Production performance of blueberry cultivars under high-altitude tropical conditions over a production cycle¹

Desempenho produtivo de cultivares de mirtilo em condições tropicais de alta altitude durante um ciclo de produção

ABSTRACT

Blueberries have gained popularity due to their nutritional value. Colombia’s suitability for production is due to its relatively stable climate, which allows it to export blueberries to other countries when they are out of season. Despite the crop’s economic importance, uncertainties remain regarding the selection of the most suitable cultivars. This research aimed to evaluate the performance of three southern highbush blueberry cultivars over a production cycle under high-altitude tropical climatic conditions in Colombia. Cultivars were established under different planting densities, and cumulative yields were interpreted in relation to these conditions. A repeated measures design was adopted, with cultivars as the between-subjects factor and evaluation time as the within-subject factor. Variables evaluated included fruit weight per plant (FW), mean individual fruit weight (IFW), total number of fruits (TNF), and fruit size. Repeated-measures ANOVA, trend analysis, and autocorrelation were applied, along with characterization of fruit size and quality. Yield peaks occurred during key export months, with production increasing approximately every five weeks across all cultivars. ‘Biloxi’ (15.0 t ha^-1) maintained stable yields, ‘Legacy’ (7.7 t ha^-1) showed the lowest performance. In contrast, ‘Victoria’ (26.9 t ha^-1) exhibited the highest performance, associated with better acclimation, producing a higher proportion of medium and large fruits with high quality, despite a slight decrease in IFW. These results highlight the importance of selecting cultivars to maximize productivity and export potential.

Key words:
Vaccinium corymbosum L.; yield; berries; postharvest quality; tropical highlands

HIGHLIGHTS

Cultivar ‘Victoria’ yielded 26.9 t ha⁻¹ and produced mostly medium and large fruits under high-altitude tropical conditions.

Blueberry production occurs every five weeks and reaches significant peaks in key months for export.

This study provides the first evidence of the productive performance of ‘Victoria’ blueberry under high-altitude tropical conditions.

RESUMO

Os mirtilos têm ganhado popularidade devido ao seu valor nutricional. A aptidão da Colômbia para a produção deve-se ao seu clima relativamente estável, que permite exportar mirtilos para outros países durante os períodos de entressafra. Apesar da importância econômica da cultura, persistem incertezas quanto à seleção dos cultivares mais adequados. O objetivo da pesquisa foi avaliar o desempenho de três cultivares de mirtilo do grupo southern highbush durante um ciclo produtivo sob condições climáticas tropicais de altitude na Colômbia. As cultivares foram estabelecidas em diferentes densidades de plantio, e os rendimentos cumulativos foram interpretados em função dessas condições. Adotou-se um delineamento de medidas repetidas, com cultivares como fator entre sujeitos e tempo de avaliação como fator intra-sujeito. Avaliaram-se variáveis: peso de frutos por planta (PF), peso médio individual (PMF), número total de frutos (NTF) e tamanho dos frutos. Aplicaram-se ANOVA de medidas repetidas, análise de tendência e autocorrelação, além da caracterização do tamanho e da qualidade dos frutos. Os picos de rendimento ocorreram nos principais meses de exportação, com aumento da produção aproximadamente a cada cinco semanas em todos os cultivares. ‘Biloxi’ (15,0 t ha^-1) manteve rendimentos estáveis; ‘Legacy’ (7,7 t ha^-1) apresentou o menor desempenho. Em contraste, ‘Victoria’ (26,9 t ha^-1) apresentou o maior rendimento, associado a uma melhor aclimatação, produzindo mais frutos médios e grandes de alta qualidade, apesar de leve redução no PMF. Esses resultados destacam a importância da seleção adequada de cultivares para maximizar a produtividade e o potencial de exportação.

Palavras-chave:
Vaccinium corymbosum L.; rendimento; bagas; qualidade pós-colheita; terras altas tropicais

Introduction

Blueberry (Vaccinium corymbosum L.) is a berry from North America noted for its distinctive slightly sweet and sour taste, nutraceutical properties, vitamins, minerals, and high antioxidant content. These fruits are effective in preventing diseases such as diabetes, Alzheimer’s, and cancer, and they also fulfill antihypertensive and antibacterial functions. Such benefits make blueberries widely appreciated by consumers for their contributions to human health, thereby promoting their cultivation and elevating the crop’s economic importance worldwide (Yousef et al., 2013; An et al., 2023). Currently, the main blueberry cultivars correspond to highbush cultivars that dominate the market due to their broad adaptation to soils and climates, resistance to pests and diseases, and favorable organoleptic properties, representing approximately 55% of global production. Blueberries are subdivided into northern and southern highbush blueberries. The latter come from interspecific hybridization between northern highbush blueberries (V. corymbosum) and two species native to southeastern North America: the evergreen blueberry (V. darrowi) and the rabbiteye blueberry (V. ashei), selected for their outstanding traits for plant breeding programs. In the search for less cold-dependent cultivars under climate change, rising temperatures have increasingly limited production in temperate regions (Cantuarias-Avilés et al., 2014; INIA, 2017; Fischer et al., 2021).

Some southern highbush cultivars are ideal for tropical areas (Cortés-Rojas et al., 2016; INIA, 2017). At the commercial level, among the main cultivars are ‘Misty’, ‘Jewel’, ‘Star’, ‘Biloxi’, ‘Emerald’, ‘O’Neal’, ‘Jubilee’, ‘Victoria’, ‘Reveille’, ‘Ventura’ and ‘Legacy’, which are characterized by low chilling requirements (between 200 and 600 hours, under 7 °C). Chilling requirements are essential to break dormancy and ensure proper flowering (Song & Cheng, 2018), and within southern highbush blueberries, genetic variation in chilling requirements leads to distinct phenological and productive responses among cultivars (Nagasaka et al., 2022). These cultivars are intended for the early fruit production, especially concentrated in October and November, with a prolonged harvest period, needing at least 100 hours of cold to produce without adaptation to warm weather conditions (Cantuarias-Avilés et al., 2014; INIA, 2017; Fischer et al., 2021; Salazar-Gutiérrez et al., 2023).

The United States leads the world’s blueberry production and, together with Canada, Chile, and Peru, accounts for 75% of the world’s cultivated area. Major blueberry exporters are Chile (20%), Canada (19%), Spain (11%), Peru (10%), the United States (9%), and the Netherlands (6%) (Pérez et al., 2022). The United States and Canada are the main blueberry consumers, but during the off-season (September to April) domestic supply falls short. As a result, they rely on imports from Chile, Argentina, Uruguay, and Peru, and more recently from Mexico, Brazil, and Colombia, creating new commercial opportunities for these countries (Cantuarias-Avilés et al., 2014; Medina et al., 2018). Blueberry production in Colombia started in 2019 with low-chill cultivars, and the rapid expansion of cultivated areas positions the crop as one of the most promising agricultural products in the country. Among the cultivars, ‘Biloxi’ predominates, accounting for approximately 80% of the planted area, followed by ‘Emerald’ (10%), ‘Legacy’ (5%), and ‘Victoria’ (3%) (Cantuarias-Avilés et al., 2014; Fischer et al., 2021; Castaño-Tarazona et al., 2025).

Colombia’s non-seasonal (relatively stable climatic) conditions, together with cultivar genetics and management practices such as pruning—which modifies hormonal balance, stimulates bud differentiation and favors floral induction and synchronization—shape the country’s productive potential, allowing blueberries to be harvested throughout the year (Fischer et al., 2021; Xie et al., 2021; Miranda et al., 2025). Moreover, high-altitude regions with specific soils and cool Andean climates provide environments that enhance antioxidant properties and palatability of the berries (Olmstead et al., 2015; Medeiros et al., 2017; Zeng et al., 2020), often favoring local economic development. Despite this, the productive sector has had low dissemination; and research on blueberry production in the country is incipient, limiting crop development due to ignorance of its behavior and production costs (Cortés-Rojas et al., 2016; Fischer et al., 2021; Prada-Muñoz & Coy-Barrera, 2024). So, to address this need, it is essential to consider that blueberry production depends on genetic factors, cultivation practices, external treatments, and climate (Salvo et al., 2012). The objective in this study was to evaluate the performance of three southern highbush blueberry cultivars during a harvest season under high-altitude tropical climatic conditions in Colombia, in order to take advantage of environmental characteristics that enable continuous production, thereby enabling domestic demand to be satisfied and contributing to the supply of importers during months when conditions limit production.

Material and Methods

The experiment was conducted during the second half of 2024 in an established commercial blueberry plantation located in Cómbita, a municipality of the department of Boyacá, Colombia (5° 41′ 55″ N, 73° 16′ 12″ W; 2720 m a.s.l.). Uniform four-year-old southern highbush blueberry plants of the cultivars ‘Biloxi’, ‘Legacy’, and ‘Victoria’, obtained from Fall Creek Farm & Nursery, Inc. (Lowell, OR, USA), were evaluated.

The cultivar ‘Victoria’ originated from the cross between ‘TH-622’ × ‘Millennia’ and has an estimated chilling requirement of approximately 500–550 hours below 7 °C (U.S. Plant Patent No. 25,994). The cultivar ‘Legacy’, a public southern highbush blueberry cultivar, was derived from the cross ‘Elizabeth’ × ‘US75’ and is characterized by moderate to high chilling requirements (> 800 chilling units) within this genetic group. It produces medium-sized berries with high firmness, desirable flavor, and a small, dry picking scar (USDA, 1993; Song & Cheng, 2018; Castaño-Tarazona et al., 2025). The cultivar ‘Biloxi’, also a public cultivar, with a relatively low chilling requirement (~200 h, considered early harvest), originated from the cross between ‘Sharpblue’ × ‘US329’ and is characterized by producing medium-sized, light-blue berries with high firmness and outstanding flavor quality (Spiers et al., 2002; Spiers et al., 2006; Castaño-Tarazona et al., 2025).

Evaluations were carried out from the week of July 12 to the week of December 27, 2024. The plants were arranged in soil classified as Andic Dystrustepts with the characteristics shown in Table 1, and covered with black polypropylene to prevent weed growth using a drip irrigation system consisting of four 1 L h^-1 drippers per plant. The mineral nutrition plan was implemented, and the weekly supply per hectare for the cultivar with the lowest planting density (‘Legacy’) involved mineral fertilizers at a rate of 1.03 kg of N, 0.34 kg of P, 1.15 kg of K, 0.27 kg of Ca, 0.16 kg of Mg, 0.14 kg of S, 0.005 kg of B, and 0.005 kg of Zn, with application frequency of once or twice a week depending on the phenological stage. Fertilizer application was adjusted according to planting density to ensure that all plants received equivalent nutrient doses regardless of cultivar density. During the experimental period, the plants were under an anti-hail net and all management procedures established by the company, such as pest and disease control, were maintained. Five plants of each cultivar were selected.

Table 1
Physicochemical characterization of the soil of the experimental area

Indicator	Value	Variable	Value
pH (soil:water 1:1)	5.43	Aluminum (cmolc kg-1)	0.25
Electrical conductivity (dS m-1)	1.46	Phosphorus (ppm)	75.7
Clay (%)	36	Iron (ppm)	85.74
Silt (%)	30	Manganese (ppm)	5.82
Sand (%)	34	Zinc (ppm)	12.88
Texture	Clay loam	Copper (ppm)	6.99
Organic matter (%)	6.63	Boron (ppm)	0.86
Organic carbon (%)	3.8	Sulfur (ppm)	39.3
Available nitrogen (%)	0.31	Cation exchange capacity (cmolc kg-1)	11.33
Potassium (cmolc kg-1)	1.19	Sum of bases (cmolc kg-1)	10.93
Calcium (cmolc kg-1)	8.39	Aluminum saturation (%)	2.17
Magnesium (cmolc kg-1)	1.16	Exchangeable acidity (cmolc kg-1)	0.4
Sodium (cmolc kg-1)	0.21

Environmental conditions were monitored throughout the experiment using the WS-ZL6 weather station (METER Group, US). The station recorded data on air temperature (°C) — maximum (Tmax), mean (Tmean), and minimum (Tmin) — relative humidity (%) — maximum (RHmax), mean (RHmean), and minimum (RHmin) — as well as precipitation (mm) and photosynthetically active radiation (PAR; mol m^-2 per week) (Figure 1). The data was stored and downloaded using the same measuring instrument at the end of the experiment. During this period, the mean daily light integral was 30.2 mol m^-2 per day, while the mean air temperature and relative humidity were 13 °C and 75%, respectively, and total accumulated precipitation amounted to 417.2 mm. The climatic data corresponded to the period between days 193 and 365 of the year, covering the 24-week evaluation period. Additionally, from pruning (12 February; day 43 of the year) until day 365, the experimental site accumulated approximately 1167 chilling units, estimated using the Utah Model (Richardson et al., 1974).

Figure 1
Climatic conditions during the experimental time: Photosynthetically Active Radiation – PAR (A) and precipitation (B) with a smoothing curve, relative humidity (C), and air temperature (D)

Fruits were harvested every eight days for 24 weeks between 8:00 and 10:00 hours from the cultivars as follows: ‘Biloxi’ planted at a distance of 1.5 × 0.5 m, ‘Legacy’ at 1.5 × 0.7 m and ‘Victoria’ at 1.2 × 0.5 m. All cultivars were subjected to the same pruning protocol and pruning date, consisting of uniform pruning at 20 cm height, performed five months before the beginning of the evaluation period, in order to ensure homogeneous management conditions among genotypes. Starting in July 2024, fruits were assessed from new shoots for variables measured immediately after harvest, as follows: fruit weight per plant (FW, g per plant), referred to as yield, recorded weekly and expressed as the mean value for each cultivar per week, mean individual fruit weight (IFW, g), total number of fruits (TNF), number of small-diameter fruits (SDF, < 12 mm), number of medium-diameter fruits (MDF, 12–16 mm), and number of large-diameter fruits (LDF, >16 mm), selecting only ripe fruit according to the Colombian technical standard for blueberries NTC-6373 (ICONTEC, 2019) and according to the harvest frequency managed in the region. Fruit quality variables, including firmness (N), total soluble solids (°Brix), and titratable acidity (%), were evaluated in week 13 of the production cycle using the methodology described by Castaño-Tarazona et al. (2025).

The study involved as response variables the FW and IFW evaluated week by week. The ‘Legacy’ cultivar was measured starting in week eight because from that week onward, more than 50% of the plants evaluated as observation units provided information of the response variables.

A repeated measures design was used, with the cultivars as a between-subjects factor and the time of evaluation as a within-subject factor, which allowed configuring a repeated measures design. The appearance of a late cultivar allowed a double consideration of the problem of evaluating the effect of the factors involved: one with two cultivars that could have 24 levels for the repeated measures factor (time) and another with 17 weeks for the three cultivars. Initially, the data were described by generating response profiles (yield indicators) for the two scenarios described (24- and 17-time evaluations). For this, a plot of profiles of average fruit weight (g) over time was constructed followed by marking the profiles of each cultivar with the fruit count, as well as the profiles of yield (FW, g per plant) over time per cultivar, smoothing the profile. Cumulative yield (t ha^-1 season^-1) was calculated as the sum of weekly yields (g per per plant season^-1) multiplied by the number of plants per hectare according to the planting density of each cultivar (‘Biloxi’: 13,333; ‘Legacy’: 9,524; and ‘Victoria’: 16,667 plants ha^-1).

A longitudinal data modeling procedure was used for the inferential component to consider the repeated measures design in factorial designs, with a semiparametric approach. The function ‘RM()’ from the R library ‘MANOVA.RM’ calculates the statistic using a nonparametric approach based on resampling, where no distributional assumptions or unique structures were made for the variance-covariance matrices. This has proven to be appropriate in small samples (Friedrich et al., 2019). For the case of the ‘RM()’ function, the linear model generated for d-random variate vectors was considered (Eq. 1):

(1) ${\text{Y}}_{\text{ik}}\text{=}{\left({\text{Y}}_{\text{ik}}\right)}_{\text{j=1}}^{\text{d}}{\text{=μ}}_{\text{i}}{\text{+ε}}_{\text{ik}}$

With n_i (unbalanced case, as in the present study) denoting the observational unit (plant) in group i, where i = 1,2 for the two-cultivar scenario and i = 1,2,3 for the three-cultivar scenario. For the repeated-measures factor, j = 1,…,q indexed the evaluation weeks, with q = 24 for the two-cultivar analysis and q = 17 for the three-cultivar analysis. The vector μ_i represented the mean response profile across evaluation weeks for cultivar i. For d = 1, errors were assumed to be independently and identically distributed when the single response variable FW was analyzed.

The null hypothesis tested by the ‘RM()’ procedure for cultivar effects was defined as H₀:Cμ = 0, where C is the contrast matrix used for comparing cultivar means. However, Friedrich et al. (2017) proposed a convenient technique for repeated measures based on Wald permutation, which controls the type I error rate quite well.

To determine the trend of IFW, the ‘trend()’ function of the ‘forecast’ library was used to extract the trend component of the time series. Also, the ‘notrend_test()’ function was used to perform hypothesis tests of no trend in the time series, based on the Mann-Kendall composite method. The results included the test statistical value, the p-value, and, in some cases, the trend slope estimator (Lyubchich et al., 2023).

To evaluate the temporal pattern of yield (FW), an autocorrelation analysis was performed to explore the temporal dependence of the variable using the function ‘ACF()’ from the R base library.

Fruit quality variables were analyzed using a multivariate analysis of variance (MANOVA) with the function ‘mancova()’, from the R package ‘jmv’, to evaluate the overall effect of cultivar on the combined response variables. Cultivar was included as a fixed factor, and firmness, total soluble solids, and titratable acidity were treated as dependent variables in a multivariate framework. The multivariate effect was assessed using Pillai’s Trace, considered the most robust statistic for small sample sizes. When a significant multivariate effect was detected, follow-up univariate analyses of variance (ANOVA) were performed for each dependent variable to identify the traits contributing to the overall effect. Statistical significance was determined at p ≤ 0.05.

Finally, fruit size distribution was evaluated as a physical attribute by calculating the proportion of fruits in each size category relative to the total fruit count, expressed as percentages. For each sampling month, differences among cultivars were analyzed using a one-way analysis of variance (ANOVA) under a completely randomized design. When significant effects were detected, mean separation was performed using Tukey’s honestly significant difference (HSD, p ≤ 0.05). All statistical analyses were conducted in R (R Core Team, 2024).

Results and Discussion

The visualization of the profile indicates an absence of parallelism associated with the change in the pattern of IFW between cultivars over time (Figure 2), where ‘Victoria’ and ‘Biloxi’ could be considered early fruiting cultivars and ‘Legacy’ late fruiting for this agroclimatic zone. When observing the cultivars that produced fruit during the 24 weeks, cultivar ‘Victoria’ showed higher performance than ‘Biloxi’. On the other hand, ‘Legacy’ excelled from week 13 to the end, compared to the other cultivars.

Figure 2
Response profile of the mean individual fruit weight of three blueberry cultivars during the second half of harvest, after 24 weeks under high-altitude tropical climatic conditions in Colombia. For cultivars ‘Biloxi’ (B), ‘Legacy’ (L), and ‘Victoria’ (V), numbers indicate the total number of fruits (TNF) per plant

IFW has a decreasing behavior harvest after harvest for all cultivars, highlighting that as time passes and FW increases (Figure 3), IFW decreases (Figure 2). Similar results to this pattern were presented by Milivojević et al. (2016), who evaluated some quality parameters in two highbush cultivars, ‘Duke’ and ‘Bluecrop’, for two years, finding that ‘Bluecrop’ decreased its IFW harvest after harvest, and regardless of the year, IFW decreased as well.

Figure 3
Average yield per plant of three blueberry cultivars, ‘Biloxi’ (B), ‘Legacy’ (L), and ‘Victoria’ (V), during the second half of the harvest, under high-altitude tropical climatic conditions in Colombia. Dashed lines represent observed weekly mean values, and solid lines correspond to smoothed trends illustrating the temporal production pattern

The IFW decrease could be related to the increase in TNF, which increased just as IFW decreased between weeks 16 and 20 for all three cultivars. When a plant produces more fruits, each fruit competes for a limited supply of resources, decreasing individual fruit weight (Jorquera-Fontena et al., 2018). This response observed in the month of November was similar to that presented by Murakami et al. (2023), who evaluated TNF in plants of the cultivar ‘Biloxi’ in their third and fourth productive year under tropical climatic conditions, finding an increase from the last week of October to the fourth week of November. It is important to mention that ‘Victoria’ was the cultivar with the highest TNF (Figure 2).

The cultivar ‘Biloxi’ showed a mean IFW value of 1.5 g for all weeks, the lowest among the three cultivars evaluated; however, higher than those that have been reported for this cultivar, for instance by García-Vázquez et al. (2023), who reported a value of 0.83 g in 3-year-old plants and Murakami et al. (2023), who presented values of 1.38 and 1.22 in plants in their third and fourth productive year, respectively. This cultivar showed an approximately stationary production; however, its IFW is usually lower when compared to that of other cultivars (Cortés-Rojas et al., 2016).

The cultivar ‘Legacy’ had its highest value in week 13, but it dropped until week 24; its total mean value was 1.9 g for all weeks and was consistent with the values reported by Muñoz-Vega et al. (2016) of 1.78 and Strik et al. (2017) of 2.12 g. The results are similar to those presented by Strik & Vance (2019), who evaluated nine highbush blueberry cultivars, obtaining a maximum value of 2.41 g and a minimum of 1.62 g for the cultivar ‘Legacy’, and like the results exhibited here, its IFW decreased harvest after harvest.

The cultivar ‘Victoria’ exhibited a decreasing type IFW and a mean value of 1.8 g for all weeks. The cultivar ‘Legacy’ showed a decreasing linear trend (p = 0.001), where IFW decreased from week 13 but stood out among the others until week 24. The cultivars ‘Victoria’ (p < 0.005) and ‘Biloxi (p < 0.029) showed decreasing linear trends (data not shown). Despite the clear interaction between the factors, it was evident that IFW seems to discriminate quite well between the cultivars (Muñoz-Vega et al., 2016).

De Salvador et al. (2017) evaluated southern highbush blueberries and found that, although IFW was higher for certain cultivars, they would not necessarily be the highest-yielding cultivars per plant, as is also observed in this study when contrasting the results observed in Figures 2 and 3.

The analysis of variance tables for the repeated measures design validates the interaction between the effect of time and the factor associated with the cultivar, both for the 24-week and 17-week cases (Table 2). This validation was most likely caused by something usual in agriculture: the experimental or observational units at the beginning of the trials are usually similar, and the differences become larger in later stages (Figure 3), as observed in the results of Cortés-Rojas et al. (2016).

Table 2
Wald-type statistic (p-values obtained by resampling) of two cultivars in 24 weeks and of three cultivars in 17 weeks, under high-altitude tropical climatic conditions in Colombia

Sources of variation	DF		Yield (24)	Yield (17)
	(24)	(17)	p-value
Cultivar (C)	1	2	0.064ns	0.115ns
Week (W)	23	16	0.002*	0.094ns
C × W	23	32	0.02*	0.044*

• ^*
  p ≤ 0.05;
• ^ns
  Not significant

Although the interaction plot (same profiles) was descriptive, the interaction was clearer in the yield in Figure 3, which decreased from week 10, when the differences become larger, with more similar patterns between ‘Legacy’ and ‘Biloxi’ but lower in yield compared to ‘Victoria’. Notably, when considering fruit count to standardized production (Figure 2), the interaction pattern was different; even the standardized production per fruit made ‘Legacy’ look superior. This is an indicator of interest regarding fruit size (Milić et al., 2018).

The analysis of the Wald-type statistic (p-values obtained by resampling) revealed significant differences in blueberry yield over time (Table 2). For the experiment with two cultivars evaluated over 24 weeks, significant effects of week (p = 0.002) and the cultivar × week interaction (p = 0.02) were observed, while the main effect of cultivar was not significant (p = 0.064).

In the evaluation with three cultivars over 17 weeks, no significant differences were found for either the cultivar effect (p = 0.115) or the week effect (p = 0.094); however, the interaction between the two factors showed marginal statistical significance (p = 0.044). This fact shows that cultivar differences depended on the evaluation period rather than being consistently expressed across all weeks. Therefore, cultivar-level comparisons in the 17-week scenario were supported primarily by the significant cultivar × week interaction (p = 0.044) and the descriptive yield profiles, rather than by a significant cultivar main effect. Differences among cultivars became more evident during specific time windows, particularly between weeks 16 and 24, when yield divergence among cultivars was more pronounced. This result demonstrates that the combined effect of cultivar and time simultaneously influenced blueberry yield and that yield patterns over time varied according to cultivar throughout the evaluation period.

The cultivar ‘Legacy’ exhibited delayed production that could be associated with the climatic conditions of the study area. Given that this cultivar has intermediate to high chilling requirements (over 800 chilling units) (Song & Cheng, 2018), it is possible that the environmental conditions in the region did not provide sufficient chilling accumulation for complete and synchronized dormancy release. The experimental site accumulated approximately 1167 chilling units from pruning until the final evaluation week, and nearly 800 chilling units until dormancy break, which occurred approximately one week before the beginning of the evaluation period. Therefore, the delayed onset of harvest observed in ‘Legacy’ may reflect a cultivar-specific phenological response associated with its comparatively higher chilling requirement under the high-altitude tropical conditions evaluated. According to Chu et al. (2025), this limited availability of chilling could result in slower and more uneven bud break and flowering, with consequent delays in production.

The cultivar ‘Legacy’ showed the lowest peak yield among the cultivars, attributable to climatic supply. This cultivar has elevated yields compared to other cultivars under temperate climate conditions (Strik et al., 2017; Yang et al., 2019a). Southern highbush blueberry cultivars exhibit contrasting chilling requirements, with cultivars such as ‘Biloxi’ generally characterized by lower chilling demands than ‘Legacy’. Therefore, differences in cultivar-specific chilling responses may have influenced flowering synchronization and harvest dynamics under the high-altitude tropical conditions evaluated in this study. Harvest peaks for the cultivars ‘Legacy’ and ‘Biloxi’ appeared in week 16 and 19, respectively.

Considering that phenotypic expression and the observed variation depend on the genetic background (G), the environment (E), and their interaction (G × E) (Falconer & Mackay, 1996), these results allow for the determination of genotypic performance (Redpath et al., 2021), highlighting the ‘Victoria’ cultivar. Its production exceeded that of the other cultivars throughout all weeks and exhibited the three highest production peaks during weeks 16, 19, and 22. This superior performance may be attributed to the compatibility between climate and genotype, enabling improved harvest performance (Redpath et al., 2021).

The acclimation of this cultivar to environmental conditions such as temperature, radiation, photoperiod, and chilling hours likely enhanced metabolic activity, enabling continuous assimilate production and allocation to different physiological processes and organs, thereby influencing development and yield (Fischer et al., 2021). Similarly, production peaks between weeks 16 and 22 may also be associated with increased radiation. Since light is a key factor in biomass conversion, greater availability and more efficient use of resources may increase final yield (Bryla et al., 2009; Salazar-Gutiérrez et al., 2023).

Production peaks observed between weeks 16 and 22 may be explained by the interaction of two synergistic processes. First, once dormancy is broken, blueberry plants progress through flowering and fruit set phases that tend to concentrate harvestable fruit within relatively defined temporal windows (Steyn et al., 2022). Second, because blueberry fruits are not extremely strong sinks, their growth depends largely on current photosynthesis and therefore on environmental conditions that enhance carbon assimilation, such as light, temperature, and water availability (Petridis et al., 2018; Lima et al., 2022).

In this context, the increase in photosynthetically active radiation (PAR) observed during this period (Figure 1A) may have favored assimilate supply to developing fruits. Likewise, radiation may promote yield by stimulating stomatal opening (Vialet-Chabrand., 2021; Salazar-Gutiérrez et al., 2023), greater CO₂ fixation, and photosynthetic activity, contributing to biomass accumulation and ultimately influencing yield (Doddrell et al., 2023; Salazar-Gutiérrez et al., 2023). The convergence of these phenological stage and favorable environmental conditions likely promoted the simultaneous growth and maturation of multiple fruits, resulting in the production peaks observed.

Production peaks may also be understood by the increase in TNF during the months of October and November (Figure 2). Cortés et al. (2016) observed similar results and reported production peaks in the last two months of the year with climatic conditions similar to those of this study. The harvest period has interesting behavior for fruit export: It provides berries to northern hemisphere countries during their low production season, usually between August and March (Medina et al., 2018).

Cumulative yield per plant (g per plant season^-1) differed among cultivars. ‘Victoria’ showed the highest yield (1613 g per plant season^-1), followed by ‘Biloxi’ (1126 g per plant season^-1) and ‘Legacy’ (812 g per plant season^-1). It was reflected in the cumulative yield extrapolated on an area basis and in the planting densities adopted for each cultivar. ‘Victoria’ showed the highest seasonal yield (26.9 t ha^-1 season^-1), followed by ‘Biloxi’ (15 t ha^-1 season^-1), while ‘Legacy’ exhibited the lowest cumulative yield (7.7 t ha^-1 season^-1), highlighting the importance of selecting the appropriate plant density to increase cumulative yield. Although close spacing may reduce yield per plant, overall yield per hectare increases due to the larger plant population (Fang et al., 2020).

The higher yield is usually a function of fruit size and weight (Yang et al., 2019b). The cultivar’s genetics could also explain this since these cultivars must adjust to all high-altitude Andean conditions to produce, and some might have better physiological acclimatization when compared to other cultivars.

Although southern highbush blueberries are obtained with low cold requirements, the plants of some cultivars, as in the case of ‘Legacy’, may fail to break dormancy properly due to insufficient low-temperature accumulation (Fischer et al., 2021). This condition could explain the better acclimatization and performance of ‘Victoria’ compared to the other cultivars.

The autocorrelation results allow us to understand how the yields over time are related to each other for the three cultivars evaluated (Figure 4). A recurring seasonal pattern with an approximate periodicity of five weeks was observed across cultivars. This behavior can be explained by the concept of “growth flushes”, which is well documented in perennial species grown under tropical conditions (Medina et al., 2018; Miranda et al., 2025). In such environments, plants exhibit successive waves of vegetative growth and flowering, reflecting the occurrence of multiple alternating vegetative and reproductive cycles (yield peaks) (Kozlowski & Pallardy, 1997). These cycles promote continuous biomass accumulation and support fruit production throughout the year.

Figure 4
Autocorrelation analysis (ACF) of weekly plant yields for blueberry ‘Biloxi’ (A), ‘Legacy’ (B), and ‘Victoria’ (C) during the second half of harvest under high-altitude tropical conditions in Colombia

Beyond characterizing yield dynamics, these results suggest that yields obtained in previous periods may influence subsequent production. This temporal dependency has practical implications, as it may inform management decisions and improve agricultural planning by allowing productive peaks to be brought forward and the optimization of cultural practices. It is necessary to conduct studies that assess the effects of different management practices on blueberry yield, since management is one of the main factors influencing production (Salvo et al., 2012). Also, since blueberries are temperate-zone fruit trees, certain management practices for inducing sprouting and flowering and increasing growth could improve production (Pio et al., 2019; Miranda et al., 2025).

The multivariate analysis revealed a significant overall effect of cultivar on the combined fruit quality attributes. However, subsequent univariate analyses indicated that this effect was exclusively associated with titratable acidity, which differed significantly among cultivars (F = 52.24, p < 0.0000001). In contrast, firmness (F = 1.43, p = 0.25) and total soluble solids (F = 0.40, p = 0.67) did not show significant differences. Mean firmness values ranged from 0.49 N to 0.51 N, and total soluble solids ranged from 11.76 to 12.01 °Brix across cultivars. For titratable acidity, ‘Victoria’ (0.75%) and ‘Biloxi’ (0.712%) exhibited significantly higher values than ‘Legacy’ (0.55%).

Previous studies describe ‘Biloxi’ as exhibiting intermediate to high fruit firmness (Castaño-Tarazona et al., 2025), a trait commonly associated with extended postharvest performance (Hirzel, 2024). Similarly, higher acidity levels have been linked to delayed ripening and improved shelf life (Li et al., 2021). In this context, the higher acidity observed in ‘Biloxi’ and ‘Victoria’ suggests a potentially greater postharvest quality.

The range of total soluble solids observed in this study can be considered high. Environmental factors, particularly radiation and temperature, have been reported to influence soluble solids accumulation. Greater solar radiation enhances photosynthetic carbon assimilation, increasing the availability of photoassimilates for fruit filling, whereas lower temperatures typical of high-altitude tropical environments are associated with reduced respiration rates, potentially favoring carbohydrate retention (Cortés-Rojas et al., 2016).

Overall, the combination of adequate firmness, elevated soluble solids, and cultivar-dependent acidity levels indicates that Colombian blueberries meet quality standards associated with both extended shelf life and desirable organoleptic attributes, supporting their potential for export markets.

Blueberry fruit size for the three cultivars indicates that ‘Biloxi’ has 51% MDF, followed by 35% LDF, and 14% SDF. These results agree with those reported by García-Vázquez et al. (2023), where most of the fruits of this cultivar correspond to medium size fruits.

Most of the fruits of the cultivar ‘Legacy’ corresponded to LDF (51%) and MDF (43%). Yang et al. (2019a) found that the main calibers for ‘Legacy’ corresponded mainly to MDF and LDF. Results indicated that, for the cultivar ‘Victoria’, the highest percentages corresponded to LDF and MDF, with values of 47 and 44%, respectively (data not shown). Overall, these findings provide valuable insights into the production of the ‘Victoria’ cultivar. Given the limited information available in the literature, they represent the first report on its productive performance.

This behavior is evidenced in the percentage distribution of calibers month by month. Figure 5 shows the percentage distribution of fruit size categories for the blueberry cultivars. Across all months evaluated, medium- and large-sized fruits accounted for the highest proportions, whereas small-sized fruits consistently represented the lowest percentage in the three cultivars.

Figure 5
Percentage diameter of three blueberry cultivars for small- (SDF), medium- (MDF), and large-diameter (LDF) fruits for ‘Biloxi’ (B), ‘Legacy’ (L), and ‘Victoria’ (V) during the second half of harvest under high-altitude tropical climatic conditions in Colombia. Different letters indicate significant differences among cultivar means within each fruit size category and month, according to Tukey’s honestly significant difference (HSD) test (p ≤ 0.05)

For ‘Biloxi’, MDF was the predominant size category in most months, with values ranging from approximately 45 to 55%, followed by LDF, while SDF remained below 30% in all cases. However, this cultivar showed a comparatively higher contribution of SDF in certain months, reaching values above 25%, which resulted in significant differences with the other cultivars within this size category.

In the cultivar ‘Legacy’, LDF and MDF were consistently dominant throughout the analyzed period, jointly accounting for more than 80% of total fruit production in all months. The relative contribution of SDF was minimal, with percentages generally below 15%, and significant differences were observed among cultivars in specific months, particularly for the LDF fraction. ‘Victoria’ exhibited a similar size distribution pattern, characterized by high proportions of MDF and LDF. Significant differences among cultivars were detected within the SDF category in some months, indicating a variable contribution of small fruits depending on the evaluation period.

The results indicate that fruit size distribution varied significantly among cultivars and months, with medium- and large-sized fruits predominating across the evaluated period. The presence of significant differences highlights the influence of both cultivar and temporal variation on blueberry fruit size. It is important to note that, as the productive cycle progressed, the proportion of small-diameter fruits (SDF) increased, suggesting that higher total number of fruits (TNF) was associated with a reduction in individual fruit size. This response is consistent with the well-documented inverse relationship between fruit number and fruit size observed in many crops, which arises from increased competition for assimilates and water among developing fruits (Khurshid & Sanderson, 2023).

In this context, the increase in SDF over time (Figure 5) was accompanied by a concomitant decrease in individual fruit weight (IFW) (Figure 2), indicating that reductions in fruit size directly translated into lower fruit mass. This effect is likely mediated by limitations in carbohydrate availability and water supply, which restrict both cell division and cell expansion during fruit development, ultimately favoring the production of smaller fruits under high fruit load conditions (Hirzel, 2024). Considering that fruit size is one of the main determinants of blueberry consumer acceptance (Babiker et al., 2025), a key component that typically increases proportionally with fruit weight, the observed reduction in fruit size may negatively impact commercial value. Larger fruits are generally preferred by consumers and command higher market prices, while also improving harvesting efficiency (Milivojević et al., 2016; Milić et al., 2018; Hirzel, 2024).

Taken together, these results highlight that cultivar selection based on acclimatization to local agroclimatic conditions is a decisive factor for optimizing yield, thereby strengthening the commercial competitiveness and export potential of blueberries.

Conclusions

1. Blueberry production under high-altitude tropical conditions exhibited a descriptive five-week periodic pattern, with yield peaks coinciding with periods of high export demand.

2. Based on yield performance, physical and fruit quality characteristics, the cultivar Victoria demonstrated the greatest suitability for these conditions.

3. ‘Biloxi’ maintained stable production throughout the season and continues to represent a consistent productive option, whereas ‘Legacy’ showed the lowest overall yield among the evaluated cultivars.

¹

Research developed at Universidad Pedagógica y Tecnológica de Colombia, Tunja, Boyacá, Colombia

Financing statement:

This research was supported by the Ministry of Science, Technology and Innovation of Colombia, the Government of Boyacá, Call for Proposals 865 of 2019: “Closing Technological Gaps in the Agricultural Sector through the Strengthening of I+D+I Capabilities in the Department of Boyacá”, the Universidad Pedagógica y Tecnológica de Colombia (UPTC), and the Vice-rectory for Research and Extension of UPTC (SGI Project 3711), for financing and support during the development of the research.

Data Availability Statement:

There are no supplementary sources.

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Editors:

Toshik Iarley da Silva & Walter Esfrain Pereira

*

Corresponding author - E-mail: [e-mail]

Conflict of interest:

The authors declare no conflicts of interest.

Publication Dates

• Publication in this collection
  28 Sept 2026
• Date of issue
  2026

History

• Received
  29 Oct 2025
• Accepted
  30 June 2026
• Published
  14 Sept 2026