Influence of weed control area on “Pera Rio” orange treesAbstract
Background: Weed interference is one of the factors that reduces the productivity of citrus orchards, as it limits the availability of resources for plants.
Objective: Evaluate the influence of weed control area width on the development, productivity, and quality of the ‘Pera Rio’ orange tree.
Methods: The experiment was conducted in a commercial ‘Pera Rio’ orange orchard. A randomized block design was used, with 12 treatments and three replications, evaluating different weed control widths: manual crowning in radii of 0.5 m, 1 m, and according to the projection of the canopy; clean and infested strips of 1 m, 2 m, and 3 m; as well as two controls (with and without management). Vegetative development (height, crown diameter, branch length, and chlorophyll content) was assessed at 0, 12, and 18 months, and fruit yield and quality (mass, diameter, juice yield, soluble solids, acidity, and ratio) at two harvests. Weeds were controlled through weeding or the use of glyphosate herbicide and monitored after seven applications.
Results: The presence of weeds reduced vegetative growth (except height), productivity, and fruit quality of the ‘Pera Rio’ orange tree. The highest yields were observed in the control group, in the 1 m radius crowning, and in the 1 and 3 m clean strips. These same conditions also favored fruit with greater mass, diameter, and juice yield. Continuous coexistence with weeds resulted in yield losses of up to 60%.
Conclusions: The control area directly influences vegetative growth, productivity, fruit quality, and the industrial yield of ‘Pera Rio’ orange juice.
Chemical control; Citrus sinensis; Competition; Interference
1. Introduction
Citrus farming is a significant activity in the Brazilian agricultural sector, with the country ranking first in orange (Citrus sinensis (L.) Osbeck) production (US Department of Agriculture, 2025). In the 2024/2025 harvest, approximately 30% of global orange production and 70% of concentrated orange juice production would come from Brazil (US Department of Agriculture, 2025).
Within this scenario, the state of São Paulo, Brazil stands out as the primary citrus producer, accounting for more than 60% of the country’s orange plantations and approximately 77% of national production (IBGE, 2024).
Among citrus species, sweet oranges are very important, predominating in most producing countries and accounting for approximately two-thirds of cultivated areas, while the rest is occupied by other species (FAO, 2022). Among the most important commercial cultivars are the “Pera Rio”, “Natal”, “Hamlin”, and “Valencia” oranges (Lemos et al., 2013; Fundo de Defesa da Citricultura, 2025). The ‘Pera’ variety is the most widely grown in Brazil and is preferred by both producers and consumers due to its excellent fruit quality and agronomic advantages (Confederação da Agricultura e Pecuária do Brasil, 2025).
However, one of the main challenges in citriculture is the negative interference of weeds, which can cause significant losses (Fu et al., 2015; Azevedo et al., 2020). Interference between weeds and orange trees can result in reduced plant size, decreased fruit quantity and quality, and increased production costs (Azevedo et al., 2020; Martinelli et al., 2022). In sweet orange orchards, this competition can result in a loss of up to 28% in fruit quantity per plant (Gonçalves et al., 2018; Real et al., 2021).
Weed management is therefore essential, but controlling these plants represents one of the greatest difficulties for producers (Costa et al., 2018). As citrus is a perennial crop that coexists with these plants throughout the production cycle, there are few registered herbicides with high control efficacy compared to annual crops. Therefore, proper management requires a high level of investment to maximize productivity, production efficiency, and fruit quality (Martinelli et al., 2022).
Various cultural practices are employed to manage weeds in citrus groves, both within the rows and between the rows of plants. However, chemical control, using herbicides, has been the most used strategy by Brazilian citrus growers due to its high efficiency, ease of application, and favorable cost-benefit ratio (Martinelli et al., 2022; Amaral et al., 2023).
Despite this, defining the most efficient control range still requires more in-depth studies, as the extent of the area free from competition can directly influence plant development and production profitability. Similar spatial weed management strategies have been evaluated in other perennial fruit crops. In apple orchards, Atay et al. (2017) found that even low weed density near tree rows reduced fruit size and altered sugar content, in vineyards. These studies support the hypothesis that localized weed competition directly affects resource allocation in fruit trees, reinforcing the relevance of optimizing control zones in citrus production systems.
In this context, the aim of this study was to assess the influence of the control area and coexistence with weeds on the development, productivity, and fruit quality of the Citrus sinensis “Pera Rio” cultivar.
2. Material and Methods
2.1 Characterization of the area and experimental design
The experimental area is a commercial “Pera” orange orchard (clone Rio) grafted onto ‘Cravo’ lemon (Citrus limonia (L.) Osbeck), planted in 2011 with a spacing of 5.80 m x 2.25 m. Located at Fazenda Recreio in Monte Azul Paulista, SP, Brazil (20°59’21.5 “S, 48°40’52.9 “W), situated at an altitude of 543 meters. The climate is classified as Aw (Alvares et al., 2013), and the soil as a dystrophic Red-Yellow Argisol, sandy loam texture (Santos et al., 2011).
The experimental design used was a randomized block design (RBD) with 12 treatments and three blocks. Each experimental plot consisted of one planting row with six trees (78.3 m²), and the four central trees (52.2 m²) were considered the usable area. The study spanned two growing seasons (2014/2015 and 2015/2016).
2.2 Weed management treatments
The experimental treatments consisted of weed-control or weed-interference areas (strip or ring) located near the orange tree planting row. Twelve treatments were evaluated: T1: no weed control (entire plot “dirty”); T2: total weed-free plot (“clean”) ; T3: clean area equal to canopy projection; T4: dirty area equal to canopy projection, rest of plot manually hoed; T5: 0.5-m radius clean zone around trunk; T6: 1.0- m radius clean zone around trunk; T7: 1-m dirty strip (0.5 m each side of trunk); T8: 2-m dirty strip (1 m each side); T9: 3-m dirty strip (1.5 m each side); T10: 1-m clean strip (0.5 m each side); T11: 2-m clean strip (1 m each side); T12: 3-m clean strip (1.5 m each side).
Treatments T1–T6 used manual hoeing for weed control, whereas T7–T12 used chemical control with glyphosate. The spatial layout of all treatments is illustrated in Figure 1.
Layout of weed management treatments in the ‘Pera Rio’ orange orchard., T1–T6: weed control by hoeing (“clean”)., T7–T12: weed control with glyphosate. “Dirty” = no weed control; “Clean” = weed-free area as described
2.3 Identification of weed species and herbicide application
For the analysis of the weed community, surveys of the weed species present were conducted before and during the experiment (prior to herbicide applications) using an open quadrat with an internal area of 0.25 m² (0.50 × 0.50 m), which was randomly placed four times within each plot, resulting in a sampled area of 1 m² per plot. In each sample, the weeds were removed from the quadrat and botanically identified at the family, genus, and species levels.
Weeds were controlled by manual hoeing or herbicide when they reached approximately 15 cm in height, following the farm’s standard management threshold. A total of seven applications were carried out during the experimental period. The herbicide used was potassium glyphosate (Zapp Q.I 620), at a rate of 3 L ha-1 (1500 g a.e. glyphosate ha-1) following the farm standard. Applications were made with a pressurized knapsack sprayer using three XR 110.02 tips, spaced 50 cm apart. The sprayer operated at 40 psi and a spray volume of 200 L ha-1. All applications were performed under dry conditions, with air temperature between 28 and 32 °C, relative humidity > 50%, wind speed ≥ 4 km h-1.
2.4 Variables measured and statistical analysis
Prior to the establishment of the experiment, three branches per plant, each 10 cm in length, were tagged on the four central plants in each plot to allow subsequent determination of branch growth. The growth of these branches was measured using a measuring tape graduated in millimeters. On each of these branches, one leaf was selected and marked with a plastic tie to assess the relative total chlorophyll content in the orange trees using a chlorophyll meter (ClorofiLOG CFL1030, Falker).
Additionally, plant height (from the base of the trunk to the top of the plant) and canopy diameter (average of the measurements taken parallel and perpendicular to the planting row) were measured on the same plants. The evaluations were carried out at 0, 12, and 18 months after the installation of the experiment.
In January (15 months) and May (19 months) of 2016, the average fruit yield was evaluated and expressed as kg per plant. All fruits from the four central plants in each plot were harvested, and a composite sample of five fruits per plot was taken from the harvested fruits for physicochemical analyses.
The characteristics analyzed were fruit mass (g), longitudinal and transverse fruit diameters (cm), where the longitudinal diameter or height corresponds to the distance from the peduncle to the fruit apex, and the transverse diameter corresponds to the measurement at the equatorial position of the fruit. The percentage of juice (PJ), soluble solids content (SS), expressed in °Brix, and titratable acidity (TA) were also determined, and the SS/TA ratio was calculated. Based on the fruit analyses, industrial yield, expressed as soluble solids per box, was calculated using the following equation:
Where SSbx: soluble solids per box, expressed in kg SS bx-1; SS: soluble solids content of the fruit, expressed in °Brix; and JP = percentage of juice in the fruit, expressed in %.
Thirty days after each application, weed control ratings were assigned using a visual scale from 0 to 100%, where 0 represented no control and 100% represented complete control of the weed species (ALAM, 1974).The data obtained were subjected to analysis of variance (ANOVA) to assess the effects of weed interference on crop development and productivity. Prior to ANOVA, the assumptions of normality of residuals and homogeneity of variances were evaluated using the Shapiro–Wilk and Levene tests (p ≤ 0.05), respectively. When significant treatment effects were detected, means were compared using Duncan’s multiple range test at a 10% probability level (p ≤ 0.1). All statistical analyses were performed using the SASM-Agri statistical software (Canteri et al., 2001).
3. Results and Discussion
3.1 Weed species identified in the experiment
Twenty-four weed species were identified in the weed community, belonging to nine botanical families. Of these families, two were monocotyledons and seven were eudicotyledons. Within the monocotyledons, Poaceae was notable for its nine species, while among the eudicotyledons, Asteraceae was notable with five species (Table 1).
The weed community identified in the experimental area included species widely reported in citrus orchards and known for their competitive capacity. Among the Poaceae, Digitaria insularis, Urochloa decumbens, and Cynodon dactylon were recorded. These species present rapid growth and high biomass production, increasing competition for water and nutrients in the tree row. In Brazilian citrus systems, D. insularis and U. decumbens have been associated with reductions in vegetative growth and yield (Gonçalves et al., 2018; Martinelli et al., 2022).
Among the eudicotyledons, Bidens pilosa, Conyza canadensis, Commelina benghalensis, and Solanum americanum were also identified, all commonly reported in Brazilian citrus orchards (Gonçalves et al., 2018; Real et al., 2021; Martinelli et al., 2022). Notably, C. benghalensis has been shown to accumulate higher concentrations of N, P, and K in its biomass than Citrus sinensis leaves, indicating strong nutrient competition (Sanches et al., 2024), which reinforces the role of resource limitation as a mechanism of interference in this study.
Additionally, Amaranthus viridis, Conyza canadensis, and Digitaria insularis, which were present in the experimental area, have documented cases of glyphosate resistance in Brazil (Alcántara-de la Cruz et al., 2020; Heap, 2024). Although resistance was not evaluated in this study, the occurrence of these species reinforces the importance of maintaining adequate weed-free areas and adopting diversified management strategies in citrus orchards.
3.2 Weed control
Weed control evolved over consecutive applications. During the first application, control ranged from fair to good, with T9 (3 m strip without control) providing sufficient results. The second application saw efficiency improve to sufficient or good, and by the third application, scores reached good to very good (Table 2).
With subsequent applications, weed control continued to improve. The fourth application ranged from very good to excellent, with treatments T7 (1 m strip with no control) and T11 (strip with control) achieving over 80% control, while others exceeded 90%. In the fifth application, T7 and T8 (2 m strip with no control) showed 73% and 75% efficiency, respectively, and T9 (3 m strip with no control) once again demonstrated excellent control (Table 3). The sixth application yielded no statistically significant difference, with results ranging from good to very good. In the final application, T9 achieved 93% control, distinguishing itself from the other treatments (Table 3).
Percentage of weed control according to the ALAM scale (1974) in the second year (2015/2016)
The high control rate in T9 can be attributed to the treated strip, which covered 1.5 m on either side of the plant, as well as a 0.5 m strip between the rows of the crop, an area with a higher occurrence of spreading liverseed grass (U. decumbens), a weed that is easy to control with glyphosate. In the other treatments, weed control was carried out close to the planting line or in smaller strips than in treatment T9. As a result, it was common for plants such as Canadian horseweed (C. canadensis), sourgrass (D. insularis), straggler daisy (Synedrellopsis grisebachii), and jio (C. benghalensis) to escape, species that are more difficult to control due to their resistance or tolerance to glyphosate (Heap, 2024).
3.3 Biometric characteristics of the plants
About the biometric characteristics of the ‘Pera Rio’ orange plants, statistical differences were observed between the treatments 12 months after the experiment began (Table 4). At 12 months, only parallel diameter and chlorophyll content showed statistical differences (P ≤ 0.10). The 1-m dirty strip (T7) had the numerically highest parallel diameter, but it did not differ significantly from T2, T5, T6, T8–T10. The clean and dirty crown treatments (T3 and T4) showed the smallest value and differed significantly from T7.
Biometric characteristics of ‘Pera Rio’ orange trees evaluated 12 months after experiment establishment
Chlorophyll content was highest in T10 (1-m clean strip), but this treatment is statistical with all others except T8, indicating limited practical differentiation (Table 4). In the last evaluation, performed 18 months after the experiment began, only plant height remained unaffected (Table 5).
Biometric characteristics of ‘Pera Rio’ orange trees evaluated 18 months after experiment establishment
For parallel diameter, treatment 4 (crowning in the swale according to the canopy projection) continued to provide the smallest diameter. Treatments 5 (0.5 m radius swale), 6 (1 m radius swale), 7 (1 m swale strip), and 10 (1 m swale strip) resulted in higher parallel diameter values. For the perpendicular diameter, the crowning in the dirt also resulted in a smaller diameter. In contrast, treatments 2 (control in the dirt), 5 (1 m radius in the dirt), 7 (1 m strip in the dirt), and 10 (1 m strip in the dirt) produced the highest perpendicular diameter values.
These results indicate that weed interference near the trunk and within the canopy projection compromises orange tree development, even when plant height remains unaffected. Previous studies in citrus have already shown that weed interference reduces agronomic performance due to competition for water, light and nutrients (Gonçalves et al., 2018; Azevedo et al., 2020).
Certain weed species (C. benghalensis, D. insularis) also show high capacity to absorb and accumulate N and P, often reaching leaf concentrations higher than those of C. sinensis, which increases their competitive potential and can limit yield when they are not controlled during the critical period of interference (Sanches et al., 2024).
In addition, interference is not only competitive but also chemical. Allelochemicals such as ferulic acid and coumarin, reported in different weed species, can modify citrus metabolism and affect root growth and nutrient use efficiency (Chon et al., 2006). In this experiment, species such as Urochloa decumbens (Souza et al., 2006) and Eleusine indica (Dang et al., 2025) already cited as competitive or allelopathic were present in the orchard, reinforcing the need for strict management in the tree row.
For growers seeking to optimize branch length, a treatment with a 3 m clear strip (T12) yields the longest branches. This suggests that a clear radius of this size is most beneficial. Conversely, the 1 m clear radius (T6) results in shorter branches. For leaf chlorophyll, the 1 m clear radius with weeding (T6) produces the lowest content. The 3 m clear radius with herbicide application (T12) yields the highest chlorophyll levels, with T2 (clean control) expressing intermediate values for both branch length and chlorophyll content. These findings suggest that a wider, chemically managed strip in the tree row can enhance both canopy development and leaf chlorophyll status compared with a narrow, weed-free radius maintained only by hoeing.
The response observed in T12 is consistent with previous evidence on glyphosate drift in young citrus. Foresti et al. (2015), working with ‘Hamlin’ orange seedlings, reported higher leaf chlorophyll 90 days after glyphosate application than in non-treated plants, which is analogous to the present finding that trees exposed to glyphosate in the 3-m strip (T12) had higher chlorophyll content than those kept clean by weeding (T6) 18 months after treatment onset.
3.4 Productivity and quality of fruit
Regarding productivity, two harvests were carried out in the experimental area in 2016 (Table 6). On both occasions, treatments T2 (clean control), T6 (1 m clean radius), T10 (1 m clean strip), and T12 (3 m clean strip) provided the highest yields. On the other hand, the treatments with the lowest yields were T1 (control in the swale) and T4 (crowning in the swale, according to the projection of the canopy) (Table 6).
The results indicate that, when comparing a completely clean orchard with one completely overgrown with weeds, there was a 58% reduction in production in the first harvest and 60% in the second. In addition, the absence of simple plant crowding reduced productivity by 46% in the first harvest and 27% in the second, resulting in an average loss of 38% across both harvests. These yield losses are in line with reports that weed interference in citrus can cause yield reductions ranging from about one-third up to more than half, depending on infestation level and management strategy (Martinelli et al., 2017; Gonçalves et al., 2018; Real et al., 2021).
In ‘Natal’ orange, integrated programs based on glyphosate in the row, with or without cover crops such as jack bean, maintained yields comparable to the grower’s standard, while treatments that allowed weeds under the canopy produced yields similar to unweeded control, emphasizing the critical role of the area beneath the canopy projection (Real et al., 2021).
Regarding the qualitative characteristics of the fruits, only titratable acidity did not show a statistically significant difference between treatments, while the other parameters differed from each other (Table 7). Treatments 10 and 3 showed the best results for fruit mass, as well as longitudinal and transverse diameters. In contrast, the control in the dirty area and treatment 4 resulted in lower values for these characteristics.
Coexistence with weeds led to a 15% reduction in fruit mass compared to the clean control, and a 26% reduction when plants were not crowned. This reduction in fruit mass demonstrates that weeds compete strongly for resources. Moreover, other studies have also found that weeds reduce fruit size and yield in citrus (Gonçalves et al., 2018). If weeds persist for a long time, especially by affecting the transport of carbohydrates to reproductive parts, fruit filling and total yield are reduced (Soares et al., 2021).
For industrial yield, treatment T5 (0.5-m clean radius) showed the highest numerical value but did not differ significantly from T3 or T10 (Table 7). The highest juice yield occurred in the clean strip 1 m treatment (63.1%), which differed from the lowest values observed in the dirty crown (45.1%) and dirty strip 1 m (48.0%) treatments. The clean crown, clean controls and clean radius treatments (treatments 2, 3, 5, 6, 8, 9, 12) formed an intermediate group, generally not differing from each other but tending to show higher yields than the dirty crown and dirty strip 1 m (Table 8).
Soluble solids (SS) content varied between 7.9 and 10.7 °Brix, with dirty crown and clan strip 2 m presenting the highest °Brix, while clean radius 2 m and clean control showed the lowest values, indicating a dilution or maturity effect (Table 8).
In the second harvest, the treatments significantly affected fruit mass, fruit size and industrial yield. Fruit mass varied from 0.857 kg in the dirty crown to 1.010 kg in the clean strip 2 m treatment, with the latter presenting the heaviest fruits, while clean crown and dirty strip 1 m also produced fruits close to 1.0 kg, differing from the lighter fruits of dirty crown and clean control.
Transverse diameter ranged from 32.5 to 35.4 cm, with the smallest values in dirty crown and clean control and the largest in clean strip 2 m and in treatments with strip and crown cleaning, indicating that these managements favored greater fruit thickness. Longitudinal diameter varied between 33.0 and 36.5 cm, with clean crown and strip treatments (dirty and clean at different distances) standing out with the highest values and differing from dirty crown and some radius and strip treatments with lower diameters, demonstrating that keeping the crown and inter-row strips clean favored more elongated fruits (Table 9).
Industrial yield (kg of SS per box) also differed among treatments, ranging from 1.6 to 2.0 kg SS/box. The clean strip 2 m treatment provided the greatest industrial yield (2.0 kg SS/box), differing from the lowest yields (1.6 kg SS/box) obtained in clean control, clean radius 1 m and dirty strip 3 m, while the remaining treatments showed intermediate values (1.8–1.9 kg SS/box) that did not differ from the best treatment but were superior to the lowest group (Table 9).
The treatments significantly affected juice yield and SS content but did not change titratable acidity or the SS/titratable acidity ratio (Table 10). Juice yield ranged from 41.8 to 53.7%, with the highest values observed in clean strip 2 m (T11), clean crown (T3), clean radius 2 m (T6) and dirty strip 1 m (T7) (around 52–54%), which differed from the lowest yields recorded in clean control, clean radius 1 m (T5) and dirty strip 3 m (T12) (around 42%).
SS varied between 8.6 and 10.2 °Brix, with dirty crown showing the highest °Brix (10.2 °Brix), differing from the group with lower values (8.6–9.0 °Brix) that included clean crown, clean radius 2 m, dirty strip 1 m, clean strip 1 m and clean strip 2 m, while the remaining treatments presented intermediate SS contents. The analysis of SS in the two harvests showed an inverse relationship between productivity and sugar concentration.
This pattern is consistent with previous studies, which have shown that fruit under stress or reduced size often concentrates sugars, resulting in higher Brix values, a phenomenon already reported in the literature as a dilution effect. In treatments where productivity was higher, there was a lower concentration of SS, possibly due to the greater number of fruits acting as sinks for assimilates. In treatments where productivity was lower, the fruits had a higher SS content, reflecting the lower demand for assimilates (Jordan, Russell, 1981; Atay et al., 2017).
The results indicate that fruit quality traits exhibit variable responses to weed management practices. Titratable acidity remained consistent across all treatments, suggesting minimal sensitivity to competitive stress. Conversely, fruit mass, juice yield, SS content, and industrial yield were significantly affected by weed presence or absence.
The yield and quality responses to tree-row weed control observed here fit within the broader concept of orchard-floor management, in which vegetation in the row and alley influences water status, nutrient uptake, tree vigor and fruit quality. Studies in apple have shown that strong weed competition in the tree row can reduce SS and alter nutrient ratios in the fruit, confirming that insufficient weed suppression is a limiting factor for fruit quality in high-density orchards (Atay et al., 2017).
In citrus, integrated weed-management strategies that combine physical methods with residual and post-emergence herbicides have achieved sustained weed suppression and reduced weed seed banks, while supporting citrus growth and yield (Martinelli et al., 2017; Martinelli et al., 2022; Azevedo et al., 2020)
These findings indicate that maintaining a vegetation free area beneath the canopy projection and along an adequate strip or radius surrounding the tree row, physical and chemical weed management, is important for minimizing competition and supporting optimal yield, fruit size, and key industrial quality parameters in orange orchards. Defining an appropriate weed control zone is therefore a critical management decision, as insufficient control may lead to measurable reductions in both productivity and fruit quality, as observed across the evaluated treatment ranges.
4. Conclusions
The control area directly influences vegetative growth, productivity, fruit quality, and industrial juice yield of the “Pera Rio” orange tree.
The crowning of the orange tree, in radii of 0.5 and 1 m, and the control of weeds by treating a 1m strip, resulted in greater development of the orange trees in relation to their diameter. The control in the clean area, the crown within a radius of 1 m, and the control strips starting at 1 m resulted in higher productivity of the orange trees.
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Data availability
All data supporting the findings of this study are available in main text of this article.
Edited by
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Editor in Chief:
Carol Ann Mallory-Smith
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Associate Editor:
Marcos Yanniccari
All data supporting the findings of this study are available in main text of this article.
