 in Pearson correlation revealed that out of 18 observed traits, 11 showed a significant and positive correlation with sugarcane productivity. These traits included stalk diameter (0.60), leaf width (0.63), leaf length (0.55), internode length (0.51), number of tillers (0.76), shoot weight (0.85), root weight (0.69), leaf color (0.66), and NDVI (0.54). This indicates that sugarcane productivity tends to increase in line with the enhancement of these nine traits. In contrast, two traits were significantly and negatively correlated with productivity: leaf angle (–0.50) and NGRDI (–0.34). Additionally, four traits showed a positive but non-significant correlation with productivity: brix content (0.02), number of internodes (0.11), root length (0.28), and CCM index (0.32). Two other traits, plant height (–0.10) and VARI (–0.01), were negatively correlated with productivity, but not significantly. Based on the correlation analysis (Table 3), traits with significant positive correlations were selected as inputs for path analysis. The path analysis results (Table 2) indicated that traits with a direct positive effect on sugarcane productivity per hectare included stalk diameter (0.057), leaf width (0.034), leaf length (0.071), and internode length (0.084), and leaf angle (0.006), number of tillers (0.406), shoot weight (0.613), and root weight (0.037). Conversely, traits with a direct negative effect on productivity included leaf color (–0.071), NDVI (–0.075), and NGRDI (–0.024). Among all traits, shoot weight exhibited the highest direct positive effect on sugarcane productivity per hectare (0.613), followed by number of tillers (0.406). These two traits can therefore be considered key selection criteria for identifying high-yielding sugarcane genotypes. The path analysis of the effect of several sugarcane varieties under different nanosilica concentrations is presented in Table 4 below.

Table 4: Path analysis of the effect of several sugarcane varieties under different nanosilica concentrations

The relationships among traits and their effects on vegetation indices NDVI and NGRDI are presented in Figure 4. The analysis revealed that tiller number (BT) and stalk height (JA) strongly influenced NDVI and NGRDI, either directly or through other traits. Meanwhile, traits such as leaf diameter (LD), plant diameter (DB), and internode number (PD) had moderate or indirect contributions. Despite these relationships, the model explained only a small portion of the variability in NDVI and NGRDI. This is shown by the high residual effect value (0.9997), indicating that many other factors, possibly physiological processes or environmental influences, affect the vegetation indices but were not included in the model. This also highlights the need to include broader physiological and environmental parameters in future models for a more comprehensive understanding of plant responses.

In addition to essential macro- and micronutrients, there are also beneficial nutrients, elements whose presence in the soil is not a primary requirement for plant growth. Nevertheless, their availability can provide additional advantages, including enhanced quality and improved plant health. According to Pilon-Smits et al. (2009), elements that promote growth and may be crucial for particular plant species, but are not universally required, are referred to as beneficial elements.

The result of this study demonstrated that sugarcane productivity was highest under the application of liquid nanosilica fertilizer at a concentration of 5.5 ml·L-1 (P3) in combination with the Bululawang variety (V1). High-dose nanosilica application significantly improved productivity, reaching 178.06 tons·ha-1. This can be attributed to nanosilica’s role in enhancing photosynthetic efficiency, thereby enabling the plant to produce more energy for growth. Enhanced sugarcane tolerance to abiotic stressors such as drought and salinity, combined with climate-resilient varieties and technological innovations, can help mitigate the negative effects of climate change and maintain yield and brix content (Srivastava and Kumar, 2020). Silica fertilization is essential due to its ability to strengthen plant structural integrity, improve resistance to environmental stress, and enhance nutrient uptake. Application of silica fertilizer at low concentrations in sugarcane cultivation has been shown to increase soluble silica in the soil (Camargo et al., 2021), improve yield, enhance drought tolerance (Camargo et al., 2020), and potentially reduce damage from stem borer pests (Camargo et al., 2014). Under extreme drought conditions, where rainfall is minimal, the application of granular fertilizers becomes less effective due to limited nutrient absorption and delayed distribution within the soil.

Increased sugarcane productivity was also associated with improvements in various agronomic traits. Based on findings, traits such as plant height, number of tillers, leaf width, shoot weight, stalk diameter, internode length, leaf angle, root weight, and leaf color all reached their highest values under the P3 treatment combined with the Bululawang variety (V1). This aligns with findings of Sajid et al. (2023), who reported that different sugarcane genotypes respond variably to environmental stress, and that there is a significant relationship between increased productivity and other agronomic traits, such as plant height, photosynthetic activity, and root development. The correlation between growth traits and sugarcane productivity was supported by several observed characters that showed a positive linear relationship with productivity across specific treatment combinations, including P3V1, P3V2, and P2V1. In contrast, certain traits, such as internode length and brix content, exhibited an inverse relationship with productivity. For example, the P1V3 treatment yielded a relatively high brix content of 24% despite a lower productivity of 51.84 tons·ha-1.

These findings indicate that the application of nanosilica fertilizer significantly influenced brix content. This suggests that balanced fertilization can enhance vegetative growth and total biomass production; however, it does not necessarily result in improved quality traits such as brix content. This discrepancy may be due to increased water content in plant tissues, which leads to a dilution of sugar concentration. This is consistent with the findings of Sajid et al. (2023), who reported that higher Brix content is often associated with lower productivity. Their study showed that at a productivity level of 47.34 ± 0.25 tons·ha-1, the brix content was 18.05 ± 0.40%, while at a lower productivity level of 24.73 ± 0.44 tons·ha-1, the brix content increased to 20.47 ± 0.35%. Similar results were reported by Puspitasari (2022), who found that sugarcane productivity at 3 and 6 months after planting increased in accordance with number of tillers. At 10.90 tillers, the productivity was 99.65 tons·ha-1, while at 14.13 tillers, the productivity rose to 135.03 tons·ha-1. Supporting this, Kadarwati (2020) observed that a tiller count of 13.13 resulted in 109.58 tons·ha-1, whereas a count of 13.59 produced 133.73 tons·ha-1.

The present study recorded the lowest productivity values under treatment V3 (PS 862 variety). Genotypic differences among plants influence their structure, silica storage capacity, and the biosilicification process (Nawaz, 2020). The synergistic effect between selected sugarcane varieties and liquid nanosilica fertilizer concentrations demonstrated significant improvements in biomass production and plant responses to environmental stressors such as drought, pests, and diseases. Enhanced quantitative and qualitative traits directly contributed to higher sugar yield and overall harvest potential. According to Nurcahya (2021), environmental factors contribute most significantly to yield variation, accounting for 66.7% of variability, while genotype–environment interactions account for 13.2%.

Zeng et al. (2020) reported that productivity may influence brix content; for instance, a yield of 155.8 tons·ha-1 resulted in a brix content of 20.08%, while a higher yield of 235.2 tons·ha-1 showed a slightly lower brix content of 19.65%. Although the brix content across treatments P1, P2, and P3 reached a consistent 24.00%, this does not conclusively indicate high sugar recovery, as brix content measures total soluble solids in cane juice, including sugars and non-sugar compounds such as acids, minerals, and organic substances. According to Elisanti (2023), brix content refers to the degree of total dissolved solids, mainly sucrose, but also including pectin, organic acids, and amino acids. This study employed both correlation and path analysis to investigate the relationships between fertilizer concentration, variety, and various plant traits. Correlation analysis enabled identification of the extent to which variation in one variable (e.g., fertilizer dose or variety) corresponds to variation in another. As Ahmed et al. (2019) noted, correlation analysis helps elucidate the strength and nature of relationships among yield components in sugarcane, a crop with complex yield architecture. It also aids in understanding interrelationships among agronomic traits.

Based on the result, traits most significantly and positively correlated with productivity were number of tillers, leaf width, shoot weight, stalk diameter, leaf length, leaf angle, root weight, and leaf color. This aligns with findings by Kumar et al. (2023), who identified traits such as number of tillers, number of stalks, individual stalk weight, stalk diameter, and plant height as positively correlated with yield, excluding brix content and leaf area index. These findings underscore the importance of selecting traits such as number of millable canes and shoot weight for breeding high-yielding sugarcane varieties. However, correlation analysis has limitations in dissecting the direct and indirect effects among multiple interacting variables. Therefore, path analysis was employed as a follow-up approach to partition correlations into direct and indirect effects (Widiayani et al., 2025). Shoot weight, which includes aboveground biomass such as stalks, leaves, and tops, plays a vital role in photosynthesis, light interception, and sugar production. According to Dlamini (2024), among shoot-related traits, shoot height showed a strong association with cane and sucrose yield. However, stalk population was also found to correlate more closely with yield than other traits. However, this study emphasized that shoot biomass (weight) played a more dominant role in explaining variation in sugarcane yield.

Maintaining a high number of tillers is also a key determinant of productivity. Almeida et al. (2024) demonstrated that denser plantings resulted in more tillers and stalks per unit area, directly influencing yield. Maximum productivity (77.69 tons·ha-1) and sugar yield (10.390 tons·ha-1) were achieved at 17–24 tillers per meter, while the lowest value (61.313 tons·ha-1and 7.24 tons·ha-1) was observed at 7–11 tillers per meter. In this study, number of tillers and shoot weight exhibited both strong positive correlations and direct effects on sugarcane productivity. Fadhilah et al. (2021) reported that silicon fertilization improves cell metabolism and mitigates physiological disturbances under drought stress. It enhances water absorption and transport, maintains tissue water balance, and supports leaf and stem turgidity, photosynthetic activity, and xylem function under high transpiration conditions. This aligns with our findings (Table 2), where the Bululawang variety treated with the highest nanosilica concentration (5.5 ml·L-1) achieved the highest productivity (178.06 tons·ha-1), strongly supported by high shoot biomass.

The application of liquid nanosilica at specific concentrations across three sugarcane varieties, particularly the local Bululawang and PS 862, as well as the relatively unexplored TLH02, revealed significant productivity differences. These findings underscore the untapped potential of local varieties to achieve enhanced productivity through the targeted application of nanosilica fertilizer. The application of liquid nanosilica at a concentration of 5.5 ml·L-1 (P3) was identified as the most effective treatment, demonstrating a specific and strong response in the Bululawang variety. This suggests that silica fertilization at this concentration may be recommended to enhance cultivation efficiency in regions with environmental conditions similar to those found in the sugarcane plantations of Takalar Regency. In addition to strengthening the physical structure of the plant, nanosilica is also known to activate the antioxidant defense system, which is crucial for mitigating oxidative stress caused by environmental challenges.

According to Rajput et al. (2021), both silica and nanosilica can alleviate the negative impact of various abiotic stresses on plant growth while enhancing soil fertility by influencing rhizosphere microbial activity and modifying morpho-physiological indices. The very weak correlation (0.02) between productivity and brix content observed in this study (Table 3) suggests that increased biomass volume does not necessarily correspond to increased sugar concentration. This pattern was especially evident under high nanosilica concentrations, where productivity increased but brix content remained relatively unchanged. Jalil et al. (2022) reported that higher Brix content is generally associated with increased sugar content, which is advantageous for sugar production. However, the relationship between brix content and overall yield is not always linear. For example, some high-brix content sugarcane genotypes exhibit good adaptability across different altitudes without necessarily achieving higher yields. Moreover, Perlo et al. (2020) highlighted that metabolic shifts affecting levels of brix content may also influence other agronomic characteristics, such as fiber content and total biomass, all of which contribute to overall crop performance. However, the effects of these metabolic changes have not been extensively examined at the varietal level, particularly in Bululawang, PS 862, and TLH02. Thus, the findings of this study are highly relevant for developing integrated management strategies that simultaneously address both growth performance and sugar quality.

Varietal response to nanosilica application also depends on the genetic capacity of each variety to absorb and translocate silica. According to Yang (2019), sugarcane genotypes such as ROC22 and GT32 have demonstrated high nitrogen use efficiency (NUE), enabling them to maintain yield performance even under reduced nitrogen fertilizer input. This may explain the varietal difference observed in this study, where Bululawang exhibited the most favorable response across nearly all parameters, including plant height (Table 1), shoot biomass (Table 1), and productivity (Table 2). Furthermore, silica application has enhanced key nutrients such as nitrogen and potassium uptake efficiency. A recent study by Lewin et al. (2024) indicated that silica-treated plants were more efficient in nutrient absorption under suboptimal soil conditions. This contributed to improved growth traits such as stalk diameter, leaf length, and tiller number (Swe et al., 2021). These findings are consistent with the results of the present study, particularly the results in Table 3 (stalk diameter) and Table 1 (number of tillers), which showed significant improvements under high nanosilica treatments. Therefore, the integration of nanosilica fertilization into sugarcane cultivation practices holds considerable promise for improving crop productivity and resilience under variable environmental conditions. The strong interaction between treatment and genotype further supports the need for targeted agronomic strategies to maximize both yield and quality in sugarcane production systems.

Conclusions and Recommendations

The result of this study demonstrates that the application of liquid nanosilica fertilizer significantly enhances sugarcane productivity, with distinct responses observed among different varieties. The 5.5 ml·L-1 concentration proved to be the most effective in improving nutrient use efficiency, brix content, and overall yield, particularly under agroecological conditions similar to those of the Takalar Sugarcane Plantation. The Bululawang variety exhibited the highest productivity at 178.06 tons·ha-1, while the PS 862 variety achieved productivity at 93.98 tons·ha-1. On the other hand, the highest brix content of 24% under the same treatment is shown from PS 862, while Bululawang variety only got 20.63% of brix content. However, an increase in vegetative biomass did not always correspond to an increase in brix content, indicating a shift in resource allocation toward growth rather than sugar accumulation. Based on these findings, the use of nanosilica fertilizer at a concentration of 5.5 ml·L-1 is recommended to be used for elite sugarcane varieties such as Bululawang, TLH02, and PS 862 with similar environmental conditions, particularly for high-yielding and high brix content. Future studies should investigate the long-term effects of nanosilica on soil properties, sugar recovery efficiency, and plant metabolic pathways. In addition, further exploration of genotype-specific responses to silica uptake may help optimize fertilization strategies and support the development of integrated cultivation systems that balance productivity with quality.

Acknowledgments

The author gratefully acknowledges the support provided by the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia through the Master’s Thesis Research Grant Program at Hasanuddin University, under grant number 02035/UN4.22/PT.01.03/2024. Special thanks are extended to the Department of Agronomy, Faculty of Agriculture, Hasanuddin University, for the academic guidance and facilities provided during the research period. The author also wishes to express sincere gratitude to all supervisors, laboratory staff, and field assistants for their valuable insights, technical support, and encouragement throughout this study.

Novelty Statement

This study presents a novel investigation into synergistic effects of liquid nanosilica fertilizer and varietal responses in sugarcane, particularly under field conditions in Takalar Regency, South Sulawesi. Unlike previous studies that focus primarily on macronutrient applications, this research highlights the role of nanosilica as a beneficial element in enhancing both productivity and stress resilience. It provides the preliminary information of site-specific fertilization recommendation, especially in the local variety under different nanosilica concentrations, revealing critical insights into biomass allocation, nutrient efficiency, and the decoupling of yield and sugar content. These findings contribute significantly to the development of site-specific fertilization strategies and offer a sustainable approach to improving sugarcane performance.

Generative AI and AI-assisted technology statement

The authors have declared that no generative AI or AI-assisted technologies were used to create this manuscript.

Conflict of interest

The authors declare that there is no conflict of interest amongst authors of the manuscript.

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