Phenotyping and genetic dissimilarity among strawberry cultivars: Implications for parental selection and crossbreedingAbstract
Strawberry production in Brazil relies predominantly on cultivars developed outside the country. This study aimed to evaluate the agronomic and postharvest performance of strawberry cultivars, quantify the genetic dissimilarity among them using phenotypic data and SSR markers, and perform controlled crosses to initiate a breeding program. Five short-day cultivars and two day-neutral cultivars were evaluated for 18 quantitative traits. A total of 5,291 achenes and 332 seedlings were obtained from 19 successful hybrid combinations. The phenotypic characterization and genetic dissimilarity assessment of the cultivars enabled the identification of seven promising crosses for future breeding efforts.
Keywords:
Plant breeding; molecular markers; Fragaria × ananassa Duch
INTRODUCTION
The cultivated strawberry (Fragaria × ananassa) is an allo-octoploid species (2n = 8x = 56), originating in Europe from the hybridization between the American species F. chiloensis Mill. and F. virginiana Duch. (Zhen and Vance 2024). It produces hermaphroditic flowers and is predominantly cross-pollinated (Dung et al. 2023). Its pseudofruits are highly valued due to their high content of dietary fiber, vitamin C, folic acid, and other antioxidants (Wang et al. 2023, Cianciosi et al. 2025).
Strengthening strawberry breeding programs in Brazil is the most effective strategy to reduce dependence on imported cultivars and to provide genetic material better adapted to Brazilian growing conditions (Delazeri et al. 2024, Costa et al. 2025). One of the main objectives of strawberry breeding programs is to develop photoperiod-insensitive genotypes, enabling the transition from the vegetative to the reproductive phase and allowing off-season production (Rijal et al. 2025). However, day-neutral germplasm remains limited. Hybridization between short-day and day-neutral cultivars can generate heterotic progenies (Galvão et al. 2017).
To achieve this goal, the first step is to characterize the genotypes based on their performance, which may include morphological traits, yield, and postharvest quality of pseudofruits (Silva et al. 2024). In strawberry, marker-assisted selection has been investigated and has proven effective for assessing genetic diversity (Qarni et al. 2022), particularly due to the narrow genetic base of cultivated strawberry. These evaluations enable the identification of potential parental combinations through the application of genetic dissimilarity measures and clustering analyses, ultimately generating high genetic variability among progenies and increasing the potential for selection gain.
Therefore, the objective of this study was to evaluate strawberry cultivars based on agronomic and postharvest traits, estimate the genetic dissimilarity among them using phenotypic and molecular data, and perform hybridizations to support the establishment of a breeding program.
MATERIAL AND METHODS
The agronomic performance of the cultivars was evaluated in an experiment conducted in the municipality of Datas, Minas Gerais, Brazil. The site is located at lat 18° 22' 58.25" S and long 43° 40' 28.1670" W, and alt 1387 m asl. All analyses and hybridization were carried out at the Universidade Federal dos Vales do Jequitinhonha e Mucuri, in Diamantina, Minas Gerais, Brazil.
Agronomic performance of the genotypes and postharvest analyses
Seven strawberry cultivars were used (final parentages): Five were short-day genotypes, namely Camarosa (Douglas × Cal. 85.218-605), Dover (Florida Belle × Fla. 71-189), Festival (Rosa Linda × Oso Grande), Oso Grande (Parker × Cal. 77.3-603), and Tudla (Parker × Chandler), and two were day-neutral genotypes, namely Monterey (Albion × Cal. 97.85-6) and San Andreas (Albion × 97.86-1).
Planting was carried out in raised beds covered with plastic mulch (30-µm polyethylene film). Twenty days after planting, a mini-tunnel was installed using a 75-µm plastic cover at a height of 0.80 m above the bed surface. Irrigation was applied through a drip system, and tensiometers were installed in the beds to monitor soil moisture. All top-dress fertilizations were performed via fertigation. The treatments were arranged in a randomized complete block design and consisted of the seven cultivars, with four replications. Each plot contained twelve plants spaced 0.20 × 0.30 m apart, arranged in two rows.
Harvesting began 75 days after planting and continued for six months. Pseudofruits were harvested twice per week when more than 75% of the surface exhibited mature coloration. Commercial pseudofruits were defined as those with a mass greater than 10 g. The variables evaluated included: total and commercial pseudofruit number; total and commercial production per plant; average mass of total and commercial pseudofruits; and total and commercial yield. For fruit length and diameter, three strawberries were randomly selected within each plot for each cultivar and harvest, and measurements were expressed in millimeters (mm).
For the postharvest evaluation, only strawberries harvested during the fourth month of the harvesting period were used, corresponding to the peak production stage for short-day cultivars, whereas day-neutral cultivars are not influenced by photoperiod. The following variables were assessed: physical traits moisture content and firmness; physicochemical traits pH, titratable acidity, and soluble solids; and chemical traits vitamin C concentration and total reducing sugars, according to the methodology described by Silva et al. (2024).
Genomic DNA extraction, PCR and genetic dissimilarity analysis
Young, fully expanded leaves were collected from each cultivar for molecular analysis. Genomic DNA was extracted following the Doyle and Doyle (1990) CTAB protocol. After obtaining the DNA solution, its concentration was estimated by spectrophotometry based on absorbance readings at 260 nm and 280 nm. The DNA was then diluted to a final concentration of 10 ng µL⁻¹.
Five primer pairs targeting five SSR (Simple Sequence Repeat) loci described by Honjo et al. (2016) were used, because they are highly polymorphic. Each PCR reaction had a final volume of 15 µL, containing 1.5 µL of PCR buffer (10×), 1.0 µL of MgCl₂ (50 mM), 1.2 µL of dNTPs (10 mM), 3.0 µL of DNA (10 ng µL⁻¹), 1.0 µL of each primer pair (10 mM), 0.3 µL of Taq DNA polymerase (5 U µL⁻¹), and 6.0 µL of ultrapure water. The amplification products were separated by electrophoresis on a 2% agarose gel. For each well, 15 µL of DNA were mixed with 4.0 µL of bromophenol blue loading dye and 4.0 µL of GelRed. In addition, 6 µL of a 50 bp molecular weight marker and 4 µL of GelRed were loaded into a separate well. Electrophoresis was performed at 90 V for 180 minutes. The amplified fragments were visualized by image capture using a photodocumentation system under UV light.
The genetic diversity analysis based on phenotypic data was conducted using the mean values of the 18 evaluated characteristics. Genetic distance matrices were generated using Euclidean distance, which was adopted because the number of evaluated traits exceeded the number of cultivars, which could compromise the stable estimation of covariance matrices required for Mahalanobis distance. After estimating the matrices, clustering was performed using the hierarchical UPGMA method. The goodness of fit between the distance matrix and the resulting dendrogram was assessed using the cophenetic correlation coefficient (CCC). Molecular data were analyzed based on the scoring of polymorphic bands detected in at least two samples, each treated as a single character. A binary matrix was generated, assigning a value of 1 for the presence and 0 for the absence. Estimates of genetic dissimilarity were obtained using the Jaccard coefficient. Clustering procedures and the CCC test were performed following the same methodology adopted for the phenotypic data.
A tanglegram was generated to illustrate the similarities and divergences between the associations observed in the two dendrograms, which were aligned facing each other and connected by auxiliary lines to link samples and depict the interaction network. All analyses were performed in R software (version 3.5.2), with support from the cluster, dendextend, Rcmdr, and ade4 packages.
Hybridization and obtaining the F1 populations
Ten seedlings of each cultivar were transplanted into pots in a greenhouse, and the crosses between cultivars were performed as described by Galvão et al. (2017). The removal of achenes from the pseudofruits and seed dormancy breaking were carried out according to the methodology proposed by Campos et al. (2024). The seeds were sown in polystyrene trays filled with Bioplant® commercial substrate. The trays were maintained in a greenhouse under controlled irrigation.
Statistical analysis
For the agronomic performance and postharvest variables, the statistical assumptions were evaluated by subjecting the data to tests for error independence (Durbin-Watson test), normality (Shapiro-Wilk test), and homogeneity of variances (Bartlett’s test). Subsequently, ANOVA was performed. When significant differences were detected, means were compared using Tukey’s test (p < 0.05). All analyses were conducted using R software (R Core Team 2022).
RESULTS AND DISCUSSION
There was a significant difference (p < 0.05) for the variables number of pseudofruits per plant (NP), pseudofruit mass (PM), commercial pseudofruit mass (CPM), diameter (DIAM), and pseudofruit length (LEN) (Table 1). The variables number of commercial pseudofruits (NCP), total production per plant (TPP), commercial production per plant (CPP), total yield per hectare (TYH), and commercial yield per hectare (CYH) showed no differences (Table 1).
Mean values for the number of pseudofruits per plaint (NP), number of commercial pseudofruits (NCP), pseudofruit mass (PM), commercial pseudofruit mass (CPM), pseudofruit diameter (DIAM), pseudofruit length (LEN), total production per plant (TPP), commercial production per plant (CPP), total yield per hectare (TYH), commercial yield per hectare (CYH), pH, soluble solids (SS), titratable acidity (TA); SS/TA ratio; moisture, firmness, vitamin C concentration, and total reducing sugar content of pseudofruits of strawberry cultivars
Regarding NP, the cultivars Dover and Oso Grande were outstanding (Table 1). Dover exhibits this characteristic of producing a high number of fruits per plant compared with other genotypes such as Festival (Luraschi et al. 2025). However, its commercial pseudofruits are lighter, as observed for the CPM variable. Nevertheless, Dover is widely used as a parental genotype in breeding programs due to its resistance to the fungus Colletotrichum acutatum (Silva et al. 2023).
The largest pseudofruits in terms of DIAM were produced by the day-neutral cultivars San Andreas and Monterey. In a semi-hydroponic system, the cultivar San Andreas exhibited the greatest diameter when compared with other genotypes (Silva et al. 2024), demonstrating that in different cultivation systems under tropical conditions, photoperiod-neutral cultivars tend to produce fruits with larger diameters. Pseudofruit length is also an essential variable for determining overall fruit size. The cultivars Festival and Dover exhibited the shortest lengths; as previously discussed, Dover produces many fruits weighing less than 10 g, and consequently with reduced length. The Festival cultivar has likewise been reported to produce smaller pseudofruits and does not respond to biostimulant-based treatments aimed at increasing fruit size and yield in this genotype (Luraschi et al. 2025).
There was a significant difference (p < 0.05) for the variables pH, titratable acidity (TA), soluble solids/titratable acidity ratio (SS/TA), moisture content, and vitamin C concentration among the pseudofruits of the strawberry cultivars (Table 1). The variables soluble solids (SS), firmness, and reducing sugars did not show significant differences among the genotypes evaluated.
Much of the interest in strawberry consumption is primarily due to its high vitamin C content, as strawberries can represent an important dietary source of this vitamin (Kishimoto et al. 2023). The cultivars San Andreas (85.06) and Camarosa (70.36) showed the highest mean values (Table 1). This characteristic has already been reported for these genotypes (Manda-Hakki and Hassanpour 2024), indicating that they may serve as important parents for the introgression of this nutritional trait.
Most cultivars showed similarly high moisture content, whereas San Andreas had the lowest value. It is preferable to select genotypes that produce pseudofruits with lower moisture content, since water content is associated with susceptibility to the main postharvest disease caused by the fungus Botrytis cinerea (Rabasco-Vílches et al. 2025).
Festival exhibited the highest pH, whereas San Andreas showed the lowest value. The acidity indicated by pH is an important physicochemical attribute for defining the intended use of the cultivar, as higher pH values are generally more acceptable for fresh consumption. The balance between sugars and acids in ripe pseudofruits determines their flavor (Ahmed et al. 2024). For TA, the cultivars San Andreas, Camarosa, Tudla, and Oso Grande exhibited statistically similar means, and these genotypes have previously been reported to show higher acidity than other cultivars (Silva et al. 2024). The soluble solids content commonly does not differ among genotypes (Delazeri et al. 2024, Costa et al. 2025); therefore, the SS/TA ratio should be evaluated to select genotypes with superior organoleptic attributes. In this study, the highlight was the cultivar Monterey, which, in addition to being photoperiod-neutral, may serve as a source of alleles associated with pseudofruit flavor.
All SSR loci analyzed exhibited polymorphisms. The marker FxaACA02108C generated three polymorphic bands; FxaAGA02N04C produced six polymorphic bands; FxaAGA01G05C produced four polymorphic and one monomorphic band; FxaAGA21O11C generated six polymorphic and one monomorphic band; and FxaAGA01H04C produced seven polymorphic bands. In total, 26 polymorphic bands and two monomorphic bands were detected. Based on these results, a binary matrix was constructed, and the genetic dissimilarity analysis among the seven strawberry cultivars was performed (Figure 1). The CCC obtained was 0.8618. The cutoff point for determining the number of clusters was established subjectively, considering abrupt changes in the distance values.
Dendrogram representing the genetic divergence among seven strawberry cultivars, obtained by the UPGMA method, using the arithmetic complement of the Jaccard index as a measure of dissimilarity. OG= Oso Grande; SA= San Andreas.
The clusters formed were as follows: Cluster I: Festival; Cluster II: Oso Grande, Dover, and San Andreas; Cluster III: Monterey; and Cluster IV: Camarosa and Tudla. It is evident that the molecular data supported the formation of four clusters. Notably, the cultivar Festival was developed at the University of Florida and does not share the same genetic background as the cultivars Monterey and Camarosa (University of California), nor with Tudla, which originated in Spain. Using SSR markers, the genetic distance between Festival and Camarosa has also been reported as significant (Elec et al. 2024). Therefore, these cultivars can be used in crossing schemes, taking advantage of the agronomic attributes and post-harvest quality of Camarosa (Table 1).
Although only five SSR loci were used, they all exhibited polymorphisms. When analyzing the same number of loci, other sets of SSR and ISSR (Inter-SSR) markers have also proven effective in detecting polymorphisms among strawberry genotypes (Nunes et al. 2013, Lim et al. 2017). Cultivated strawberry has a narrow genetic base. Among the genotypes evaluated, several share common genetic backgrounds, such as Monterey and San Andreas; Festival and Oso Grande; and Oso Grande and Tudla. A noteworthy point is that the SSR set used in the present study was effective in discriminating genetically distinct genotypes.
The CCC value was 0.9851, indicating that the dendrogram adequately reflects the genetic dissimilarity matrix, with a good graphical fit (Figure 2). The cut-off point used to determine the number of clusters was defined subjectively, considering an abrupt change in distances. Formed clusters: Cluster I: Oso Grande; Cluster II: Dover; Cluster III: Camarosa, Monterey, Tudla, Festival, San Andreas. Using Euclidean distance, three clusters were formed. The cultivar Oso Grande remained isolated in a single group, demonstrating a distinct genetic background compared with the other cultivars. This suggests its potential use as a parent in crosses with the day-neutral cultivars Monterey and San Andreas, taking advantage of the strong agronomic performance of Oso Grande (Table 1) and potentially increasing the likelihood of obtaining segregating populations with greater genetic variability, which may favor the exploitation of heterosis in future generations. The same applies to the cultivar Dover, which has already been shown to exhibit substantial genetic variability when compared with other cultivars (Morales et al. 2011).
Dendrogram generated using the UPGMA clustering method for seven strawberry cultivars, based on agronomic and postharvest performance, using Euclidean distance.
When comparing the dissimilarity analyses obtained from the two methods, distinct dendrograms were generated. The tanglegram entanglement value was 0.54 (Figure 3), indicating differences in the distribution of cultivars. It can be observed that none of the cultivars occupied the same position when comparing the two methods. One reason for this is that each dendrogram was generated using different algorithms, and these differences may be related to the mathematical models employed. Methods based on molecular markers versus multivariate analyses of phenotypic means tend to disagree, as they capture different dimensions of genetic variability (Paraiso et al. 2026). Quantitative traits are strongly influenced by the environment and artificial selection, while SSR markers are considered selectively neutral and primarily capture genomic divergence resulting from recombination, genetic drift, and pedigree relationships. Therefore, differences are expected, as they assess distinct components of genetic variability.
Entanglement between seven strawberry cultivars, obtained based on genetic dissimilarity considering morphological and molecular variables. Cam= Camarosa; Fest= Festival; Mont= Monterey; OG= Oso Grande; SA= San Andreas.
Although 18 quantitative traits were considered in the Euclidean distance analysis, SSR markers revealed additional patterns of divergence not captured by the phenotypic dataset due to their ability to detect differences at the genomic level and their effectiveness in the pre-breeding stages of species with low genetic diversity (Bidyananda et al. 2024, Liu et al. 2025). This was clearly reflected in the results: three clusters were formed based on the Euclidean distance analysis, whereas four clusters were identified using the Jaccard index. The limited discrimination observed in the phenotypic analysis may be partially explained by the lack of significant variation in nearly half of the evaluated traits, indicating low phenotypic divergence among cultivars of different origins. The SSR marker set employed in this study proved useful for detecting polymorphisms among the evaluated cultivars and may represent a complementary tool for parental selection. Further studies using SSR markers should be conducted to evaluate a larger number of loci across the strawberry genome.
Crosses were performed in a complete diallel design, with all cultivars crossed with each other. The crosses produced a total of 5.291 achenes, and viable achenes were obtained from 33 of the 42 crosses conducted (Table 2). No achenes were obtained from the following crosses due to fruit abortion: Camarosa × Tudla, Dover × Camarosa, Dover × Festival, Dover × Oso Grande, Dover × San Andreas, Dover × Tudla, Festival × Camarosa, Oso Grande × Camarosa, Monterey × San Andreas, and Tudla × Festival. The cultivar Dover exhibited low achene production when used as the pollen recipient, regardless of the genotype used as the male parent. Further structural and genetic studies are required to identify the cause of this incompatibility.
Number of achenes and number of seedlings from each cross between strawberry cultivars. Cam= Camarosa; Fest= Festival; Mont= Monterey; OG= Oso Grande; SA= San Andreas
The total number of seedlings obtained was 332, studies of genetic dissimilarity are essential for parental selection in a breeding program, aiming to avoid inbreeding depression, exploit heterosis, and increase selection gain. Among the 19 crosses that produced seedlings, in 17 cases the parents were grouped into different clusters in the dendrogram generated from SSR marker data. This result suggests that molecular divergence may be useful for identifying potentially compatible parental combinations. However, the number of seedlings obtained varied substantially among inter-group crosses, indicating that genetic distance alone is insufficient to predict hybridization success and should be considered together with agronomic performance.
No plants were obtained when the two day-neutral cultivars were crossed. Notably, day-neutral germplasm is limited (Feldmann et al. 2024), and both genotypes share the cultivar Albion as a parent. This close genetic relationship may have contributed to the formation of non-viable seeds, although additional studies are necessary to confirm the mechanisms involved.
Considering that these cultivars were allocated to distinct clusters in the SSR-based analysis, successfully produced seedlings, and exhibit the potential to combine desirable traits, the following crosses are proposed for the establishment of progeny trials: Mont × Fest and Tudla × SA, aiming to exploit the day-neutral flowering habit of Mont and SA. In addition, the crosses Tudla × Dover, OG × Tudla, OG × Fest, and Cam × Dover are recommended. These combinations seek to integrate the favorable postharvest attributes of Cam and Tudla, the favorable agronomic performance of OG, and the disease resistance of Dover. The integration of agronomic, postharvest, molecular, and hybridization data allowed the identification of promising parental combinations to support the establishment of a strawberry breeding program under tropical growing conditions.
ACKNOWLEDGEMENTS
We would like to thank the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil, the Conselho Nacional de Desenvolvimento Científico e Tecnológico, and the Fundação de Amparo à Pesquisa de Minas Gerais for granting fellowships.
Data Availability Statement
The datasets generated and/or analyzed during the current research are available from the corresponding author upon reasonable request.
REFERENCES
- Ahmed MM, Asim M, Kaleri AA, Manzoor D, Rajput AA, Laghari R, Khaki SA, Musawwir A, Ullah Z, Ahmad W2024 Biochemical dynamics and quality attributes of strawberry fruits across maturity stages with respect to different preservation methods: Biochemical dynamics and quality attributesFuturistic Biotechnology 4:28-35
- Bidyananda N, Jamir I, Nowakowska K, Varte V, Vendrame WA, Devi RS, Nongdam P2024 Plant genetic diversity studies: Insights from DNA marker analysesInternational Journal of Plant Biology 15:607-640
- Campos AAA, Melo SGF, Silva LR, Oliveira LL, Moreira LC, Sousa TO, Santana RA, Nery MC, Costa MR2024 Dormência em aquênios de morango (Fragaria x ananassa Duch)Contribuciones a las Ciencias Sociales 17:e8771
- Cianciosi D, Diaz YA, Qi Z, Yang B, Chen G, Cassotta M, Villar SG, Lopez LAD, Garcia LR, Hernandez TYF, Zhang D, Mazzoni L, Mezzetti B, Battino M, Giampieri F2025 Strawberry as a health promoter: an evidence-based reviewWhere are we 10 years later? Food & Function 15:5705-5732
- Costa BM, Lima JM, Souza HRC, Santos MFS, Nerbass FR, Kretzschmar AA, Rufato L2025 New strawberry genotypes adapted for the Southern Plateau of Santa Catarina: a study of yield and fruits qualityDiscover Plants 2:192
- Delazeri EE, Schiavon AV, Becker TB, Bonow S, Cantillano RFF, Antunes LEC2024 Physical and quality fruit parameters of new strawberry genotypesBrazilian Journal of Agricultural Research 59:e03462
- Doyle JJ, Doyle JL1990 Isolation of plant DNA from fresh tissueFocus 12:13-15
- Dung CD, Wallace HM, Bai SH, Ogbourne SM, Trueman SJ2023 Fruit size and quality attributes differ between competing self-pollinated and cross-pollinated strawberry fruitInternational Journal of Fruit Science 23:1-12
- Elec VH, Cadorna CAE, Tad-Awan BA, Basquial DA, Dumaslan MR, Rey JD2024 Genetic diversity and population structure of Philippine strawberry germplasm based on genome-wide simple sequence repeat markersBiodiversitas 25:2969-2979
- Feldmann MJ, Pincot DD, Cole GS, Knapp SJ2024 Genetic gains underpinning a little-known strawberry Green RevolutionNature comunication 15:2468
- Galvão AG, Resende LV, Resende JTV, Ferraz AKL, Marodin JC2017 Breeding new improved clones for strawberry production in Brazil. Acta ScientiarumAgronomy 39:149-155
- Honjo M, Nunome T, Kataoka S, Yano T, Hamano M, Yamazaki H, Yamamoto T, Morishita M, Yui S2016 Simple sequence repeat markers linked to the everbearing flowering gene in long-day and day-neutral cultivars of the octoploid cultivated strawberry Fragaria × ananassaEuphytica 209:291-303
- Kishimoto Y, Taguchi C, Iwashima T, Kobayashi T, Kikoku Y, Nishiyama H, Masuda Y, Kongo K2023 Effect of acute strawberry consumption on sérum levels of vitamin C and folic acid, the antioxidant potential of LDL and blood glucose response: a randomised cross-over controlled trialJournal of Nutritional Science 12:1-8
- Lim S, Lee J, Lee HJ, Park K, Kim D, Min SR, Jang WS, Kim T, Kim H2017 The genetic diversity among strawberry breeding resources based on SSRsScientia Agricola 74:226-234
- Liu F, Xi L, Fu N2025 Genome-wide development of simple sequence repeat (SSR) markers at 2-Mb intervals in lotus (Nelumbo Adans.)BMC Genomics 26:4
- Luraschi MCV, Medina NIG, Oviedo VRS, Rotela RB, Enciso-Garay CR2025 Dataset on the use of biostimulants in strawberry cultivation under tropical conditionsData in Brief 60:111445
- Manda‐Hakki K and Hassanpour H2024 Changes in postharvest quality and physiological attributes of strawberry fruits influenced by L‐phenylalanineFood Science and Nutritional 12:10262-10274
- Morales RGF, Resende JTV, Faria MV, Silva PR, Figueiredo AST, Carminatti R2011 Genetic diversity in strawberry cultivars based on morphological characteristicsRevista Ceres 58:323-329
- Nunes CF, Ferreira JL, Generoso AL, Dias MSC, Pasqual M, Cançado GMA2013 The genetic diversity of strawberry (Fragaria ananassa Duch.) hybrids based on ISSR markers. Acta ScientiarumAgronomy 35:443-452
- Paraiso IGM, Sanglard DM, Ramos MB, Valadares NR, Bispo LF, Silva VPV, Guimarães LAD, Nogueira BBAP, Nietsche S2026 Integrating morphological and molecular data to assess genetic diversity in Desert roseCrop Breeding and Applied Biotechnology 26:e53922615
- Qarni A, Muhammad K, Wahab A, Ali A, Khizar C, Ullah I, Kazmi A, Sultana T, Hameed A, Younas M, Rahimi M2022 Molecular characterization of wild and cultivated strawberry (Fragaria x ananassa) trough DNA barcode markersGenetics Research 2022:9249561
- R Core Team2022 R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna.
- Rabasco-Vílches L, Porras-Peréz E, Possas A, Morcillo-Martín R, Pérez-Rodríguez F2025 A predictive approach to reducing strawberry post-harvest waste: Impact of storage conditions and water activity on Botrytis cinerea germinationFood Research International 210:116401
- Rijal S, Mahoney LL, Yang YL, Davis TM2025 Marker-trait association mapping for perpetual flowering habit in octoploid ancestral strawberry, Fragaria virginianaThe Plant Genome 18:e70108
- Silva IFL, Shimizu GD, Santos EL, Corte LE, Zeist AR, Roberto SR, Resende JTV2023 Breeding short-day strawberry genotypes for cultivation in tropical and subtropical regionsHorticulturae 9:614
- Silva LR, Campos AAA, Moreira LC, Barral DM, Andrade GFP, Guimarães AG, Silva IM, Tannure MP, Pinto NAVD, Costa MR, Zanuncio JC2024 Agronomic characteristics and postharvest quality of strawberry in a semi-hydroponic cultivation systemBrazilian Journal of Agricultural Research 59:e03384
- Wang X, Wu L, Qiu J, Qian Y, Wang M2023 Comparative metabolomic analysis of the nutritional aspects from ten cultivars of the strawberry fruitFoods 12:1153
- Zhen F, Vance MW2024 Genomic signatures of strawberry domestication and diversificationThe Plant Cell 36:1622-1636
