Clonal Selection for Citrus Production: Evaluation of ‘Pera’ Sweet Orange Selections for Fresh Fruit and Juice Processing Markets

Abstract

‘Pera’ sweet orange is a key variety for the Brazilian citrus industry, but orchards rely on a limited number of clonal selections, which restricts adaptability and productivity across diverse environments. This study assessed the agronomic performance of 13 ‘Pera’ selections grafted on Rangpur lime, cultivated under rainfed conditions in subtropical Brazil. From 2002 to 2010, trees were assessed for vegetative growth, cumulative yield, alternate bearing, and fruit quality. Market-specific performance indices were calculated to determine suitability for fresh fruit or juice processing. Substantial genotypic variation was observed across traits, particularly during early orchard stage. Selections such as ‘Morretes’, ‘Seleção 11’, ‘Seleção 27’, ‘Seleção 37’, and ‘IPR 153’ demonstrated high cumulative yield, stable productivity, and favorable canopy traits, supporting their use in both conventional and high-density systems. ‘IPR 153’ combined compact growth with high yield efficiency and excellent fruit quality, while ‘Morretes’ had the highest juice content and broad market adaptability. In contrast, ‘IPR 159’ showed low vigor and yield under rainfed conditions. The results emphasize the value of regionally targeted clonal selection to improve orchard performance and market alignment. The identification of dual-purpose genotypes offers a pathway to diversify citrus production and improve profitability under subtropical growing conditions.

1. Introduction

Sweet orange (Citrus × sinensis [L.] Osb.) holds substantial economic and agricultural relevance in Brazil, particularly in the tropical and humid subtropical regions, including the states of São Paulo, Minas Gerais, and Paraná. These regions offer optimal edaphoclimatic conditions for citrus production and represent the largest sweet orange-growing area in the world [1,2,3]. São Paulo State and the West–Southwest region of the Minas Gerais State, referred to as the São Paulo citrus belt, account for approximately 70% of the total planted area and over 80% of Brazil’s sweet orange production [3,4], serving as a key supplier to both domestic and international markets.

Among sweet orange varieties, the mid-season ‘Pera’ has exceptional commercial significance in Brazil, currently accounting for approximately 36% of the citrus area in the São Paulo citrus belt [5]. In previous decades, however, its share was even higher, surpassing 50% [6]. This variety is extensively cultivated in Brazil for both fresh fruit market and juice processing industry, primarily due to its consistent blooming, high juice quality, seedlessness, rich flavor profile, and strong consumer acceptance [7,8,9,10]. Its broad adaptability to diverse environmental conditions and strong compatibility with commonly used rootstocks, despite incompatibility observed in certain clones with Volkamer lemon (C. volkameriana V. Ten. & Pasq.), Trifoliate orange [Poncirus trifoliata (L.) Raf.], and some of its hybrids such as Swingle citrumelo (C. paradisi Macfad. ‘Duncan’ × P. trifoliata) [11,12], have further contributed to its widespread adoption across the Brazilian citrus production systems [10,13,14].

Fruits from ‘Pera’ are typically medium-sized, slightly oval, and smooth-skinned, while trees are vigorous, upright, and densely foliated, morphologically similar to ‘Valencia’ [7,8,15]. Although the fruit quality of ‘Pera’, particularly in terms of juice color and total soluble solids (TSS), is generally considered intermediate (superior to ‘Hamlin’ but inferior to ‘Valencia’) [7], the variety exhibits a valuable trait under subtropical Brazilian conditions: multiple blooming cycles, with three to four blooms per year [15]. While this trait can limit harvest scheduling for juice processing, it offers valuable flexibility for staggered harvesting, which is particularly advantageous for the fresh fruit market. The harvest period of ‘Pera’ in Brazil extends from early July to late November, effectively bridging the gap between early- and late-season varieties [15]. In subtropical Brazil, the sugar-to-acidity ratio in ‘Pera’ orange progressively increases throughout the season, reaching, on average, three times the value observed in April by November, with TSS typically peaking between September and October [16].

Nonetheless, ‘Pera’ is the most susceptible commercial sweet orange variety to citrus tristeza, a disease caused by the Citrus tristeza virus (CTV) [10]. In trees grafted on CTV-tolerant rootstocks, the typical symptom appears as stem pitting along the trunk and branches, leading to a progressive decline in tree vigor and productivity [17]. The pre-immunized selection ‘Pera IAC’, developed through inoculation with attenuated CTV isolates, demonstrated cross-protection against more severe strains with good performance in early field trials [18,19] and has since been widely adopted in commercial orchards in Brazil [20]. ‘Pera IAC’ trees ranked among the most productive in a comparative trial of ‘Pera’ clones conducted in Paraná State [21]. In addition to ‘Pera IAC’, several other clones carrying attenuated strains of CTV have been identified and evaluated for commercial use. Among these, ‘Bianchi’, ‘EEL’, ‘Vimusa’, ‘Ipiguá’, and ‘IAC 2000’ have shown high yield potential and desirable fruit quality [10]. Currently, ‘IAC’ and ‘Olímpia’ are the most widely planted clones in commercial orchards, particularly across the São Paulo citrus belt [20], while ‘IPR 159’ is prominent in Paraná [22,23] and ‘D-6’ in Northeast Brazil [24,25]. Overall, these clones have contributed to sustaining ‘Pera’ cultivation in Brazil [15].

However, despite its widespread use, traditional ‘Pera’ clones are known to exhibit considerable variability in terms of vegetative growth, yield performance, fruit quality, and alternate bearing tendencies [20,21,26], particularly because ‘Pera’ has more vegetative flushes throughout the year in comparison with other commercial varieties [15,27]. These variabilities can compromise orchard efficiency, particularly in modern citrus production systems that aim to optimize planting density, mechanization, and resource use efficiency [28]. Consequently, efforts have been renewed to identify and characterize elite clones of ‘Pera’ with attenuated and protective CTV isolates that combine early and sustained productivity with desirable horticultural traits and better adaptation to local conditions [20].

Field evaluation of clonal selections is a critical step in advancing the sustainability and productivity of citrus orchards. While fresh fruit breeding programs typically emphasize traits such as productivity, flavor, aroma, juiciness, and resistance to biotic, abiotic, and postharvest stresses, breeding efforts focused on juice processing prioritize yield potential, juice quality, and an extended harvest window [29]. In addition to these fruit-related traits, vegetative growth dynamics and canopy architecture are key determinants of orchard design and management practices, influencing decisions related to pruning, pest and disease control, irrigation, and overall input efficiency. Moreover, cumulative yield and the alternate bearing index (ABI) serve as essential metrics for assessing the long-term agronomic performance and economic viability of clonal selections [30,31].

In this context, the present study aimed to assess the long-term horticultural performance of 13 clonal selections of ‘Pera’ sweet orange, all preimmunized with a mild protective isolate of CTV. The selections included both widely cultivated commercial varieties and new genotypes developed by the Instituto de Desenvolvimento Rural do Paraná—IAPAR/Emater (IDR-Paraná) breeding program. All clones were grafted onto Rangpur lime (C. × limonia Osb.) and grown under rainfed subtropical conditions in Brazil. Over nine years (2002–2010), selections were evaluated for early vigor, canopy development, alternate bearing, cumulative yield, and fruit quality. In addition, market-oriented performance indices were developed to classify genotypes based on their suitability for fresh fruit or juice processing. The findings of this study aim to support the strategic selection and recommendation of elite ‘Pera’ clones for the renewal and diversification of citrus orchards in subtropical Brazil and similar regions, with an emphasis on improving productivity, canopy uniformity, fruit marketability, and long-term orchard performance.

2. Materials and Methods

2.1. Experimental Location

The experiment was carried out in the Paranavaí Research Station on the IDR-Paraná, in the municipality of Paranavaí, Paraná State, Brazil (23°05′43″ S; 52°26′35″ W; 465 m a.s.l.), on a Typic Hapludox with sandy soil texture, characterized by 83% sand, 16% clay, and 1% silt in the 0–60 cm soil layer. According to the Köppen–Geiger classification, the local climate is humid subtropical (Cfa), with an average annual maximum temperature of 29.0 °C, a minimum of 18.2 °C, and a mean annual precipitation of 1430 mm for the 2003–2010 study period. Daily weather data, including temperature and rainfall, were recorded throughout the experimental period by a meteorological station located near the experimental area. The climatic water balance was calculated following the Thornthwaite and Mather [32] methodology, using an assumed soil water holding capacity of 100 mm (Figure 1).

2.2. Plant Material and Orchard Establishment

The clonal selections of ‘Pera’ sweet orange evaluated in this study were preselected from the Active Germplasm Bank of Citrus (AGB–Citrus) maintained by the Instituto de Desenvolvimento Rural do Paraná—IAPAR/Emater (IDR-Paraná), located in Londrina, Paraná State, Brazil. Rootstocks were propagated from seeds, with nucellar seedlings selected for grafting following standard commercial nursery protocols.

A total of 13 ‘Pera’ clones were assessed: ‘IPR 153’, ‘IPR 159’, and ‘IPR 158’—corresponding to ‘Bianchi’, ‘Vacinada 4’, and ‘Vacinada 3’, respectively [33]—originally obtained from the Universidade Estadual Paulista (UNESP), Botucatu, São Paulo State, Brazil. Additional selections included ‘D-6’ (accession I-111) from EMBRAPA Mandioca e Fruticultura (Cruz das Almas, Bahia State, Brazil), ‘Gullo’ (I-87) and ‘Vimusa’ (I-66) from the Instituto Agronômico de Campinas (IAC), Centro de Citricultura Sylvio Moreira (Cordeirópolis, São Paulo State, Brazil), and ‘Morretes’ (I-34) from the Unidade de Pesquisa de Morretes, IDR-Paraná (Morretes, Paraná State, Brazil). The remaining six clones, including ‘Seleção 11’ (I-405), ‘Seleção 12’ (I-403), ‘Seleção 14’ (I-406), ‘Seleção 15’ (I-407), ‘Seleção 27’ (I-404), and ‘Seleção 37’ (I-408), were obtained by the citrus breeding program of the IDR-Paraná (Londrina, Paraná State, Brazil).

All scions were grafted onto Rangpur lime rootstock. Grafted nursery trees were grown under screen house conditions and transplanted to the field in January 2001, at a spacing of 7.0 m × 6.0 m (238 trees·ha⁻¹). The experimental layout followed a randomized complete block design, with 13 treatments (‘Pera’ clones) and five replicates.

2.3. Orchard Management

Orchard management followed standard practices commonly adopted in Brazilian citrus production [34], ensuring proper nutrition, weed control, pest management, and disease control. Fertilization was carried out two times per year, from August to March, and was based on soil chemical analyses to supply adequate levels of nitrogen (N), potassium (K), phosphorus (P), boron (B), and zinc (Zn). Nutrient rates were adjusted to tree age and developmental stage. Weed management involved regular mowing between tree rows, with an ecological mower. Within-row weed control was performed through herbicide application as needed based on commercial practices.

Control measures targeted major diseases such as citrus canker (caused by Xanthomonas citri subsp. citri) and leprosis (caused by Citrus leprosis virus cytoplasmic type—CiLV-C, transmitted by the mite vector Brevipalpus yothersi Baker). Preventive chemical sprays were applied throughout the growing season, particularly from fruit set to fruit maturation. Insect and mite management focused on controlling the citrus leafminer (Phyllocnistis citrella Stainton), citrus fruit borer (Gymnandrosoma aurantiana Lima), fruit flies (mainly Anastrepha fraterculus Wiedemann and Ceratitis capitata Wiedemann), citrus rust mite (Phyllocoptruta oleivora Ashmead), and the leprosis mite vector. Management practices were adjusted seasonally, with more frequent interventions during shoot flush growth and fruit development to minimize pest pressure and disease incidence. Throughout the experimental period, trees were grown under rainfed conditions without supplemental irrigation, and no pruning or thinning practices were performed.

2.4. Vegetative Growth

Tree growth assessments were carried out annually in August–September from 2002 through 2010. To compare the vegetative performance of the selections studied at different orchard development stages, data from the 2006 and 2010 growing seasons, representing trees at five and nine years of age, respectively, were averaged and analyzed. In addition, annual vegetative data were used to model the growth dynamics of the different clones over time using second-degree polynomial regression. Tree height and canopy diameter were recorded annually using a graduated pole to calculate the canopy volume, which was estimated using the equation described by Mendel [35]:

$$\mathrm{CV}={\displaystyle \frac{2}{3}}\times \mathsf{\pi } \times { \mathrm{CR}}^{2 }\times \mathrm{TH},$$

where CV is the canopy volume (m³); π corresponds to 3.14; CR is the canopy radius (m); and TH is the tree height (m).

Trunk circumference was measured using a measuring tape 10 cm above and below the graft union, then converted to trunk diameters. The trunk index was determined by the ratio of the scion trunk diameter to the rootstock trunk diameter, providing an estimate of graft compatibility and vigor balance.

2.5. Fruit Yield

Annual fruit yield was assessed by weighing the total fruit yield per tree at harvest, which occurred each August from 2003 to 2010. Yield performance of the different selections was evaluated across two distinct orchard phases: the establishment phase (2003–2006; trees aged two to five years) and the full production phase (2007–2010; trees aged six to nine years). The cumulative yield for each phase was determined by summing the annual yields within that period. Yield efficiency was determined based on the total fruit yield per tree (kg·tree⁻¹) to canopy volume (m³), using canopy volume data from 2006 and 2010 to represent the establishment and full production phases of the orchard, respectively. Results were expressed in kg·m⁻³. The alternate bearing index (ABI) was calculated following the method proposed by Pearce and Doberšek-Urbanc [36]:

$$\mathrm{ABI} ={\displaystyle \frac{1}{\mathrm{n}-1}} \times \left\{{\displaystyle \frac{\left|{\mathrm{a}}_2-{\mathrm{a}}_1\right|}{{\mathrm{a}}_2+{\mathrm{a}}_1}}+{\displaystyle \frac{\left|{\mathrm{a}}_3-{\mathrm{a}}_2\right|}{{\mathrm{a}}_3+{\mathrm{a}}_2}}+\dots +{\displaystyle \frac{\left|{\mathrm{a}}_{\mathrm{n}}-{\mathrm{a}}_{\mathrm{n}-1}\right|}{{\mathrm{a}}_{\mathrm{n}}+{\mathrm{a}}_{\mathrm{n}-1}}}\right\},$$

where ABI is the alternate bearing index; n is the number of years; and a₁, a₂,…, a_n represent the annual yields for each year. This index quantifies year-to-year yield fluctuations, with values closer to zero indicating higher stability.

2.6. Fruit Quality

Fruit quality assessments were performed using 10 fruits per plot (50 fruits per assessment), randomly sampled from the canopy within each experimental unit. Fruits were collected from the middle canopy (1–2 m above ground) during June and July for four consecutive years (2007–2010). For comparative purposes, data were averaged over the four harvest seasons, considering both sampling months. Morphological traits, including fruit length and diameter, were evaluated using a digital caliper (ABS, Mitutoyo, Kawasaki, Japan), and fruit weight was recorded. The fruit shape index was determined as the ratio between fruit length and diameter. Seed count was recorded per sample. Juice was extracted using an industrial extractor (Croydon, Duque de Caxias, Rio de Janeiro, Brazil), and juice content (JC) was expressed as the percentage of juice weight relative to fruit weight:

$$\mathrm{JC}={\displaystyle \frac{\mathrm{JW}}{\mathrm{FW}}} \times 100,$$

where JC is juice content (%); JW is juice weight (g); and FW is the total fruit weight (g).

Total soluble solids (TSS) content was measured using a digital refractometer (PAL-3, Atago Co., Ltd., Tokyo, Japan) with undiluted juice (0.3 mL), adjusted to 20 °C, and expressed in degrees Brix (°Brix). Titratable acidity (TA) was determined by an automatic titrator (TitroLine^® easy, Schott Instruments GmbH, Mainz, Germany), using 25 mL of diluted juice with 0.1 N NaOH solution, and phenolphthalein as endpoint indicator. Acidity was reported as grams of citric acid per 100 mL of juice (g·100 mL⁻¹), following the AOAC [37] protocols. The ratio of total soluble solids to titratable acidity (TSS/TA) was calculated to provide an integrated measure of fruit flavor balance. The technological index, an estimate of the extractable TSS per commercial citrus box (maximum capacity of 40.8 kg), was calculated based on Di Giorgi et al. [38]:

$$\mathrm{TI}={\displaystyle \frac{\mathrm{TSS}\times \mathrm{JC}\times 40.8}{\mathrm{10,000}}},$$

where TI is the technological index (kg TSS·box⁻¹); TSS is total soluble solids (°Brix); and JC is juice content (%). TI is expressed as kg of TSS per box (kg TSS·box⁻¹).

2.7. Fresh Fruit and Juice Processing Performance Indices

To integrate multiple yield and quality traits into a single metric reflecting market-specific performance, we developed two composite indices: the Fresh Fruit Index (FFI) for table fruit and the Juice Processing Index (JPI) for industrial processing based on normalized data. Mean values of cumulative yield, TSS, fruit weight, seed number, and juice content were used from four consecutive July harvests (2007–2010), representing the full productive phase of the orchard. Variable values were normalized to account for differences in scale and units, following Ramos et al. [39], allowing fair comparison among traits with different magnitudes:

$$\mathrm{N}1=\left|{\displaystyle \frac{\max -\min }{2}}\right|,\mathrm{N}2=\left|{\displaystyle \frac{\mathrm{N}1\times 100}{\max }}\right|,\mathrm{N}3=\left|{\displaystyle \frac{\mathrm{N}2\times \mathrm{V}}{\mathrm{N}1}}\right|,$$

where max and min represent the extreme values of each variable for all clones, and V is the observed value for a given variable.

The general formula for each index was adapted from Ramos et al. [39] and Caputo et al. [40]:

$$\mathrm{Index}=\left|{\displaystyle \frac{\left(\mathrm{A}\times \mathrm{a}\right)+\left(\mathrm{B}\times \mathrm{b}\right)+\cdots +\left(\mathrm{N}\times \mathrm{n}\right)}{\left(\max -\min \right)}}\right|,$$

where A, B,…, N represent the normalized scores of each variable; a, b,…, n are the corresponding weightings that reflect the relative importance of each variable for the targeted market; max and min represent the extreme values of each trait for all selections.

The weights assigned to each trait in the FFI and JPI reflect their relative importance for the target market. For fresh fruit (FFI), traits were weighted based on their relevance to consumer preference: yield (35%) and fruit weight (25%) influence marketable volume, TSS content (25%) affects sweetness, and seed number (15%) is inversely related to consumer acceptability. For industrial purposes (JPI), yield, TSS, and juice content were considered equally critical for juice processing efficiency (33.3% each) [29].

2.8. Data Analysis and Graphical Visualization

All data were analyzed using a randomized complete block design, with assumptions of normality of residuals using the Shapiro–Wilk test and homogeneity of variances using Bartlett’s test, both at a significance level of p ≤ 0.05. Horticultural variables were subjected to one-way analysis of variance (ANOVA), and treatment means were grouped using the Scott–Knott clustering test at p ≤ 0.05. Fruit quality data were analyzed in a two-way ANOVA, considering the fixed effects of ‘Pera’ clones (13 levels) and sampling month (June and July), as well as their interactions. Mean comparisons were performed when significant effects were detected. To investigate the vegetative growth dynamics of the selections over time, annual measurements of tree height and canopy volume were modeled using second-degree polynomial regression. The fitted model followed the equation:

$$\mathrm{y}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1\mathrm{x}+{\mathsf{\beta }}_2{\mathrm{x}}^2,$$

where x represents tree age (in years), and y corresponds to the growth variable under evaluation.

Model performance was assessed based on the coefficient of determination (R²) and the significance of the regression via F-test (p ≤ 0.05). All statistical analyses were conducted using the R software (v. 4.4.0; R Foundation for Statistical Computing, Vienna, Austria) through the RStudio interface.

3. Results

3.1. Vegetative Growth

Significant differences (p ≤ 0.05) in tree height, trunk diameters, and trunk indices were observed among the 13 ‘Pera’ sweet orange selections evaluated at five years of age, during the 2006 growing season (

Table 1. Vegetative growth traits of 13 ‘Pera’ sweet orange selections evaluated at the young (five-year-old in 2006) stage of the orchard in Paranavaí, Paraná State, Brazil.

Pera Selection	Rootstock Trunk Diameter (cm) a	Scion Trunk Diameter (cm)	Trunk Diameter Index b	Tree Height (m)	Canopy Diameter (m)	Canopy Volume (m3)
D-6	13.0 b c	10.5 b	0.811 b	2.57 b	2.90 a	12.2 a
Gullo	12.7 b	10.6 b	0.834 b	2.66 b	3.09 a	13.3 a
IPR 153	14.1 a	12.4 a	0.872 a	2.92 a	3.27 a	16.7 a
IPR 158	11.5 c	10.6 b	0.925 a	2.58 b	2.97 a	12.5 a
IPR 159	10.1 c	9.0 b	0.899 a	2.39 b	2.72 a	9.7 a
Morretes	13.6 a	12.0 a	0.887 a	2.77 a	3.09 a	13.9 a
Seleção 11	15.7 a	13.4 a	0.855 a	2.82 a	3.58 a	19.3 a
Seleção 12	14.8 a	12.2 a	0.820 b	2.58 b	3.00 a	12.2 a
Seleção 14	14.9 a	12.2 a	0.818 b	2.80 a	3.10 a	14.4 a
Seleção 15	15.9 a	12.9 a	0.811 b	2.88 a	3.22 a	16.3 a
Seleção 27	15.7 a	14.0 a	0.891 a	2.76 a	3.35 a	16.5 a
Seleção 37	14.7 a	12.9 a	0.878 a	2.83 a	3.16 a	15.1 a
Vimusa	15.2 a	13.2 a	0.875 a	2.74 a	3.16 a	14.4 a
CV (%)	11.27	12.09	6.42	8.84	11.63	28.44
F value
Selection	6.17 ***	4.72 ***	2.31 *	1.97 *	1.72 ns	1.85 ns
Block	0.87 ns	1.31 ns	3.60 *	3.04 *	1.49 ns	1.93 ns

^a Trunk diameters were calculated based on trunk circumference measured 10 cm below and 10 cm above the graft union. ^b Trunk diameter index was based on the ratio between scion and rootstock trunk diameters. ^c Means followed by the same letter in the column belong to the same group according to Scott–Knott’s test. Significance level: ns, non-significant; *, p ≤ 0.05; ***, p ≤ 0.001.

; Figure 2). Rootstock trunk diameter ranged from 10 (‘IPR 159’) to 16 cm (‘Seleção 15’), while scion trunk diameter varied from 9 (‘IPR 159’) to 14 cm (‘Seleção 27’). Selections ‘IPR 159’, ‘IPR 158’, ‘Gullo’, and ‘D-6’ exhibited the smallest trunk diameters (≤13 cm), with values below the ones of all other selections. Trunk diameter indices were generally higher in selections such as ‘IPR 158’, ‘IPR 159’, ‘IPR 153’, ‘Morretes’, ‘Seleção 11’, ‘Seleção 27’, ‘Seleção 37’, and ‘Vimusa’ (≥0.85), suggesting more balanced graft unions compared to the other selections (≤0.83).

Tree height ranged from 2.39 (‘IPR 159’) to 2.92 m (‘IPR 153’), with moderate but significant differences. However, canopy diameter and volume did not differ (p > 0.05), although numerical differences were evident. ‘Seleção 11’ showed the largest canopy diameter and volume (3.58 m and 19.3 m³, respectively). In contrast, ‘IPR 159’ had the smallest growth measurements (2.72 m and 9.7 m³, respectively), consistent with its lower trunk dimensions and tree height. These results highlight the morphological variability among ‘Pera’ selections at the young orchard stage, with potential implications for vigor and early orchard development.

At the mature orchard stage (nine-year-old trees), differences among the selections were observed only for rootstock trunk diameters (p ≤ 0.001), while all other vegetative measurements showed no significant variation (p > 0.05;

Table 2. Vegetative growth traits of 13 ‘Pera’ sweet orange selections evaluated at the young (nine-year-old in 2010) stage of the orchard in Paranavaí, Paraná State, Brazil.

Pera Selection	Rootstock Trunk Diameter (cm) a	Scion Trunk Diameter (cm)	Trunk Diameter Index b	Tree Height (m)	Canopy Diameter (m)	Canopy Volume (m3)
D-6	16.6 b c	14.6 a	0.880 a	3.02 a	3.34 a	18.9 a
Gullo	17.0 b	15.3 a	0.902 a	3.16 a	3.54 a	21.1 a
IPR 153	19.5 a	17.7 a	0.906 a	3.40 a	3.63 a	23.9 a
IPR 158	16.2 b	15.2 a	0.932 a	3.00 a	3.41 a	19.1 a
IPR 159	13.1 c	12.3 a	0.939 a	3.04 a	3.04 a	15.3 a
Morretes	17.5 b	16.6 a	0.950 a	3.14 a	3.65 a	22.0 a
Seleção 11	21.1 a	18.8 a	0.895 a	3.20 a	3.82 a	24.7 a
Seleção 12	19.2 a	16.3 a	0.849 a	2.95 a	3.33 a	17.6 a
Seleção 14	18.7 a	16.1 a	0.864 a	3.03 a	3.49 a	20.1 a
Seleção 15	22.2 a	17.8 a	0.794 a	3.39 a	3.55 a	24.6 a
Seleção 27	20.2 a	18.5 a	0.916 a	3.12 a	3.78 a	23.6 a
Seleção 37	18.8 a	17.3 a	0.918 a	3.52 a	3.82 a	28.1 a
Vimusa	19.1 a	14.5 a	0.761 a	3.04 a	3.70 a	21.8 a
CV (%)	10.92	18.50	13.95	11.54	12.47	32.56
F value
Selection	6.93 ***	1.88 ns	1.02 ns	1.18 ns	1.30 ns	1.19 ns
Block	1.27 ns	1.73 ns	1.70 ns	3.12 *	1.69 ns	1.82 ns

^a Trunk diameters were calculated based on trunk circumference measured 10 cm below and 10 cm above the graft union. ^b Trunk diameter index was based on the ratio between scion and rootstock trunk diameters. ^c Means followed by the same letter in the column belong to the same group according to Scott-Knott’s test. Significance level: ns, non-significant; *, p ≤ 0.05; ***, p ≤ 0.001.

). Rootstock trunk diameter ranged from 13 cm for ‘IPR 159’ to 22 cm for ‘Seleção 15’ trees at mature age, and scion trunk diameter ranged from 12 (‘IPR 159’) to 19 cm (‘Seleção 11’). Selections such as ‘IPR 153’, ‘Seleção 11’, ‘Seleção 12’, ‘Seleção 14’, ‘Seleção 15’, ‘Seleção 27’, ‘Seleção 37’, and ‘Vimusa’ maintained higher trunk diameters, similar to the trends observed at the young orchard stage.

Despite not showing any differences in most growth measurements, ‘Seleção 37’ had the largest canopy volume (28.1 m³), followed closely by ‘Seleção 11’ (24.7 m³), ‘Seleção 15’ (24.6 m³), and ‘IPR 153’ (23.9 m³). These selections were also among the ones with the highest trunk diameters, reinforcing their vigorous vegetative growth. ‘IPR 159’ remained the least vigorous selection, with the smallest trunk dimensions, canopy diameter (3.04 m), and canopy volume (15.3 m³).

Trunk diameter indices remained within a narrow range: 0.76–0.95. Some selections, such as ‘Morretes’, ‘IPR 159’, and ‘IPR 158’, maintained a more balanced scion/rootstock ratio (≥0.93) compared to ‘Vimusa’ and ‘Seleção 15’, which showed lower indices (≤0.79), indicating thicker rootstock trunks relative to the scion. Overall, although vigor differences among selections were more evident at the young stage, several selections, particularly ‘Seleção 11’, ‘Seleção 15’, ‘Seleção 37’, and ‘IPR 153’, sustained high vegetative growth at the mature stage.

Tree height showed a consistent nonlinear growth trend for all selections over time (Figure 3). Quadratic regression models were highly significant for all genotypes (p ≤ 0.001), with R² ranging from 0.91 (‘Seleção 12’) up to 0.98 (‘Seleção 14’), indicating a strong fit for the dataset. Growth curves followed a decelerating trend over time, indicating a gradual reduction in vertical growth rate with increasing tree age. ‘Morretes’ showed a strong initial growth rate followed by a marked curvature (y = 1.18 + 0.42x − 0.024x², R² = 0.98; Figure 3F), while ‘IPR 153’ combined a high linear term with moderate curvature (y = 1.44 + 0.37x − 0.018x², R² = 0.98; Figure 3C). Conversely, genotypes such as ‘IPR 159’ (y = 1.61 + 0.26x − 0.012x², R² = 0.94; Figure 3E) and ‘Seleção 12’ (y = 1.66 + 0.27x − 0.015x², R² = 0.92; Figure 3H) had slower growth and flatter curves. Overall, the regression models captured the genotype-specific growth dynamics, demonstrating significant variation in vertical development potential among the 13 ‘Pera’ selections. These findings demonstrated the importance of genotype choice when looking for orchard architectures that demand specific canopy management strategies or height limitations.

Quadratic regression models fitted to the canopy volume data were significant for all selections (p ≤ 0.001), with high coefficients (R² = 0.97–0.99), indicating strong genotype-dependent trends in canopy development (Figure 4). Among the evaluated genotypes, ‘Seleção 37’ showed the most vigorous canopy growth, reaching a mean volume of 28.1 m³ by the ninth year (Table 2). This selection was characterized by a steep growth rate over time (y = 0.87 + 3.26x − 0.017x², R² = 0.99; Figure 4L). ‘Seleção 15’ displayed a similar pattern with a slightly higher linear coefficient (y = −1.44 + 4.12x − 0.144x², R² = 0.99; Figure 4J). On the other hand, ‘IPR 159’ and ‘Seleção 12’ had the smallest canopy volumes at the end of the evaluation period, with final means of 15.3 and 17.6 m³, respectively. ‘IPR 159’ showed a relatively slow growth pattern (y = −1.36 + 1.75x − 0.022x², R² = 0.98; Figure 4E), while ‘Seleção 12’ had a moderate initial growth rate and a sharper decline in growth increment over time (y = −0.39 + 3.25x − 0.149x², R² = 0.98; Figure 4H). ‘Seleção 11’, ‘Seleção 15’, ‘IPR 153’, and ‘Seleção 27’ exhibited expressive canopy growth, each surpassing 23 m³ in mean volume at nine years old. Notably, ‘Seleção 11’ had one of the highest linear coefficients and a relatively large quadratic term, indicative of a rapid early growth followed by a marked deceleration (y = −4.88 + 5.99x − 0.299x², R² = 0.99; Figure 4G). Some selections exhibited large variability in annual means for both vegetative measurements, particularly ‘D-6’ (Figure 4A), ‘Seleção 15’ (Figure 4J), and ‘Seleção 37’ (Figure 4L), as indicated by larger standard errors in later years. The interannual variation in canopy volume observed in these genotypes indicates field heterogeneity that may affect orchard uniformity and require site-specific management, including differential pruning or planting density adjustments.

3.2. Fruit Yield

Significant differences (p ≤ 0.001) were observed among the ‘Pera’ selections in regard to annual and cumulative yields during the first four cropping seasons (2003–2006) at the young stage of the trees (two to five years old), as well as in relation to the alternate bearing index (ABI), whereas no differences were found for yield efficiency (p > 0.05) (Figure 5A; Table S1). ‘Gullo’, ‘IPR 153’, ‘Morretes’, ‘Seleção 12’, ‘Seleção 27’, and ‘Vimusa’ consistently ranked among the highest-yielding selections, with cumulative yields exceeding 214 kg·tree⁻¹, reflecting their early and sustained productivity during this stage.

In contrast, ‘IPR 159’, ‘IPR 158’, ‘Seleção 14’, ‘Seleção 11’, ‘Seleção 15’, ‘D-6’, and ‘Seleção 37’ had lower cumulative yields (<194 kg·tree⁻¹), with ‘IPR 159’ and ‘Seleção 11’ showing particularly limited early bearing potential at orchard establishment. Notably, ‘Seleção 11’ displayed very low yield in the first two years but showed substantial improvement in the latter seasons, particularly in 2006 (103 kg·tree⁻¹), contributing to a moderate cumulative yield of 182 kg·tree⁻¹ after the first four cropping seasons.

The ABI ranged from 0.21 to 0.47, with ‘Seleção 11’ exhibiting the highest index (0.47), indicating pronounced year-to-year yield fluctuations (Figure 5C). Selections ‘IPR 158’ and ‘Seleção 37’ also had relatively high ABI values (0.34 and 0.39, respectively), resulting in a less stable production. In contrast, all other ‘Pera’ selections had lower indices (<0.30) with more regular yields at the early bearing period. Although no significant differences were observed in yield efficiency during the young stage of the orchard (Figure 5E), the results show that several selections, including ‘Gullo’, ‘Morretes’, ‘D-6’, and ‘IPR 159’, combined satisfactory productivity with moderate vegetative growth (≥6.33 kg·m⁻³), demonstrating good early efficiency under the local conditions.

At the mature phase of the orchard (2007–2010), when trees were six to nine years old, ‘Morretes’, ‘Seleção 11’, ‘Seleção 27’, ‘Seleção 37’, ‘IPR 153’, and ‘Vimusa’ had the highest cumulative yields, above 370 kg·tree⁻¹ across four cropping seasons (Figure 5B; Table S2). ‘Seleção 11’ reached the highest cumulative production (418 kg·tree⁻¹), followed closely by ‘Seleção 37’ (414 kg·tree⁻¹) and ‘Morretes’ (404 kg·tree⁻¹), confirming superior performance and sustained productivity during the mature phase. In contrast, ‘IPR 159’ had the lowest cumulative yield (218 kg·tree⁻¹), showing lower productive potential under the same growing conditions. Although ‘Gullo’ had good performance in the early years, the cumulative yield during maturity was moderate (331 kg·tree⁻¹).

While ‘Seleção 14’, ‘Seleção 12’, ‘D-6’, and ‘Seleção 37’ had higher ABI values (≥0.16), other selections showed more regular fruiting patterns with lower indices (0.05 up to 0.14) at this period (Figure 5D). ‘IPR 153’ had the highest yield efficiency (5.59 kg·m⁻³), showing excellent yield per tree with moderate canopy volume (Figure 5E). In contrast, several selections with larger canopies, such as ‘Seleção 15’ and ‘Seleção 37’, had lower yield efficiency (<3.0 kg·m⁻³), indicating a less favorable balance between vegetative growth and fruit production. These results suggest that while some selections excelled in absolute fruit yield, others, such as ‘IPR 153’ and ‘Seleção 14’, combined high productivity with efficient space utilization, potentially offering better agronomic performance in high-density plantings or resource-limited conditions.

3.3. Fruit Quality

Differences (p ≤ 0.05) were also observed among the ‘Pera’ selections for all fruit quality attributes evaluated over four harvest seasons (2007–2010) (

Table 3. Average fruit quality parameters of 13 ‘Pera’ sweet orange selections cultivated in Paranavaí, Paraná State, Brazil. Data represent the mean values obtained during four harvest seasons (2007–2010).

Source of Variance	Fruit Length FL (mm)	Fruit Diameter FD (mm)	Fruit Shape (FL/FD)	Fruit Weight (g)	Number of Seeds
Pera selection
D-6	70.2 b a	69.7 a	1.02 c	197 a	4.3 a
Gullo	69.4 b	67.5 b	1.02 c	183 b	4.2 a
IPR 153	71.7 a	70.3 a	1.02 c	201 a	3.1 c
IPR 158	72.3 a	69.9 a	1.03 b	195 a	3.7 b
IPR 159	70.1 b	66.6 b	1.05 a	171 b	4.3 a
Morretes	70.6 b	68.1 b	1.03 b	185 b	4.1 a
Seleção 11	71.4 a	69.0 a	1.03 b	190 a	4.1 a
Seleção 12	69.1 b	67.0 b	1.02 c	175 b	4.0 a
Seleção 14	71.2 a	69.3 a	1.03 c	188 a	4.0 a
Seleção 15	70.4 b	67.7 b	1.04 b	179 b	3.7 b
Seleção 27	69.1 b	66.6 b	1.04 b	169 b	4.2 a
Seleção 37	69.3 b	67.5 b	1.03 b	178 b	4.0 a
Vimusa	70.4 b	68.1 b	1.03 b	184 b	3.8 b
Month
June	70.0 b	68.1 a	1.03 a	180 b	4.2 a
July	70.8 a	68.4 a	1.03 a	189 a	3.7 b
CV (%)	3.30	3.04	1.03	8.33	12.26
F value
Selection (S)	1.99 *	3.43 ***	7.74 ***	3.87 ***	4.51 ***
Month (M)	4.79 *	0.86 ns	2.45 ns	10.6 **	34.3 ***
S × M	0.29 ns	0.25 ns	0.50 ns	0.20 ns	1.48 ns
Block	4.01 **	3.72 **	2.76 *	4.02 **	1.37 ns

^a Means followed by the same letter in the column belong to the same group according to Scott–Knott’s test. Significance level: ns, non-significant; *, p ≤ 0.05; **, p ≤ 0.01; ***, p ≤ 0.001.

and

Table 4. Average juice quality parameters of 13 ‘Pera’ sweet orange selections cultivated in Paranavaí, Paraná State, Brazil. Data represent the mean values obtained across four harvest seasons (2007–2010).

Source of Variance	Juice Content (%)	Total Soluble Solids TSS (°Brix)	Titratable Acidity TA (g∙100 mL−1)	Ratio (TSS/TA)	Technological Index (kg TSS∙box−1)
Pera selection
D-6	54.6 b a	10.8 b	0.89 b	12.0 d	1.55 b
Gullo	54.9 b	11.8 a	0.89 b	13.7 b	1.62 a
IPR 153	54.3 b	10.4 b	0.72 d	14.5 a	1.52 c
IPR 158	53.1 c	10.4 b	0.74 d	14.3 a	1.50 c
IPR 159	52.9 c	10.6 b	0.95 a	11.5 d	1.51 c
Morretes	56.1 a	10.7 b	0.84 c	13.1 c	1.57 b
Seleção 11	53.7 c	10.2 b	0.78 d	13.3 c	1.50 c
Seleção 12	54.9 b	10.9 b	0.84 c	13.0 c	1.56 b
Seleção 14	53.3 c	10.7 b	0.81 c	13.1 c	1.52 c
Seleção 15	52.5 c	10.9 b	0.87 c	12.8 c	1.53 c
Seleção 27	53.4 c	11.1 b	0.83 c	13.7 b	1.55 b
Seleção 37	54.8 b	10.7 b	0.75 d	14.4 a	1.54 c
Vimusa	55.0 b	10.4 b	0.77 d	13.8 b	1.53 c
Month
June	53.5 b	10.7 a	0.88 a	12.3 b	1.52 b
July	54.7 a	10.8 a	0.76 b	14.4 a	1.55 a
CV (%)	2.16	8.03	7.74	5.71	3.96
F value
Selection (S)	8.14 ***	2.09 *	10.6 ***	13.4 ***	3.07 ***
Month (M)	30.6 ***	1.34	107.9 ***	245.0 ***	8.59 **
S × M	1.80 ns	0.09 ns	0.48 ns	0.27 ns	0.33 ns
Block	0.47 ns	3.20 *	2.15 ns	0.73 ns	3.07 *

^a Means followed by the same letter in the column belong to the same group according to Scott–Knott’s test. Significance level: ns, non-significant; *, p ≤ 0.05; **, p ≤ 0.01; ***, p ≤ 0.001.

). Fruit length ranged from 69 (‘Seleção 12’ and ‘Seleção 27’) to 72 mm (‘IPR 158’), with ‘IPR 153’, ‘IPR 158’, ‘Seleção 11’, and ‘Seleção 14’ exhibiting the highest values (≥71 mm). Fruit diameter varied between 67 (‘IPR 159’ and ‘Seleção 27’) and 70 mm (‘IPR 153’), and the average fruit shape index ranged from 1.02 to 1.05. ‘IPR 159’ displayed a more elongated fruit shape (1.05), differing from most other selections, which generally produced rounder fruits (≈1.02–1.03). Fruit weight was also influenced by genotype, ranging from 169 (‘Seleção 27’) to 201 g (‘IPR 153’). Selections ‘IPR 153’, ‘IPR 158’, ‘D-6’, ‘Seleção 11’, and ‘Seleção 14’ comprised the group with the heaviest fruits (≥188 g), while lighter fruit weights were observed for the other selections. Seed count per fruit showed moderate variation among selections (3 to 4 seeds), with ‘IPR 153’ trees producing fewer seeds per fruit than most other selections, a desirable trait for fresh market preferences. Other selections with relatively fewer seeds included ‘IPR 158’, ‘Seleção 15’, and ‘Vimusa’ (Table 3).

The timing of harvest had a significant effect on fruit length and weight, with fruit harvested in July exhibiting higher length and weight than those collected in June, while fruit diameter and shape were not affected by harvest timing (Table 3). Also, there were no significant selection × month interactions for any of the evaluated traits (Table 3 and Table 4). This indicates that all selections followed a similar seasonal pattern of fruit development and maturation. Therefore, while individual fruit size and weight varied with harvest date, the overall timing of ripening did not differ significantly among the evaluated selections.

Highly significant variation (p ≤ 0.001) was also observed among the selections for most juice quality traits, except for TSS, which showed a moderate difference among the genotypes (p ≤ 0.05; Table 4). Juice content ranged from 53 (‘Seleção 15’) to 56% (‘Morretes’). TSS concentration ranged from 10.2 to 11.8 °Brix, with ‘Gullo’ achieving the highest value. TA also showed strong genotypic effects, with acidity levels ranging from 0.72 (‘IPR 153’) to 0.95 g∙100 mL⁻¹ (‘IPR 159’). The TSS/TA ratio was the highest for fruits of ‘IPR 153’, ‘IPR 158’, and ‘Seleção 37’ trees (≥14.3).

The technological index ranged from 1.50 to 1.62 kg TSS·box⁻¹. Although the differences among the ‘Pera’ selections were significant, the values were relatively close, with ‘Gullo’ showing the highest index (1.62 kg TSS·box⁻¹), indicating excellent suitability for juice-processing. The timing of harvest significantly influenced most juice parameters. Fruits harvested in July exhibited higher juice content, lower acidity, and consequently higher sugar–acid ratio compared to those collected in June, reflecting advanced ripening. Despite this seasonal effect, the relative performance of the ‘Pera’ selections remained consistent between months.

3.4. Fresh Fruit and Juice Processing Performance Indices

The highest-ranked selections for the fresh fruit market were ‘Seleção 12’, ‘Seleção 37’, ‘Seleção 11’, ‘Morretes’, ‘Vimusa’, and ‘Seleção 27’ (index > 4.0), characterized by higher values for fruit weight and TSS, combined with lower seed count (Figure 6A). In contrast, ‘IPR 159’, ‘IPR 153’, ‘Gullo’, ‘Seleção 15’, ‘IPR 158’, ‘D6’, and ‘Seleção 14’ comprised the lower-performing group (index < 4.0) for the fresh fruit market, with reduced indices (p ≤ 0.001).

Similarly, the industrial processing index (Figure 6B) also differentiated the selections into three groups (p ≤ 0.001). The group with the highest juice-processing potential included ‘Morretes’, ‘Seleção 11’, ‘Seleção 12’, ‘Seleção 37’, ‘Vimusa’, ‘IPR 153’, ‘Seleção 15’, and ‘Seleção 27’, which exhibited superior combinations of juice content, cumulative yield, and TSS content (index > 3.0). Conversely, ‘IPR 159’ scored the lowest industrial processing index (1.39), reflecting less favorable horticultural traits for industrial processing, particularly lower juice content and yield. All other selections exhibited intermediate indices (<3.0). Together, these indices demonstrate the versatility of some ‘Pera’ selections, such as ‘Seleção 12’, ‘Seleção 37’, ‘Seleção 11’, ‘Morretes’, ‘Vimusa’, and ‘Seleção 27’, which showed high performance in both fresh and industrial markets, making them promising candidates for dual-purpose cultivation strategies. On the other hand, ‘IPR 159’ exhibited consistently lower scores for both indices, indicating limited suitability for either commercial use.

4. Discussion

The nine-year field evaluation of 13 ‘Pera’ sweet orange selections under humid subtropical Brazil revealed pronounced differences in vegetative growth, yield performance, and fruit quality, key traits that determine their suitability for either juice processing or fresh fruit markets. Variations in tree height, trunk diameter, and trunk index at five years of age indicate that specific clones may be better aligned with particular planting systems or management approaches. For instance, the consistently low vigor of ‘IPR 159’ for all vegetative parameters points to its potential use in high-density orchards or settings where compact tree growth is desirable [28,41,42]. However, this selection had the lowest yields among the selections but showed higher productivity under supplemental irrigation [21]. In contrast, selections such as ‘Seleção 11’, ‘Seleção 15’, ‘Seleção 37’, ‘Seleção 27’, ‘Morretes’, and ‘IPR 153’ combined vigorous growth with consistently high yields, making them strong candidates for commercial cultivation in subtropical Brazil. To fully explore the potential of these high-performing clones, further investigations are needed to refine production systems, particularly in terms of rootstock combination, optimal spacing, planting density, pruning intensity, and pest and disease management strategies.

Although significant differences in most vegetative growth parameters were evident during the early stage of the orchard development (five-year-old trees), these variations became less pronounced as the trees matured (nine-year-old trees). This convergence over time likely reflects a natural stabilization of vegetative growth as trees approach their structural and physiological limits [43]. Additionally, the reduced variability at maturity may be attributed to the uniform genetic background of the evaluated clonal selections, all derived originally from ‘Pera’. Despite early differences in vigor and canopy development, this common genetic origin appears to lead to more uniform growth patterns in later stages, particularly under the same environmental and management conditions. Nonetheless, some selections, such as ‘Seleção 37’ and ‘Seleção 15’, maintained more vigorous growth through the later years, indicating a degree of sustained vegetative potential beyond the initial establishment phase and contributing to a less uniform composition within the orchard. This variability may reflect genotype-by-environment interactions or fluctuations in vegetative allocation and could pose challenges in commercial settings where uniformity is desirable [8,44]. Furthermore, all selections were graft-compatible with Rangpur lime, as evidenced by higher trunk diameter indices (≥0.80) and absence of over-growth at the graft union or tree decline [45,46] after nine years. This aligns with previous reports demonstrating the broad compatibility of this rootstock with various sweet orange selections and related germplasms [21,47,48].

These growth trends were further clarified through the regression analysis of the tree growth, which revealed different growth curves among the selections. The strong fit of quadratic models (R² ≥ 0.91) confirmed the decelerating nature of vertical and canopy growth over time (Figure 3 and Figure 4), a pattern typical of perennial fruit crops [49]. Although many selections showed a tapering of growth consistent with orchard maturity, clones such as ‘IPR 153’, ‘Morretes’, and ‘Seleção 15’ combined initial growth with sustained development over time. In contrast, other selections, including ‘IPR 159’ and ‘Seleção 12’, exhibited flatter curves and smaller final canopy volumes. These distinctions in long-term growth behavior, though subtle by orchard maturity, remain highly relevant for aligning genotype selection with specific orchard designs and management goals, particularly in commercial systems that prioritize uniform tree heights and canopy architecture to maximize planting density.

In this context, Girardi et al. [28] emphasize the importance of tailoring canopy architecture and tree vigor to orchard productivity, demonstrating that while more vigorous trees produce higher yields, their benefit to land use efficiency is maximized when planted at moderate to high densities. On the other hand, selections that maintain more compact growth could allow for more efficient harvest operations and spraying volumes [50,51,52], both critical for sustainable citrus production. This aspect becomes even more important when considering pest and disease management, particularly for the Asian citrus psyllid (ACP, Diaphorina citri Kuwayama), the vector of the huanglongbing (HLB or citrus greening) bacterium Candidatus Liberibacter asiaticus, which demands a high frequency of spraying in endemic areas [53,54]. During the experimental period (2001–2010), pests and diseases were effectively controlled, particularly citrus canker, and trees were not yet affected by HLB. Rehberg et al. [55] showed that uneven canopy height and depth reduce insecticide coverage, particularly on the undersides of leaves and within the inner canopy, thereby allowing the residual population of ACP to persist. Therefore, selections with more uniform and manageable canopies could support more effective pest and disease control through improved spray penetration and coverage.

Fruit yield varied among selections, both during the young and mature phases of the orchard. Notably, ‘Morretes’, ‘Seleção 11’, ‘Seleção 27’, ‘Vimusa’, and ‘IPR 153’ showed high cumulative yields and stable performance over time, with relatively low ABIs. These findings are particularly relevant given that alternate bearing poses a significant challenge to citrus production, often associated with endogenous hormonal imbalances and limitations in carbohydrate storage [56,57,58]. The low ABI values observed in these genotypes suggest better flowering and fruit set regulation mechanisms over time [59], making them suitable for commercial production with more predictable yields.

Yield efficiency, which reflects the relationship between fruit production and canopy volume, favored the selection ‘IPR 153’, which demonstrated a desirable combination of higher yields and moderate vegetative vigor (Figure 5F). This trait is particularly advantageous in modern orchard systems, where constraints such as limited land availability, high labor demands, and rising input costs necessitate more efficient production strategies for diverse fruit species [60,61,62]. Trees that exhibit higher yield efficiency support more sustainable and cost-effective cultivation by maximizing resource use, facilitating canopy management, and allowing for higher planting densities without compromising output [28]. In contrast, although ‘Seleção 37’ showed high absolute yields, its lower yield efficiency reveals a trade-off between vigor and the productive use of canopy space.

Fruit quality and performance indices were also influenced by genotype, underscoring the importance of selecting varieties that align with market demands and production goals. Genotypic variation in fruit morphology and internal quality traits, such as TSS, juice content, sugar–acid ratio (TSS/TA), and seed number, revealed consistent differences that were stable across harvest periods, reinforcing the commercial reliability of top-performing selections. Despite the differences in the number of seeds (3–4), all ‘Pera’ selection fruits were commercially classified as seedless, since they had fewer than eight seeds per fruit [7]. Among the evaluated genotypes, ‘IPR 153’ stood out for producing larger fruits, a trait highly desirable for fresh consumption [7,63]. However, its fresh market index was comparatively lower because the calculation incorporated both TSS content and cumulative yield, rather than yield efficiency. As a result, the benefits of its larger, seedless fruits were offset by lower TSS levels and intermediate cumulative yield over the evaluation period.

Juice quality parameters further distinguished ‘Pera’ selections. ‘Morretes’ consistently showed high juice content, and ‘Gullo’ had a high technological index, thus indicating favorable juice yield potential. In addition, the maturity index, a critical flavor determinant balancing sugar and acidity [63], was higher for ‘IPR 153’, ‘IPR 158’, and ‘Seleção 37’ orange juices. These traits are particularly relevant for juice processors, who seek varieties that deliver high TSS extraction efficiency along with consumer-pleasing flavor profiles. Importantly, the absence of significant genotype × month (harvest time) interactions for most fruit traits suggests that quality performance is relatively stable during the harvest periods, an attribute that enhances scheduling flexibility and commercial reliability.

The evaluation of market-specific indices offered a more integrative perspective on clonal selection performance (Figure 6). For the fresh market index, selections such as ‘Seleção 12’, ‘Seleção 37’, ‘Seleção 11’, ‘Morretes’, ‘Vimusa’, and ‘Seleção 27’ comprised a group characterized by higher productivity, larger fruit size, and higher TSS content (Figure 6). These traits are key drivers of consumer preference and postharvest value, as they directly influence visual appeal, eating quality, and market classification standards [63,64]. Larger fruits with uniform size improve packing efficiency and meet grading criteria for premium markets, while higher sweetness contributes to better flavor retention during storage and enhances consumer satisfaction [65,66,67]. Conversely, genotypes such as ‘IPR 159’, ‘Gullo’, and ‘D6’ scored lower on this index, largely due to smaller fruit size, reduced sweetness, and cumulative yield.

The juice-processing index yielded a slightly different ranking, emphasizing selections with high juice content, TSS, and cumulative yield. Top performers included ‘Morretes’, ‘Seleção 11’, ‘Seleção 27’, ‘Seleção 37’, ‘IPR 153’, and ‘Vimusa’, confirming the multifaceted potential of these genotypes (Figure 6B). On the other hand, genotypes such as ‘IPR 159’ and ‘D6’, ranked lower in both indices in our study; however, based on their vegetative growth, could present better performance in high-density orchards, increasing cumulative yield, facilitating cultural practices such as pruning, pest and disease control, fertilization, and harvesting, while also enhancing land-use efficiency and being alternatives to mitigate the economic impact of removing HLB-symptomatic trees and to manage ACP [28]. In addition, further investigations testing dwarfing rootstocks could enable even higher planting densities in clones that already show lower vegetative growth, such as ‘IPR 159’ and ‘D6’. Overall, integrating fruit quality traits with market-specific indices provides a robust framework for genotype selection. Moreover, the observed genotype stability during the July harvest suggests that certain selections could support extended harvest windows, improving labor and processing logistics throughout the production chain.

In summary, selections such as ‘Morretes’, ‘IPR 153’, ‘Seleção 11’, ‘Seleção 27’, and ‘Seleção 37’ consistently demonstrated superior agronomic performance under rainfed conditions of subtropical Brazil, combining high cumulative yields, favorable canopy development, and desirable fruit traits for both fresh and juice processing markets. Their dual-purpose potential offers flexibility for diversified production strategies, particularly important in regions facing market fluctuations or processing constraints. These findings reveal the importance of selecting varieties that align with specific orchard designs, such as high-density plantings or mechanized operations, while also considering traits such as canopy uniformity, yield efficiency, and fruit quality stability during the harvest periods. In particular, ‘IPR 153’ and ‘Morretes’ stand out as promising options for integrated production systems that demand both high productivity and market versatility. Finally, the use of market-specific indices and vegetative growth models provides a practical decision-making framework to guide scion selection, orchard design, and long-term management planning in subtropical citrus-growing regions. Continued field validation across diverse rootstocks, spacing configurations, irrigation, pest and disease management systems will be essential to refine recommendations and maximize the genetic potential of these elite ‘Pera’ selections.