Agro-morphological characterization of Coffea canephora (Robusta) genotypes from the INERA Yangambi Coffee Collection, Democratic Republic of the Congo

Agro-morphological characterization of Coffea canephora (Robusta) genotypes from the INERA Yangambi Coffee Collection, Democratic Republic of the Congo | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Agro-morphological characterization of Coffea canephora (Robusta) genotypes from the INERA Yangambi Coffee Collection, Democratic Republic of the Congo Robrecht Bollen, Jean-Léon Kambale, An-Sofie Tas, Benjamin Ntumba Katshela, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5305587/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Meeting rising quality standards while at the same time addressing climate challenges will make the commercial cultivation of Robusta coffee increasingly difficult. Whereas breeding new varieties may be an important part of the solution, such efforts for Robusta lag behind, with much of its genetic diversity still unexplored. By screening existing field genebanks to identify accessions with desirable traits, breeding programs can be significantly facilitated. This study quantifies the morphological diversity and agronomic potential of 70 genotypes from the INERA Coffee Collection in Yangambi, Democratic Republic of the Congo. We measured 29 traits, comprising vegetative, reproductive, tree architecture, and yield traits. Classification models were applied to establish whether these traits could accurately classify genotypes based on their background. Furthermore, the agronomic potential and green bean quality of the genotypes were studied. While significant variation in morphological traits was observed, no combination of traits could reliably predict the genetic background of different genotypes. Genotypes with promising traits for green beans were identified in both ‘Lula’ and ‘Lula’ – Wild hybrids, while promising yield traits were found in ‘Lula’ – Congolese subgroup A hybrids. Additionally, certain ‘Lula’ – Wild hybrids showed low specific leaf area and stomatal density, indicating potential fitness advantages in dry environments, warranting further study. Our findings highlight the agronomic potential of underexplored Robusta coffee genotypes from the Democratic Republic of the Congo and indicate the need for further screening to maximize their value. Robusta DRC germplasm morphology phenotyping agronomy traits Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Coffea canephora , commonly known as Robusta coffee, contributes to more than 40% of the world’s coffee production (ICO 2023 ). However, climate model predictions indicate that climate change implications may be detrimental to coffee cultivation, with the Robusta-producing countries expected to be strongly affected (Bunn et al. 2015 ). The cultivation of Robusta coffee under these climate challenges while at the same time meeting rising quality standards will become increasingly difficult. Yet, Robusta research and breeding is in many respects still in its infancy, with a lot of unknown variation in its gene pool and vast unexplored genetic resources for breeders (WCR 2023 ). In this context, breeding a new generation of climate-smart cultivars (Ferrão et al. 2024b ) could capitalize on the extensive genetic diversity of C. canephora . In general, there has been no long-term and sustained effort to improve the species in more than 30 years (Prakash 2018 ). The past efforts were also constrained due to a lack of adequate genetic resources in countries located outside the zone of origin of C. canephora in combination with political instability in some of the countries within its zone of origin (Montagnon et al. 1998 ; Stoffelen et al. 2019 ). Whereas recent advances in molecular genetics have shed light on the genetic diversity within C. canephora , revealing distinct genotypes that could hold the key to improving coffee production and quality (Loor Solórzano et al. 2017 ; Anagbogu et al. 2019 ; Ngugi and Aluka 2019 ; Kiwuka et al. 2021 ; Verleysen et al. 2023 ), the morphological variability and agronomic potential of these genetic resources are understudied. Morphological screening of genetic resources and identifying accessions with desirable morphological traits can facilitate the genetic variation available for breeding programs (Anthony et al. 2011 ; Kiwuka et al. 2021 ; Paredes-Espinosa et al. 2023 ). Furthermore, Robusta germplasm derived from controlled or natural hybridization events between wild or related semi-wild genotypes can be of exceptional value for selection, as they conserve part of the original variation of the wild population while also introducing new trait variation (Campuzano-Duque and Blair 2022 ). By systematically characterizing morphological traits associated with C. canephora’s genotypes, new phenotypes that may possess unique attributes, such as increased green bean size and production (Ngugi and Aluka 2019 ), climate resilience (Ferrão et al. 2024b ) or enhanced cup quality (Bollen et al. 2024 ) can be discovered and incorporated into breeding pipelines. The desirable morphological traits of coffee trees can range from plant architectural to microscopical traits. For example, the number of nodes per branch is a morphological indicator of higher yield potential (Cilas et al. 2006 ; Spinelli et al. 2018 ). Leaf morpho-anatomical characteristics like the specific leaf area (SLA) and stomatal density of the coffee tree directly reflect water use efficiency and the ability to adapt to different environmental conditions (Dubberstein et al. 2021 ). These traits have also been reported to correlate with the productivity of the coffee tree (Gagliardi et al. 2015 ). SLA and stomatal density are highly sensitive to local environmental conditions, but their plasticity does have a genetic basis (De Kort et al. 2020 ). These leaf traits are valuable to consider when selecting genotypes in a rapidly changing climate (Bertolino et al. 2019 ). In addition to morphological traits of the coffee tree, green bean morphological traits such as screen size distribution, bean weight, and peaberry rate, which refers to the occurrence of rounded coffee seeds resulting from a single developed seed (Cilas and Bouharmont 2005 ; Ngugi and Aluka 2019 ). These attributes are particularly important to green coffee importers and significantly influence market pricing. Larger screen sizes, uniform bean weight, and lower peaberry rates are often associated with higher prices (Lingle and Menon 2017 ; Coffee Quality Institute 2019 ). Traditionally, C. canephora is divided into two genetic groups: the Guinean group, which originates from the rainforests of West Africa, located west of the Dahomey Gap, and the Congolese group, originating from the rainforests of Central Africa (Berthaud and Charrier 1988 ). In the early 20th century, the genetic resources of Central Africa played a key role in Robusta coffee’s breakthrough as a commercial crop (Leplae 1936 ; Van Der Vossen 1985 ). Between 1930 and 1960, l’Institut National pour l’Etude Agronomique du Congo Belge (INEAC) was the leading breeding institute for Robusta coffee, with research stations in Lula, Luki and Yangambi. By the 1950s, Yangambi had become the leading research station for Robusta coffee. In later breeding programs, such as those in West Africa (Montagnon et al. 1998 ) and Vietnam (Vi et al. 2023 ), genetic resources from the Congolese group again played a key role thanks to their valuable agronomic traits and contributions to cup quality (Moschetto et al. 1996 ). This genetic diversity of C. canephora is of utmost importance to the climate adaptation of Robusta coffee (Bramel et al. 2017 ) but has been overlooked and insufficiently studied (Stoffelen et al. 2019 ). Natural C. canephora populations tend to hold high genetic variation, likely due to its self-incompatibility (Moraes et al. 2018 ; Verleysen et al. 2023 ). Conservation of existing Robusta populations is essential for leveraging this diversity to characterize the wide range of variability for quality traits (Prakash 2018 ). The National Agricultural Study and Research Institute (INERA), formerly INEAC, manages the ex-situ coffee collection in Yangambi, DRC. Regional instability, weak governance, and lack of resources resulted in the decimation of the INERA Coffee Collection in Yangambi, and much documentation was lost (Stoffelen et al. 2019 ). In collaboration with Meise Botanic Garden (Belgium), this collection has been rehabilitated, screened, and enriched with new genetic material from the wild and local home gardens (Stoffelen et al. 2019 ; Vanden Abeele et al. 2021 ). A comprehensive genetic analysis of the INERA Coffee Collection revealed a relatively broad genetic diversity and genetic material from the Congolese subgroup A (likely originating from the INERA Research Station in Luki) and subgroup BE (hybrid between subgroups B and E; likely corresponding to ‘Wild’ genotypes from the Yangambi rainforest) were discovered. However, the most abundant materials in the INERA Coffee Collection in Yangambi are known as ‘Lula’ cultivars. These ‘Lula’ cultivars are currently of unknown geographical origin (Verleysen et al. 2023 ). The INERA Coffee Collection also contains individuals with an admixed genotypic background, likely derived from hybridization between ‘Lula’ and subgroup BE (local ‘Wild’) or between ‘Lula’ and subgroup A material. Previous research differentiated eight C. canephora genetic groups corresponding to different geographic origins across West and Central Africa (Merot-L’anthoene et al. 2019 ). The geographical regions of Congolese subgroups A, B, E, and R overlap with the west, north, central, and southeast of DRC, respectively. The INERA Coffee Collection has not been screened for morphological traits and agronomic performance since 1960 (Coste 1955 ). The wild genetic resources introduced from the nearby Yangambi rainforest and the spontaneous, new hybrids found in the collection have never been studied. However, the sensory quality of 70 genotypes, which represented the genetic diversity of the INERA Coffee Collection, has recently been reported (Bollen et al. 2024 ). This study aims to provide an assessment of the morphological diversity and agronomic potential of the same 70 genotypes from the INERA Coffee Collection in Yangambi. We first explore the variation in morphological traits and evaluate if traits can be used to discriminate the genotypes based on genetic background. Secondly, we assess the agronomic potential of the genotypes based on two consecutive harvest years and their green bean traits. The latter in combination with their previously reported sensory quality scores. Overall, we aimed to contribute to the detailed evaluation and baseline phenotyping of underexplored C. canephora genetic resources, laying the groundwork for developing new and improved coffee varieties that are better adapted to the challenges of the coffee market. 2. Materials and methods 2.1. Site description and genetic material The INERA Coffee Collection in Yangambi is located in the Tshopo province, DRC (Lat: 0°50'59.60"N Long: 24°27'50.85"E, 485m altitude). The Yangambi region is classified as Köppen Af (rainforest) with a dry season, average annual precipitation of 1837 mm, and a mean annual temperature of 25.1°C (Kasongo Yakusu et al. 2023 ). The soil of the Yangambi region is classified as Ferralsol, a strongly weathered soil with low nutrient-holding capacity (Jones et al. 2013 ). The coffee collection is maintained as an unshaded monoculture system with coffee trees spaced 2.5x2.5 meters apart (Supplementary Fig. S1 A). Agricultural management practices include mulching, weeding, pruning, and minimal pesticide application without irrigation or fertilization. Genetic analysis of the INERA Coffee Collection by Verleysen et al. ( 2023 ) delineated three subpopulations: Firstly, ‘Lula’ cultivars that originate from the Lula breeding station of the DRC which currently have an unknown geographic origin; Secondly, the INERA accession belonging to Congolese subgroup A originating from another INERA breeding station in Luki; Thirdly, the collection contains local Wild accessions introduced from the nearby Yangambi rainforest (Supplementary Fig. S1 B). Additionally, admixed genotypes were identified, likely ‘Lula’ – Wild hybrids and ‘Lula’ – Congolese subgroup A hybrids (Supplementary Fig. S1 C). Recently, 70 genotypes from the INERA Coffee Collection were selected and studied for their sensory quality. The selected genotypes were subdivided into five classes: Wild (n = 3), ‘Lula’ – Wild hybrids (n = 14), ‘Lula’ (n = 39), ‘Lula’ – Congolese subgroup A hybrids (n = 13), and Congolese subgroup A (n = 1) (Bollen et al. 2024 ). The same genotypes and clonal material were selected for our study. One to three clones per genotype, based on the genetic fingerprinting of Verleysen et al. ( 2023 ), were used in this study (Supplementary Table S1 ). The age of the trees was based on the planting dates from recent INERA documentation. 2.2. Morphological traits A set of 29 morphological traits (Table 1 ) was measured on 118 individual C. canephora coffee trees comprising 70 genotypes. 2.2.1. Foliar traits Three healthy plagiotropic branches were sampled from the middle portion of each tree's main stem. Herbarium vouchers were made following Degreef et al. ( 2004 ) and transported to Meise Botanic Garden. The stipule and petiole length were measured on the vouchers with an electronic caliper. The leaf length, width, area, and apex were measured from digital scans of the leaves using the ImageJ software (v15.3) (Schneider et al. 2012 ). The plant vouchers were dried at 60°C for 24 hours to achieve homogeneous dried plant material. The specific leaf area (SLA) and stomatal density (stomata) were determined from five dried leaf samples. SLA was calculated from the individual leaf area and weight. The stomatal density was determined using the workflow of Meeus et al. ( 2020 ). Two imprints of the abaxial side of each leaf were made using the transparent varnish method. The imprints were made in the middle of the leaf, one on each side of the main nerve. Three photomicrographs of 1.600 × 1.200 pixels were taken per leaf print (dimensions = 344×258 µm; area view field = 0.09 mm²) using a Keyence VH-5000 digital microscope (v1.5.1.1) (Keyence International, Mechelen) with full coaxial lightning and default factory settings for shutter speed at ×1.000 lens magnification (VH-Z250R). Detection of stomata on the photomicrographs was achieved using a fine-tuned deep-learning model. We started from an open-source YOLOv8 deep learning detection model developed by Ultralytics ( https://github.com/ultralytics/ultralytics ). This model was pre-trained with the COCO dataset (Lin et al. 2014 ) and then fine-tuned on 14 photomicrographs of C. canephora for a total of 392 stomata. This is in line with previous recommendations of Meeus et al. ( 2020 ) who recommend training on at least 250 stomata to achieve accurate detection results. Standard data augmentation techniques, such as rotation, flipping, and mosaic augmentation were applied to the training photomicrographs. This species-specific YOLOv8 object detection model was accurate, with a precision of 0.99 and a recall value of 0.98 across 100 photomicrographs of a test set. The stomata count was converted to the number of stomata per square millimeter to obtain the stomatal density. 2.2.2. Reproductive traits During the main harvest season of November 2021, fresh, developed coffee cherries were handpicked per individual coffee tree. A picture of five coffee cherries was taken for each coffee tree, including a standard ruler (mm). Secondly, after pulping the coffee cherries, a second picture of the ten fresh seeds was taken. Lastly, a transverse cut was made on five coffee cherries for a third picture. During the main flowering period of February 2022, five fully developed flowers were harvested per individual tree. A photo was taken of the lateral and apical view of the flower (Silva et al. 2021 ). An Olympus TG-6 camera was used for all pictures (Olympus Corporation, Tokyo). The reproductive traits were measured on the resulting images (4000×3000 pixels) using the ImageJ software (v15.3) (Schneider et al. 2012 ). 2.2.3. Architectural and yield traits The architecture and yield of the coffee trees were measured on-site. The coffee cherry yield was estimated using the method proposed by Cilas et al. ( 2006 ). Starting from the top of the tree's main stem, two fructifying plagiotropic branches were selected at orthotropic nodes 5, 15, and 25, representing three levels of coffee tree production. For each of the six fructifying branches, the branch length, number of nodes, number of fructifying nodes, and number of coffee cherries per individual node were counted, resulting in an estimation of the tree's coffee cherry production. Table 1 List of morphological traits measured in 70 C. canephora genotypes from the INERA Coffee Collection, DRC Foliar Method of evaluation Leaf length (mm) Average length of five leaves from the petiole end to the apex Leaf width (mm) Average width of five leaves at the widest section Leaf apex (mm) Average length of the apex to the convex transition of the leaf margin for five leaves Stipule length (mm) Average length of ten stipules measured from the basis to the tip Petiole length (mm) Average length of ten petioles measured from the basis to the insertion with the blade SLA (mm²/g) Average specific leaf area of five adult leaves Stomatal density (number/mm²) Average stomatal density in an area of one mm² from fifteen leaf prints Reproductive Cherry length (mm) Average length of five fully-developed coffee cherries Cherry width (mm) Average width of five fully-developed coffee cherries Seed length (mm) Average length of ten fully-developed coffee seeds Seed width (mm) Average width of ten fully-developed coffee seeds Cherry thickness (mm) Average thickness of five fully-developed coffee cherries Seed thickness (mm) Average thickness of ten fully-developed coffee seeds Pulp thickness (mm) Average thickness of the mesocarp from five fully-developed coffee cherries Petal length (mm) Average length of five fully-developed coffee flower petals Petal width (mm) Average width of five fully-developed coffee flower petals Anther length (mm) Average length of five fully-developed coffee flower anthers Corolla tube length (mm) Average length of five fully-developed coffee flower corolla tubes Architecture and yield Branch length (cm) The average length of four adult plagiotropic branches in the middle of the tree Internode length (cm) Plagiotropic branch length divided by the nodes averaged over four branches Plagiotropic branches Amount of plagiotropic branches on the main stem Tree height (cm) Height of the coffee tree Tree width (cm) Width of the coffee tree at the widest section Fruct. nodes Average amount of fructifying nodes across levels 5, 15, and 25 Cherry per node Average amount of coffee cherries per fructifying node across levels 5, 15, and 25 Fruct. node ratio Ratio of fructifying nodes to total nodes across levels 5, 15 and 25 Max. node Largest amount of coffee cherries recorded at a node Fruct. branch ratio Ratio of fructifying plagiotropic branches to total branches on the main stem Total cherries Amount of total cherries across the three levels 2.3. Agronomic potential of the genotypes To estimate the genotypes' agronomic potential, the following morphological traits were measured and averaged over two harvest years (2021 and 2022) to account for biennial bearing (Cilas et al. 2011 ): fructifying nodes, cherry per node, fructifying branches, maximum node, and total cherries. When applicable, an average was made across the genotype's clonal trees. Additionally, the following green bean traits were measured at the genotype level, sourced from green bean samples used in the sensory quality study by Bollen et al. ( 2024 ): Screen 15+ (%): Dimension distribution using a perforated screen plate with a 15/64-inch diameter screen. The green beans retained by screen size 15 were weighed and recorded as a percentage; Screen 17+ (%): Dimension distribution using a perforated screen plate with a 17/64-inch diameter screen. The green beans retained by screen size 17 were weighed and recorded as a percentage; 100 bean weight (BW) (g): The weight of 100 dried green beans; Peaberry: The number of rounded coffee beans (peaberries) was counted in 50 g of green beans. The sensory quality of the selected genotypes was previously evaluated through coffee cupping with the Fine Robusta Standards and Protocols (Coffee Quality Institute 2019 ), resulting in a total score on a 100-point scale (Bollen et al. 2024 ). The reported total cupping score was included as metadata in our study (Fig. 4 ). 2.4. Data analysis Descriptive statistics and correlations between traits were calculated across the 118 individual coffee trees. Spearman rank correlation was employed as not all traits exhibited a normal distribution. The significance of the Spearman correlations was Bonferroni corrected for multiple testing. Principal component analysis (PCA) of the traits was performed on the genotype level, i.e., based on the average of the clonal trees when applicable. Data were scaled before multivariate analysis. A linear discriminant analysis (LDA) and a random forest model were then applied to evaluate if the genotypes could be discriminated by their genetic background based on morphological traits. Because the ‘Lula’ genotypes were overrepresented in this study, a subset of genotypes was created based on the subpopulations described by Verleysen et al. ( 2023 ): Wild, ‘Lula’, and Congolese subgroup A (Supplementary Fig. S1 C). Based on their fastSTRUCTURE values, the genotypes with the strongest ‘Lula’ identity ( n = 8), Wild identity ( n = 6), and Congolese subgroup A identity ( n = 8) were selected for this classification study (Supplementary Table S1 ). The classification accuracy of the LDA model was evaluated with a 10-fold cross-validation. The random forest classification model was trained with 500 trees, and four variables were tested at each split. The correlation matrix was constructed using the corrplot package (v0.84). PCA was applied using the factoextra package (v1.0.7), LDA using the MASS package (v7.3.60), and the Random Forest using the randomForest package (v4.7.1) in R (v4.2.3). 3. Results 3.1. Variation and correlation of morphological traits at the individual tree level There was considerable variation in the morphological traits across the 118 measured trees (Table 2 ). The coffee cherry and seed dimensions exhibited the least variation, with seed width having the lowest coefficient of variation of the studied traits. Most variation was observed in the yield traits, with the total cherries having the highest coefficient of variation in the studied traits. Table 2 Mean ± standard deviation (SD) and coefficient of variation (CV) of the morphological traits of the 118 individual coffee trees comprising 70 Robusta genotypes from the INERA Coffee Collection, DRC Trait Mean ± SD CV (continued) Mean ± SD CV Leaf length (mm) 180.95 ± 21.42 11.84 Petal length (mm) 10.19 ± 2.13 20.89 Leaf width (mm) 73.88 ± 9.62 13.02 Petal width (mm) 2.75 ± 0.49 17.90 Leaf apex (mm) 14.46 ± 3.84 26.53 Anther length (mm) 7.56 ± 1.17 15.42 Stipule length (mm) 6.42 ± 0.76 11.90 Corolla tube length (mm) 8.92 ± 1.71 19.14 Petiole length (mm) 12.86 ± 2.16 16.79 Branch length (cm) 85.21 ± 17.87 20.97 SLA (mm²/g) 8167 ± 1138 13.94 Internode length (cm) 4.81 ± 0.66 13.79 Stomatal density (number/mm²) 347.39 ± 60.27 17.35 Plagiotropic branches 45.95 ± 10.84 23.58 Cherry length (mm) 13.84 ± 1.18 8.50 Tree height (cm) 277.24 ± 59.56 21.48 Cherry width (mm) 13.53 ± 1.04 7.69 Tree width (cm) 169.76 ± 32.71 19.27 Seed length (mm) 11.31 ± 0.97 8.58 Fruct. nodes 36.61 ± 10.99 30.01 Seed width (mm) 8.78 ± 0.64 7.33 Cherry per node 9.91 ± 3.50 35.30 Cherry thickness (mm) 11.37 ± 0.98 8.66 Fruct. node ratio 0.41 ± 0.11 26.07 Seed thickness (mm) 5.06 ± 0.47 9.37 Max. node 25.19 ± 9.04 35.89 Pulp thickness (mm) 1.21 ± 0.24 19.38 Fruct. branch ratio 0.62 ± 0.13 20.20 Total cherries 379.12 ± 206.01 54.34 Significant Spearman correlations were observed between the studied morphological traits (Fig. 1 ). Leaf length correlated with SLA ( r = 0.46) and with stomatal density ( r = -0.47). Similarly, leaf width correlated significantly with SLA ( r = 0.54) and with stomatal density ( r = -0.48). SLA and stomatal density were negatively correlated ( r = -0.52). Larger coffee cherry dimensions resulted in larger coffee seed dimensions. Flower petal length correlated significantly with petal width ( r = 0.49) and anther length ( r = 0.67). Anther length correlated significantly with the corolla tube length ( r = 0.44). The number of plagiotropic branches on the main stem correlated significantly with the branch length ( r = 0.47) and tree width ( r = 0.44). The number of fructifying nodes correlated with cherries per node ( r = 0.46). The maximum node correlated with the number of cherries per node ( r = 0.77). To reduce the multicollinearity when analyzing the variation in morphological traits of the genotypes the leaf length and width variables were combined into a leaf length-to-width ratio variable; the cherry length, width, and thickness variables were omitted, given their strong collinearity with the seed dimension variables; The width of the coffee tree was omitted as it correlated strongly with branch length. The maximum node and total cherries variables were omitted as they correlated strongly with the fructifying nodes and cherry per node variables. The first two principal components explained 28.13% of the variation in data. No clear separation of the genotypes based on their genetic background was observed (Fig. 2 ). In the first dimension, the samples were grouped based on productive traits (fructifying nodes, cherry per node, and branch length) and floral traits (petal, anther, and corolla tube dimensions). The separation in the second dimension was explained mainly by the coffee seed dimensions. The majority of the ‘Lula’ – subgroup A hybrid genotypes were positioned more along the positive axis of the first dimensions, exhibiting a higher number of cherries per node and fructifying node values. 3.2. Discrimination of the genetic background based on morphological traits Discrimination of the genotypes by genetic background based on their morphological traits was evaluated through an LDA and random forest classifier on a subset of genotypes. The LDA separated the genotypes with the strongest Wild identity from genotypes with the strongest ‘Lula’ and Congolese subgroup A identity by the first linear discriminant (LD1) (Fig. 3 ). The anther length, petal length, seed thickness, SLA, and stomatal density were the most important traits of the first LD. The ‘Lula’ genotypes were separated from the genotypes with the strongest Congolese subgroup A identity by LD2. The seed thickness, SLA, seed length, and stomatal density were the most important traits of the second LD. The classification accuracy of the LDA model after 10-fold cross-validation was 43.33%. The random forest model reported a prediction error of 59.09% for the classification of the genotypes. The class error rates were 0.50, 0.50, and 0.83 for the ‘Lula’, Congolese subgroup A, and Wild identity groups, respectively. The most important traits in the random forest classifier were, in decreasing order, the seed width, number of fructifying nodes, petiole length, stipule length, and anther length (Supplementary Fig. S2 ). 3.3. Agronomic potential of the genotypes The agronomic potential of the genotypes was assessed based on the morphological yield traits over two consecutive harvest years, the green bean traits, and the tree architecture, with additional attention to the sensory quality of the coffee (Fig. 4 ). Screen size distribution ranged from 8% retained by screen 15 to 92% retained by screen 17. The weight of 100 green beans ranged from 8.70 g to 19.77 g. The highest and widest genotypes recorded were 425 and 255 cm, respectively. The number of fructifying branches on the main stem ranged from 11 to 63. The number of fructifying nodes across the three measured coffee tree levels ranged from 14 to 70. The average amount of cherries per node across the three levels ranged from 4 to 17. The highest amount of coffee cherries recorded at a single node was 52. The total amount of coffee cherries counted across the three levels of the coffee tree ranged from 70 to 816. 4. Discussion Phenotyping the genetic diversity of C. canephora germplasm collections, especially in the geographical origins of the species, is an important first step in discovering promising genetic material for developing new coffee varieties. Our exploration of the morphological diversity and agronomic potential of the understudied C. canephora genetic resources from the DRC revealed a substantial variation in morphological traits. The yield traits exhibited the most variation, while the coffee cherry and seed dimensions exhibited the least variation. The variation in morphological traits was not consistent with the genetic background, and no morphological traits were found to classify and predict the genetic background of the genotype accurately. The variation in agronomic and quality-relevant traits was substantial, with the ‘Lula’, ‘Lula’ – Wild hybrid, and ‘Lula’ – subgroup A hybrid material containing promising genotypes for further evaluation. Regarding the morphological diversity of the genotypes, substantial variation in all traits was found, especially in the yield traits. Akpertey et al. ( 2019 ) studied 71 genotypes from germplasm in Ghana and reported similar coefficients of variation for the leaf and coffee cherry dimensions. Similarly, they reported a higher variation in tree architecture and branch traits compared to the coffee leaf and cherry traits. Previous research on Conilon varieties (Congolese subgroup A) reported less variation for anther and corolla tube length compared to our study but similar positive correlations between petal, anther, and corolla tube lengths (Silva et al. 2021 ). We found a substantial range in SLA and stomatal density across the studied genotypes but this variation was not consistent with the genetic background of the respective genotypes. Dubberstein et al. ( 2021 ) also reported a wide morphological diversity in stomatal density for Conilon genotypes. In our study, no significant correlation was found between SLA and coffee cherry production or between stomatal density and coffee cherry production. In comparison, previous research reported a correlation between specific leaf area and coffee plant yield (Gagliardi et al. 2015 ). In line with Pompelli et al. ( 2010 ) on C. arabica and also previously reported in other plant species (Xu and Zhou 2008 ; Sun et al. 2014 ), we observed a negative correlation between SLA and stomatal density. The environmental plasticity of these two functional leaf traits likely has a genetic basis (De Kort et al. 2020 ) and plays an important role in the climate adaptation of food crops (Bertolino et al. 2019 ). Genotypes with a low SLA and stomatal density can be expected to have a fitness advantage under dry environmental conditions (Poorter et al. 2009 ; Bertolino et al. 2019 ). This trait combination was found in the ‘Lula’ – Wild hybrids G0023 and G0067 (Supplementary Table S1 ), which both, interestingly, also scored a high sensory quality, with G0067 exhibiting a large screen size (Fig. 4 ). Accurate prediction of the genetic background of the genotypes based on their morphological traits was not achieved. Cross-validation of the LDA model revealed a low prediction accuracy (43.33%) for the constructed linear discriminants. The classification errors for genetic background in both the LDA and random forest models were high, and the reported morphological traits of importance in the two classification models were not the same. No combination of morphological traits was able to accurately classify an accession according to its genetic background, consistent with previous research on Robusta genotypes. Akpertey et al. ( 2019 ) reported high coefficients of variations for most studied traits in Robusta genotypes, with origins in Togo, Ghana, Cameroon, and the Ivory Coast and the grouping of the genotypes based on morphological traits was inconsistent with the genetic background. Similarly, Robusta genotypes from Uganda exhibited morphological variation but this was not predictive for their genetic background (Ngugi and Aluka 2019 ). Advancements in molecular research have increased the efficiency and cost-effectiveness of genotyping plant material, making it easier to analyze the genetic composition of Robusta coffee's genetic resources. However, classifying Robusta genetic material based on morphological traits still requires further evaluation, as it could provide a practical method for screening genetic resources for desirable traits. Additionally, access to molecular research is often limited in the regions where Robusta's genetic resources are found. A substantial variation in traits linked to agronomic performance was observed across the genotypes. If the green bean traits and sensory quality would be prioritized, promising material was found within the ‘Lula’ – Wild hybrid and ‘Lula’ material. Genotypes G0067 and G0035 from the ‘Lula’ – Wild and genotype G0077 from the ‘Lula’ class had a total sensory quality score of 83 points or higher, and were characterized by large coffee beans (> 50% retained by screen 17), a high 100 bean weight, and a moderate peaberry rate. The relatively large coffee bean size of the ‘Lula’ – Wild material, combined with their high sensory quality scores, indicates the potential of this hybrid material for further breeding efforts. These genotypes exhibited a tree height above three meters, an agronomic trait that is less desired but can be managed through adapting cultivation practices. As a comparison, screen sizes of Ugandan Robusta accessions averaged around screen 15, but with a high level of morphological and bean size variability (Ngugi and Aluka 2019 ). Also genotype G0240 from the ‘Lula’ class is noteworthy, as it had the largest screen size distribution in the studied material, with good coffee production and sensory quality. If agronomic production would be prioritized, promising material was found within the ‘Lula’ – subgroup A class. The PCA indicated that the ‘Lula’ – subgroup A hybrids were associated with higher coffee cherry production. More specifically, genotypes G0086 and G0010 had the highest total cherry production across two harvest years, with G0010 recording a maximum node of 52 coffee cherries. Genotype G0102 recorded the most fructifying nodes, which correlates with cumulative coffee production over time (Cilas et al. 2006 ). The relatively high number of fructifying nodes in these ‘Lula’ – subgroup hybrids is an interesting characteristic. Congolese subgroup A material has its geographical origin in Gabon, the Republic of the Congo, and western DRC, from which the ‘Petit-Kwilu’ variety and the Conilon material were derived. These coffee varieties exhibit high coffee production but small coffee bean dimensions, whereas the ‘Lula’ material is known for its larger green bean size. Genotype G0087 (subgroup A) originated from the Luki region of western DRC and was labeled as ‘Petit-Kwilu’ material, exhibiting a small coffee bean size. Hybridization between the Congolese subgroup A and ‘Lula’ material could result in coffees with a high productivity and larger bean size, as was suggested by genotypes G0222, G0220, and G0010. On the other hand, genotype G0073 from this hybrid class scored exceptionally high in Total score and performed well in agronomic production but lagged in coffee bean size. These results indicate a promising starting point for future breeding activities between these Congolese subgroup A and ‘Lula’ material. Inter-varietal hybridization between Congolese subgroup A (‘Petit-Kwilu’) and subgroup BE (‘Lula’) for the development of drought-resistant varieties with large beans was initiated at INERA (Montagnon et al. 1998 ) but it was never completed. Our ‘Lula’ – subgroup A hybrids are possibly the descendants of these initial inter-varietal crossing experiments. The direct parents of these hybrids are not known, so the level of heterosis could not be evaluated. Our results indicate that specific genotypes from ‘Lula’ – subgroup A class exhibited large bean sizes and performed well on sensory quality. It would be interesting to evaluate their performance in dry environments as a considerable range in SLA and stomatal density was reported in this hybrid class (Supplementary Table S1 ). The discovered genotypes with promising morphological traits could be used as parent plants in breeding programs. In this context, no hybrid crossings between the Wild and Congolese subgroup A were found in the INERA Coffee Collection (Verleysen et al. 2023 ). Future research could undertake targeted crossings between these two genetic classes, as the genetic distance of the parental material correlates with phenotypic performance (Ferrão et al. 2024a ). Previous research on hybrid crossings between Conilon (Congolese subgroup A) and a Robusta variety (Congolese subgroup E) reported better agronomic and yield performance, with a high expression of heterosis in the hybrid material (Teixeira et al. 2017 ; Carvalho et al. 2019 ; Alkimim et al. 2021 ). Similar hybrid vigor observations were made in the hybrid material of Ghanaian accessions (Akpertey et al. 2022 ) and Conilon varieties of Brazil (Ferrão et al. 2024a ). These breeding experiments mainly focused on the already established genetic resources from the same origin groups. The ongoing introduction of local wild materials from the Yangambi rainforest into the INERA Coffee Collection will result in newly captured genetic diversity available for crossing experiments. These hybrids could potentially exhibit a desirable combination of traits. The multiplication of accessions at the INERA Coffee Collection through seedlings and open pollination resulted in the hybridization of the initial genetic material into many new and unique genetic fingerprints (Verleysen et al. 2023 ). This resulted in a substantial variation in sensory quality, and promising sensory profiles within the different genetic classes of the collection were discovered (Bollen et al. 2024 ). Similar observations were made in terms of agronomic potential. Promising material was found within the ‘Lula’ – Wild hybrid, ‘Lula’, and ‘Lula’ – subgroup A hybrid material. Ngugi and Aluka ( 2019 ) reported similar observations in agronomic traits from crossings between cultivars and local wild material in Ugandan germplasm. The Wild and Congolese subgroup A material did not perform well, but their sample sizes were too small to draw solid conclusions. A more comprehensive screening of the INERA Coffee Collection is advised in this context, as only 70 of the 263 unique genetic identities reported in Verleysen et al. ( 2023 ) were studied. Furthermore, the initial genetic analysis included only a fraction of the collection’s total genetic resources, leaving many opportunities for further exploration. Additionally, the continuous introduction of accessions from plantations and wild populations expands the available genetic resources for phenotypic screening (Verleysen et al. 2024 ). 5. Conclusion Our study explored the morphological diversity and agronomic potential of Robusta genetic resources from the Democratic Republic of the Congo, arguably the main center of Robusta diversity. Considerable variation in morphological traits was observed, but no combination of morphological traits was found to classify and predict the genetic background of the genotype accurately. The yield traits exhibited the most variation, while the coffee cherry and seed dimensions exhibited the least variation. Genotypes with desired traits for green beans were identified in both ‘Lula’ and ‘Lula’ – Wild hybrids, while promising yield traits were found in ‘Lula’ – Congolese subgroup A hybrids. Promising genotypes with low SLA and stomatal density were found in the ‘Lula’ – Wild class, which could have a fitness advantage in dry environments warranting further research. Future research should focus on designing field trials to explore the promising material. These trials should ideally be conducted at multiple locations with different environmental conditions. Crossing Wild and Congolese subgroup A material is proposed since hybrids from these two genetic classes are currently non-existent. Our findings highlight the agronomic potential of the underexplored Robusta coffee genetic resources from the Democratic Republic of the Congo. They also stress the importance of conserving and thoroughly screening these genetic resources. Declarations CONFLICT OF INTEREST The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. FUNDING This study was funded by the Belgian Science Policy Office (BELSPO) contract no. B2/191/P1/COFFEEBRIDGE (CoffeeBridge Project) of the Belgian Research Action through Interdisciplinary Networks (BRAIN-be 2.0). Meise Botanic Garden coordinated the rehabilitation and characterization of the INERA Coffee Collection with the support of the European Union’s 11th Development Fund (FED/2016/381–145) through the “Formation, Recherche et Environnement dans la Tshopo” (FORETS) project implemented by the Center for International Forestry Research (CIFOR). Local infrastructure was created in the framework of the Climcoff project funded by the Flemish Government, Dept. Environment – SIDO. Author Contribution R. B.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft. J. K.: Data curation, Formal analysis, Investigation, Methodology. A. T.: Data curation, Formal analysis, Investigation, Methodology, Writing – original draft. B. N. K.: Data curation, Formal analysis, Investigation, Methodology. E.A.T.: Data curation, Formal analysis, Investigation, Methodology, Project administration. F. W.: Conceptualization, Investigation, Methodology, Writing – review & editing. F. V.: Conceptualization, Formal analysis, Investigation, Methodology, Writing – review & editing. O.H.: Funding acquisition, Project administration, Supervision, Writing – review & editing. P.S.: Funding acquisition, Project administration, Supervision, Writing – review & editing. Acknowledgement We want to thank the Institut National pour l’Etude et la Recherche Agronomiques (INERA) for giving access to their collection and infrastructure; CIFOR for the logistic and administrative support during fieldwork in the Democratic Republic of the Congo; The Flemish Government for their support; Karin Vansteenwegen, Koen Wabbes, Rachel Ndezu and Jean-Claude Kazadi for their assistance with the morphological traits study. We would also like to thank the Congolese Ministère de l’Environnement et Développement Durable (MEDD) for granting research permits N°004/ANCCB-RDC/SG-EDD/BTB/2021, N°014/ANCCB-RDC/SG-EDD/BTB/11/2021, and N°025/ ANCCB-RDC/SG-EDD/BTB/11/2022. Data Availability The morphological data of the genotypes can be found in the Supplementary Materials. Individual measurements and information on the YOLOv8 deep learning detection model can be provided upon request. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5305587","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":370674266,"identity":"0d5ca031-08fe-4039-81dd-a853faf8eb42","order_by":0,"name":"Robrecht 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the 118 individual coffee trees comprising 70 Robusta genotypes\u003cstrong\u003e \u003c/strong\u003efrom the INERA Coffee Collection, DRC.\u003cstrong\u003e \u003c/strong\u003eSignificant Spearman correlations after Bonferroni correction are indicated by boxes with black edges\u003c/p\u003e","description":"","filename":"Fig1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5305587/v1/415ef42da4db7b062b9aaf43.jpeg"},{"id":67648080,"identity":"f2d8d2be-a9c4-41fb-aef5-9ba6475aef9a","added_by":"auto","created_at":"2024-10-28 11:20:03","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47349,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplot of PC1 and PC2 for the morphological traits of the 70 Robusta genotypes from the INERA Coffee Collection, DRC. The ten trait variables that contributed most to the variation captured by the first two principal components are shown.\u003cstrong\u003e \u003c/strong\u003eGenetic classes of the genotypes are Wild, ‘Lula’ – Wild hybrid, ‘Lula’, ‘Lula’ – Congolese subgroup A hybrid, and Congolese subgroup A\u003c/p\u003e","description":"","filename":"Fig2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5305587/v1/84fba0f65d19504ce7f18b50.jpeg"},{"id":67648079,"identity":"d9d8d3c2-72b3-45e4-a136-a5ac7a334f21","added_by":"auto","created_at":"2024-10-28 11:20:03","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17358,"visible":true,"origin":"","legend":"\u003cp\u003eLDA conducted on the morphological traits achieving a classification accuracy of 43.33%. A plot of LD1 and LD2 separates the three genetic backgrounds (left) with the morphological traits most important to LD1 and LD2 (right). Genotypes with the highest ‘Lula’ genetic identity (\u003cem\u003en\u003c/em\u003e = 8), Wild genetic identity (\u003cem\u003en\u003c/em\u003e = 6), and Congolese subgroup A genetic identity (\u003cem\u003en\u003c/em\u003e = 8) were selected for classification based on the population genetic structure analysis of Verleysen et al. (2023)\u003c/p\u003e","description":"","filename":"Fig3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5305587/v1/ae2020179a89d2c44b0b5851.jpeg"},{"id":67649225,"identity":"bd72d2ac-e3e1-4173-9204-7cb7c663e6fc","added_by":"auto","created_at":"2024-10-28 11:28:04","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":272211,"visible":true,"origin":"","legend":"\u003cp\u003eAgronomic and quality-relevant traits of 70 genotypes from the INERA Coffee Collection, DRC: Total cupping score, fraction of 50 g green beans retained by screen 15 and 17, 100 green beans weight (BW), number of peaberries in 50 g of green beans, coffee tree height and width (cm), number of fructifying branches on the main stem and the number of fructifying nodes, average cherries per node, maximum cherries per node and total cherries from six measured branches. Genetic structure based on the fastSTRUCTURE bar plot representing three genetic clusters (K = 3). Colors define subpopulations: orange (Wild), blue (Congolese subgroup A), and red (‘Lula’ cultivars) (Verleysen et al. 2023). Total sensory quality score of the genotype based on Bollen et al. (2024)\u003c/p\u003e","description":"","filename":"Fig4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5305587/v1/806d4f2f988ffca8d1a9a64e.jpeg"},{"id":67864766,"identity":"b51b617c-2bf9-4516-bb57-ae392fd89ad8","added_by":"auto","created_at":"2024-10-30 14:01:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":951311,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5305587/v1/70df87d8-41f4-4b2d-9617-d9866ca8140f.pdf"},{"id":67648083,"identity":"23118e14-ba56-44fa-8d7d-2ec90801327f","added_by":"auto","created_at":"2024-10-28 11:20:04","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1765814,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresS1S2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5305587/v1/4f86ba4af220be5a2b106f67.docx"},{"id":67648085,"identity":"1ee9b363-0bd1-4aaa-91f8-5c5ae7ecbed8","added_by":"auto","created_at":"2024-10-28 11:20:04","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":35329,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5305587/v1/f63aa3b96ab076002110163e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Agro-morphological characterization of Coffea canephora (Robusta) genotypes from the INERA Yangambi Coffee Collection, Democratic Republic of the Congo","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003eCoffea canephora\u003c/em\u003e, commonly known as Robusta coffee, contributes to more than 40% of the world\u0026rsquo;s coffee production (ICO \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, climate model predictions indicate that climate change implications may be detrimental to coffee cultivation, with the Robusta-producing countries expected to be strongly affected (Bunn et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The cultivation of Robusta coffee under these climate challenges while at the same time meeting rising quality standards will become increasingly difficult. Yet, Robusta research and breeding is in many respects still in its infancy, with a lot of unknown variation in its gene pool and vast unexplored genetic resources for breeders (WCR \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this context, breeding a new generation of climate-smart cultivars (Ferr\u0026atilde;o et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e) could capitalize on the extensive genetic diversity of \u003cem\u003eC. canephora\u003c/em\u003e. In general, there has been no long-term and sustained effort to improve the species in more than 30 years (Prakash \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The past efforts were also constrained due to a lack of adequate genetic resources in countries located outside the zone of origin of \u003cem\u003eC. canephora\u003c/em\u003e in combination with political instability in some of the countries within its zone of origin (Montagnon et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Stoffelen et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhereas recent advances in molecular genetics have shed light on the genetic diversity within \u003cem\u003eC. canephora\u003c/em\u003e, revealing distinct genotypes that could hold the key to improving coffee production and quality (Loor Sol\u0026oacute;rzano et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Anagbogu et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ngugi and Aluka \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kiwuka et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Verleysen et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the morphological variability and agronomic potential of these genetic resources are understudied. Morphological screening of genetic resources and identifying accessions with desirable morphological traits can facilitate the genetic variation available for breeding programs (Anthony et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kiwuka et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Paredes-Espinosa et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, Robusta germplasm derived from controlled or natural hybridization events between wild or related semi-wild genotypes can be of exceptional value for selection, as they conserve part of the original variation of the wild population while also introducing new trait variation (Campuzano-Duque and Blair \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy systematically characterizing morphological traits associated with \u003cem\u003eC. canephora\u0026rsquo;s\u003c/em\u003e genotypes, new phenotypes that may possess unique attributes, such as increased green bean size and production (Ngugi and Aluka \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), climate resilience (Ferr\u0026atilde;o et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e) or enhanced cup quality (Bollen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) can be discovered and incorporated into breeding pipelines. The desirable morphological traits of coffee trees can range from plant architectural to microscopical traits. For example, the number of nodes per branch is a morphological indicator of higher yield potential (Cilas et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Spinelli et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Leaf morpho-anatomical characteristics like the specific leaf area (SLA) and stomatal density of the coffee tree directly reflect water use efficiency and the ability to adapt to different environmental conditions (Dubberstein et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These traits have also been reported to correlate with the productivity of the coffee tree (Gagliardi et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). SLA and stomatal density are highly sensitive to local environmental conditions, but their plasticity does have a genetic basis (De Kort et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These leaf traits are valuable to consider when selecting genotypes in a rapidly changing climate (Bertolino et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition to morphological traits of the coffee tree, green bean morphological traits such as screen size distribution, bean weight, and peaberry rate, which refers to the occurrence of rounded coffee seeds resulting from a single developed seed (Cilas and Bouharmont \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Ngugi and Aluka \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These attributes are particularly important to green coffee importers and significantly influence market pricing. Larger screen sizes, uniform bean weight, and lower peaberry rates are often associated with higher prices (Lingle and Menon \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Coffee Quality Institute \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTraditionally, \u003cem\u003eC. canephora\u003c/em\u003e is divided into two genetic groups: the Guinean group, which originates from the rainforests of West Africa, located west of the Dahomey Gap, and the Congolese group, originating from the rainforests of Central Africa (Berthaud and Charrier \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). In the early 20th century, the genetic resources of Central Africa played a key role in Robusta coffee\u0026rsquo;s breakthrough as a commercial crop (Leplae \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1936\u003c/span\u003e; Van Der Vossen \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Between 1930 and 1960, l\u0026rsquo;Institut National pour l\u0026rsquo;Etude Agronomique du Congo Belge (INEAC) was the leading breeding institute for Robusta coffee, with research stations in Lula, Luki and Yangambi. By the 1950s, Yangambi had become the leading research station for Robusta coffee. In later breeding programs, such as those in West Africa (Montagnon et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and Vietnam (Vi et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), genetic resources from the Congolese group again played a key role thanks to their valuable agronomic traits and contributions to cup quality (Moschetto et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). This genetic diversity of \u003cem\u003eC. canephora\u003c/em\u003e is of utmost importance to the climate adaptation of Robusta coffee (Bramel et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) but has been overlooked and insufficiently studied (Stoffelen et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Natural \u003cem\u003eC. canephora\u003c/em\u003e populations tend to hold high genetic variation, likely due to its self-incompatibility (Moraes et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Verleysen et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conservation of existing Robusta populations is essential for leveraging this diversity to characterize the wide range of variability for quality traits (Prakash \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe National Agricultural Study and Research Institute (INERA), formerly INEAC, manages the \u003cem\u003eex-situ\u003c/em\u003e coffee collection in Yangambi, DRC. Regional instability, weak governance, and lack of resources resulted in the decimation of the INERA Coffee Collection in Yangambi, and much documentation was lost (Stoffelen et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In collaboration with Meise Botanic Garden (Belgium), this collection has been rehabilitated, screened, and enriched with new genetic material from the wild and local home gardens (Stoffelen et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vanden Abeele et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A comprehensive genetic analysis of the INERA Coffee Collection revealed a relatively broad genetic diversity and genetic material from the Congolese subgroup A (likely originating from the INERA Research Station in Luki) and subgroup BE (hybrid between subgroups B and E; likely corresponding to \u0026lsquo;Wild\u0026rsquo; genotypes from the Yangambi rainforest) were discovered. However, the most abundant materials in the INERA Coffee Collection in Yangambi are known as \u0026lsquo;Lula\u0026rsquo; cultivars. These \u0026lsquo;Lula\u0026rsquo; cultivars are currently of unknown geographical origin (Verleysen et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The INERA Coffee Collection also contains individuals with an admixed genotypic background, likely derived from hybridization between \u0026lsquo;Lula\u0026rsquo; and subgroup BE (local \u0026lsquo;Wild\u0026rsquo;) or between \u0026lsquo;Lula\u0026rsquo; and subgroup A material. Previous research differentiated eight \u003cem\u003eC. canephora\u003c/em\u003e genetic groups corresponding to different geographic origins across West and Central Africa (Merot-L\u0026rsquo;anthoene et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The geographical regions of Congolese subgroups A, B, E, and R overlap with the west, north, central, and southeast of DRC, respectively. The INERA Coffee Collection has not been screened for morphological traits and agronomic performance since 1960 (Coste \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1955\u003c/span\u003e). The wild genetic resources introduced from the nearby Yangambi rainforest and the spontaneous, new hybrids found in the collection have never been studied. However, the sensory quality of 70 genotypes, which represented the genetic diversity of the INERA Coffee Collection, has recently been reported (Bollen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to provide an assessment of the morphological diversity and agronomic potential of the same 70 genotypes from the INERA Coffee Collection in Yangambi. We first explore the variation in morphological traits and evaluate if traits can be used to discriminate the genotypes based on genetic background. Secondly, we assess the agronomic potential of the genotypes based on two consecutive harvest years and their green bean traits. The latter in combination with their previously reported sensory quality scores. Overall, we aimed to contribute to the detailed evaluation and baseline phenotyping of underexplored \u003cem\u003eC. canephora\u003c/em\u003e genetic resources, laying the groundwork for developing new and improved coffee varieties that are better adapted to the challenges of the coffee market.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Site description and genetic material\u003c/h2\u003e \u003cp\u003eThe INERA Coffee Collection in Yangambi is located in the Tshopo province, DRC (Lat: 0\u0026deg;50'59.60\"N Long: 24\u0026deg;27'50.85\"E, 485m altitude). The Yangambi region is classified as K\u0026ouml;ppen Af (rainforest) with a dry season, average annual precipitation of 1837 mm, and a mean annual temperature of 25.1\u0026deg;C (Kasongo Yakusu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The soil of the Yangambi region is classified as Ferralsol, a strongly weathered soil with low nutrient-holding capacity (Jones et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The coffee collection is maintained as an unshaded monoculture system with coffee trees spaced 2.5x2.5 meters apart (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). Agricultural management practices include mulching, weeding, pruning, and minimal pesticide application without irrigation or fertilization.\u003c/p\u003e \u003cp\u003eGenetic analysis of the INERA Coffee Collection by Verleysen et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) delineated three subpopulations: Firstly, \u0026lsquo;Lula\u0026rsquo; cultivars that originate from the Lula breeding station of the DRC which currently have an unknown geographic origin; Secondly, the INERA accession belonging to Congolese subgroup A originating from another INERA breeding station in Luki; Thirdly, the collection contains local Wild accessions introduced from the nearby Yangambi rainforest (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). Additionally, admixed genotypes were identified, likely \u0026lsquo;Lula\u0026rsquo; \u0026ndash; Wild hybrids and \u0026lsquo;Lula\u0026rsquo; \u0026ndash; Congolese subgroup A hybrids (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC). Recently, 70 genotypes from the INERA Coffee Collection were selected and studied for their sensory quality. The selected genotypes were subdivided into five classes: Wild (n\u0026thinsp;=\u0026thinsp;3), \u0026lsquo;Lula\u0026rsquo; \u0026ndash; Wild hybrids (n\u0026thinsp;=\u0026thinsp;14), \u0026lsquo;Lula\u0026rsquo; (n\u0026thinsp;=\u0026thinsp;39), \u0026lsquo;Lula\u0026rsquo; \u0026ndash; Congolese subgroup A hybrids (n\u0026thinsp;=\u0026thinsp;13), and Congolese subgroup A (n\u0026thinsp;=\u0026thinsp;1) (Bollen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The same genotypes and clonal material were selected for our study. One to three clones per genotype, based on the genetic fingerprinting of Verleysen et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), were used in this study (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The age of the trees was based on the planting dates from recent INERA documentation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Morphological traits\u003c/h2\u003e \u003cp\u003eA set of 29 morphological traits (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was measured on 118 individual \u003cem\u003eC. canephora\u003c/em\u003e coffee trees comprising 70 genotypes.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Foliar traits\u003c/h2\u003e \u003cp\u003eThree healthy plagiotropic branches were sampled from the middle portion of each tree's main stem. Herbarium vouchers were made following Degreef et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and transported to Meise Botanic Garden. The stipule and petiole length were measured on the vouchers with an electronic caliper. The leaf length, width, area, and apex were measured from digital scans of the leaves using the ImageJ software (v15.3) (Schneider et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The plant vouchers were dried at 60\u0026deg;C for 24 hours to achieve homogeneous dried plant material. The specific leaf area (SLA) and stomatal density (stomata) were determined from five dried leaf samples. SLA was calculated from the individual leaf area and weight. The stomatal density was determined using the workflow of Meeus et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Two imprints of the abaxial side of each leaf were made using the transparent varnish method. The imprints were made in the middle of the leaf, one on each side of the main nerve. Three photomicrographs of 1.600 \u0026times; 1.200 pixels were taken per leaf print (dimensions\u0026thinsp;=\u0026thinsp;344\u0026times;258 \u0026micro;m; area view field\u0026thinsp;=\u0026thinsp;0.09 mm\u0026sup2;) using a Keyence VH-5000 digital microscope (v1.5.1.1) (Keyence International, Mechelen) with full coaxial lightning and default factory settings for shutter speed at \u0026times;1.000 lens magnification (VH-Z250R). Detection of stomata on the photomicrographs was achieved using a fine-tuned deep-learning model. We started from an open-source YOLOv8 deep learning detection model developed by Ultralytics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/ultralytics/ultralytics\u003c/span\u003e\u003cspan address=\"https://github.com/ultralytics/ultralytics\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This model was pre-trained with the COCO dataset (Lin et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and then fine-tuned on 14 photomicrographs of \u003cem\u003eC. canephora\u003c/em\u003e for a total of 392 stomata. This is in line with previous recommendations of Meeus et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) who recommend training on at least 250 stomata to achieve accurate detection results. Standard data augmentation techniques, such as rotation, flipping, and mosaic augmentation were applied to the training photomicrographs. This species-specific YOLOv8 object detection model was accurate, with a precision of 0.99 and a recall value of 0.98 across 100 photomicrographs of a test set. The stomata count was converted to the number of stomata per square millimeter to obtain the stomatal density.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Reproductive traits\u003c/h2\u003e \u003cp\u003eDuring the main harvest season of November 2021, fresh, developed coffee cherries were handpicked per individual coffee tree. A picture of five coffee cherries was taken for each coffee tree, including a standard ruler (mm). Secondly, after pulping the coffee cherries, a second picture of the ten fresh seeds was taken. Lastly, a transverse cut was made on five coffee cherries for a third picture. During the main flowering period of February 2022, five fully developed flowers were harvested per individual tree. A photo was taken of the lateral and apical view of the flower (Silva et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). An Olympus TG-6 camera was used for all pictures (Olympus Corporation, Tokyo). The reproductive traits were measured on the resulting images (4000\u0026times;3000 pixels) using the ImageJ software (v15.3) (Schneider et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Architectural and yield traits\u003c/h2\u003e \u003cp\u003eThe architecture and yield of the coffee trees were measured on-site. The coffee cherry yield was estimated using the method proposed by Cilas et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Starting from the top of the tree's main stem, two fructifying plagiotropic branches were selected at orthotropic nodes 5, 15, and 25, representing three levels of coffee tree production. For each of the six fructifying branches, the branch length, number of nodes, number of fructifying nodes, and number of coffee cherries per individual node were counted, resulting in an estimation of the tree's coffee cherry production.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of morphological traits measured in 70 \u003cem\u003eC. canephora\u003c/em\u003e genotypes from the INERA Coffee Collection, DRC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoliar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethod of evaluation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage length of five leaves from the petiole end to the apex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf width (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage width of five leaves at the widest section\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf apex (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage length of the apex to the convex transition of the leaf margin for five leaves\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStipule length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage length of ten stipules measured from the basis to the tip\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePetiole length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage length of ten petioles measured from the basis to the insertion with the blade\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLA (mm\u0026sup2;/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage specific leaf area of five adult leaves\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStomatal density (number/mm\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage stomatal density in an area of one mm\u0026sup2; from fifteen leaf prints\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReproductive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCherry length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage length of five fully-developed coffee cherries\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCherry width (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage width of five fully-developed coffee cherries\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage length of ten fully-developed coffee seeds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed width (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage width of ten fully-developed coffee seeds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCherry thickness (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage thickness of five fully-developed coffee cherries\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed thickness (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage thickness of ten fully-developed coffee seeds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulp thickness (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage thickness of the mesocarp from five fully-developed coffee cherries\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePetal length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage length of five fully-developed coffee flower petals\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePetal width (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage width of five fully-developed coffee flower petals\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnther length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage length of five fully-developed coffee flower anthers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorolla tube length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage length of five fully-developed coffee flower corolla tubes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArchitecture and yield\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBranch length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe average length of four adult plagiotropic branches in the middle of the tree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternode length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlagiotropic branch length divided by the nodes averaged over four branches\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlagiotropic branches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmount of plagiotropic branches on the main stem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTree height (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeight of the coffee tree\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTree width (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidth of the coffee tree at the widest section\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruct. nodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage amount of fructifying nodes across levels 5, 15, and 25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCherry per node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage amount of coffee cherries per fructifying node across levels 5, 15, and 25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruct. node ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRatio of fructifying nodes to total nodes across levels 5, 15 and 25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax. node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLargest amount of coffee cherries recorded at a node\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruct. branch ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRatio of fructifying plagiotropic branches to total branches on the main stem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cherries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmount of total cherries across the three levels\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Agronomic potential of the genotypes\u003c/h2\u003e \u003cp\u003eTo estimate the genotypes' agronomic potential, the following morphological traits were measured and averaged over two harvest years (2021 and 2022) to account for biennial bearing (Cilas et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e): fructifying nodes, cherry per node, fructifying branches, maximum node, and total cherries. When applicable, an average was made across the genotype's clonal trees. Additionally, the following green bean traits were measured at the genotype level, sourced from green bean samples used in the sensory quality study by Bollen et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e): Screen 15+ (%): Dimension distribution using a perforated screen plate with a 15/64-inch diameter screen. The green beans retained by screen size 15 were weighed and recorded as a percentage; Screen 17+ (%): Dimension distribution using a perforated screen plate with a 17/64-inch diameter screen. The green beans retained by screen size 17 were weighed and recorded as a percentage; 100 bean weight (BW) (g): The weight of 100 dried green beans; Peaberry: The number of rounded coffee beans (peaberries) was counted in 50 g of green beans.\u003c/p\u003e \u003cp\u003eThe sensory quality of the selected genotypes was previously evaluated through coffee cupping with the Fine Robusta Standards and Protocols (Coffee Quality Institute \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), resulting in a total score on a 100-point scale (Bollen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The reported total cupping score was included as metadata in our study (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics and correlations between traits were calculated across the 118 individual coffee trees. Spearman rank correlation was employed as not all traits exhibited a normal distribution. The significance of the Spearman correlations was Bonferroni corrected for multiple testing. Principal component analysis (PCA) of the traits was performed on the genotype level, i.e., based on the average of the clonal trees when applicable. Data were scaled before multivariate analysis. A linear discriminant analysis (LDA) and a random forest model were then applied to evaluate if the genotypes could be discriminated by their genetic background based on morphological traits. Because the \u0026lsquo;Lula\u0026rsquo; genotypes were overrepresented in this study, a subset of genotypes was created based on the subpopulations described by Verleysen et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e): Wild, \u0026lsquo;Lula\u0026rsquo;, and Congolese subgroup A (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC). Based on their fastSTRUCTURE values, the genotypes with the strongest \u0026lsquo;Lula\u0026rsquo; identity (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8), Wild identity (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6), and Congolese subgroup A identity (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8) were selected for this classification study (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The classification accuracy of the LDA model was evaluated with a 10-fold cross-validation. The random forest classification model was trained with 500 trees, and four variables were tested at each split. The correlation matrix was constructed using the \u003cem\u003ecorrplot\u003c/em\u003e package (v0.84). PCA was applied using the \u003cem\u003efactoextra\u003c/em\u003e package (v1.0.7), LDA using the \u003cem\u003eMASS\u003c/em\u003e package (v7.3.60), and the Random Forest using the \u003cem\u003erandomForest\u003c/em\u003e package (v4.7.1) in R (v4.2.3).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Variation and correlation of morphological traits at the individual tree level\u003c/h2\u003e \u003cp\u003eThere was considerable variation in the morphological traits across the 118 measured trees (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The coffee cherry and seed dimensions exhibited the least variation, with seed width having the lowest coefficient of variation of the studied traits. Most variation was observed in the yield traits, with the total cherries having the highest coefficient of variation in the studied traits.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and coefficient of variation (CV) of the morphological traits of the 118 individual coffee trees comprising 70 Robusta genotypes from the INERA Coffee Collection, DRC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e(continued)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e180.95\u0026thinsp;\u0026plusmn;\u0026thinsp;21.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePetal length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e10.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf width (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e73.88\u0026thinsp;\u0026plusmn;\u0026thinsp;9.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePetal width (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf apex (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e14.46\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnther length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e7.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStipule length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorolla tube length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e8.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePetiole length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e12.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBranch length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e85.21\u0026thinsp;\u0026plusmn;\u0026thinsp;17.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLA (mm\u0026sup2;/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8167\u0026thinsp;\u0026plusmn;\u0026thinsp;1138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInternode length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e4.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStomatal density (number/mm\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e347.39\u0026thinsp;\u0026plusmn;\u0026thinsp;60.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlagiotropic branches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e45.95\u0026thinsp;\u0026plusmn;\u0026thinsp;10.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCherry length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTree height (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e277.24\u0026thinsp;\u0026plusmn;\u0026thinsp;59.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCherry width (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTree width (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e169.76\u0026thinsp;\u0026plusmn;\u0026thinsp;32.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed length (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFruct. nodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e36.61\u0026thinsp;\u0026plusmn;\u0026thinsp;10.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed width (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCherry per node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e9.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCherry thickness (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFruct. node ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed thickness (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax. node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e25.19\u0026thinsp;\u0026plusmn;\u0026thinsp;9.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulp thickness (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFruct. branch ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal cherries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e379.12\u0026thinsp;\u0026plusmn;\u0026thinsp;206.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSignificant Spearman correlations were observed between the studied morphological traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Leaf length correlated with SLA (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.46) and with stomatal density (\u003cem\u003er\u003c/em\u003e = -0.47). Similarly, leaf width correlated significantly with SLA (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.54) and with stomatal density (\u003cem\u003er\u003c/em\u003e = -0.48). SLA and stomatal density were negatively correlated (\u003cem\u003er\u003c/em\u003e = -0.52). Larger coffee cherry dimensions resulted in larger coffee seed dimensions. Flower petal length correlated significantly with petal width (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.49) and anther length (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.67). Anther length correlated significantly with the corolla tube length (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.44). The number of plagiotropic branches on the main stem correlated significantly with the branch length (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.47) and tree width (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.44). The number of fructifying nodes correlated with cherries per node (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.46). The maximum node correlated with the number of cherries per node (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.77).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo reduce the multicollinearity when analyzing the variation in morphological traits of the genotypes the leaf length and width variables were combined into a leaf length-to-width ratio variable; the cherry length, width, and thickness variables were omitted, given their strong collinearity with the seed dimension variables; The width of the coffee tree was omitted as it correlated strongly with branch length. The maximum node and total cherries variables were omitted as they correlated strongly with the fructifying nodes and cherry per node variables.\u003c/p\u003e \u003cp\u003eThe first two principal components explained 28.13% of the variation in data. No clear separation of the genotypes based on their genetic background was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the first dimension, the samples were grouped based on productive traits (fructifying nodes, cherry per node, and branch length) and floral traits (petal, anther, and corolla tube dimensions). The separation in the second dimension was explained mainly by the coffee seed dimensions. The majority of the \u0026lsquo;Lula\u0026rsquo; \u0026ndash; subgroup A hybrid genotypes were positioned more along the positive axis of the first dimensions, exhibiting a higher number of cherries per node and fructifying node values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Discrimination of the genetic background based on morphological traits\u003c/h2\u003e \u003cp\u003eDiscrimination of the genotypes by genetic background based on their morphological traits was evaluated through an LDA and random forest classifier on a subset of genotypes. The LDA separated the genotypes with the strongest Wild identity from genotypes with the strongest \u0026lsquo;Lula\u0026rsquo; and Congolese subgroup A identity by the first linear discriminant (LD1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The anther length, petal length, seed thickness, SLA, and stomatal density were the most important traits of the first LD. The \u0026lsquo;Lula\u0026rsquo; genotypes were separated from the genotypes with the strongest Congolese subgroup A identity by LD2. The seed thickness, SLA, seed length, and stomatal density were the most important traits of the second LD. The classification accuracy of the LDA model after 10-fold cross-validation was 43.33%. The random forest model reported a prediction error of 59.09% for the classification of the genotypes. The class error rates were 0.50, 0.50, and 0.83 for the \u0026lsquo;Lula\u0026rsquo;, Congolese subgroup A, and Wild identity groups, respectively. The most important traits in the random forest classifier were, in decreasing order, the seed width, number of fructifying nodes, petiole length, stipule length, and anther length (Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Agronomic potential of the genotypes\u003c/h2\u003e \u003cp\u003eThe agronomic potential of the genotypes was assessed based on the morphological yield traits over two consecutive harvest years, the green bean traits, and the tree architecture, with additional attention to the sensory quality of the coffee (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Screen size distribution ranged from 8% retained by screen 15 to 92% retained by screen 17. The weight of 100 green beans ranged from 8.70 g to 19.77 g. The highest and widest genotypes recorded were 425 and 255 cm, respectively. The number of fructifying branches on the main stem ranged from 11 to 63. The number of fructifying nodes across the three measured coffee tree levels ranged from 14 to 70. The average amount of cherries per node across the three levels ranged from 4 to 17. The highest amount of coffee cherries recorded at a single node was 52. The total amount of coffee cherries counted across the three levels of the coffee tree ranged from 70 to 816.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePhenotyping the genetic diversity of \u003cem\u003eC. canephora\u003c/em\u003e germplasm collections, especially in the geographical origins of the species, is an important first step in discovering promising genetic material for developing new coffee varieties. Our exploration of the morphological diversity and agronomic potential of the understudied \u003cem\u003eC. canephora\u003c/em\u003e genetic resources from the DRC revealed a substantial variation in morphological traits. The yield traits exhibited the most variation, while the coffee cherry and seed dimensions exhibited the least variation. The variation in morphological traits was not consistent with the genetic background, and no morphological traits were found to classify and predict the genetic background of the genotype accurately. The variation in agronomic and quality-relevant traits was substantial, with the \u0026lsquo;Lula\u0026rsquo;, \u0026lsquo;Lula\u0026rsquo; \u0026ndash; Wild hybrid, and \u0026lsquo;Lula\u0026rsquo; \u0026ndash; subgroup A hybrid material containing promising genotypes for further evaluation.\u003c/p\u003e \u003cp\u003eRegarding the morphological diversity of the genotypes, substantial variation in all traits was found, especially in the yield traits. Akpertey et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) studied 71 genotypes from germplasm in Ghana and reported similar coefficients of variation for the leaf and coffee cherry dimensions. Similarly, they reported a higher variation in tree architecture and branch traits compared to the coffee leaf and cherry traits. Previous research on Conilon varieties (Congolese subgroup A) reported less variation for anther and corolla tube length compared to our study but similar positive correlations between petal, anther, and corolla tube lengths (Silva et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We found a substantial range in SLA and stomatal density across the studied genotypes but this variation was not consistent with the genetic background of the respective genotypes. Dubberstein et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also reported a wide morphological diversity in stomatal density for Conilon genotypes. In our study, no significant correlation was found between SLA and coffee cherry production or between stomatal density and coffee cherry production. In comparison, previous research reported a correlation between specific leaf area and coffee plant yield (Gagliardi et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In line with Pompelli et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) on \u003cem\u003eC. arabica\u003c/em\u003e and also previously reported in other plant species (Xu and Zhou \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), we observed a negative correlation between SLA and stomatal density. The environmental plasticity of these two functional leaf traits likely has a genetic basis (De Kort et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and plays an important role in the climate adaptation of food crops (Bertolino et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Genotypes with a low SLA and stomatal density can be expected to have a fitness advantage under dry environmental conditions (Poorter et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bertolino et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This trait combination was found in the \u0026lsquo;Lula\u0026rsquo; \u0026ndash; Wild hybrids G0023 and G0067 (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), which both, interestingly, also scored a high sensory quality, with G0067 exhibiting a large screen size (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccurate prediction of the genetic background of the genotypes based on their morphological traits was not achieved. Cross-validation of the LDA model revealed a low prediction accuracy (43.33%) for the constructed linear discriminants. The classification errors for genetic background in both the LDA and random forest models were high, and the reported morphological traits of importance in the two classification models were not the same. No combination of morphological traits was able to accurately classify an accession according to its genetic background, consistent with previous research on Robusta genotypes. Akpertey et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported high coefficients of variations for most studied traits in Robusta genotypes, with origins in Togo, Ghana, Cameroon, and the Ivory Coast and the grouping of the genotypes based on morphological traits was inconsistent with the genetic background. Similarly, Robusta genotypes from Uganda exhibited morphological variation but this was not predictive for their genetic background (Ngugi and Aluka \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Advancements in molecular research have increased the efficiency and cost-effectiveness of genotyping plant material, making it easier to analyze the genetic composition of Robusta coffee's genetic resources. However, classifying Robusta genetic material based on morphological traits still requires further evaluation, as it could provide a practical method for screening genetic resources for desirable traits. Additionally, access to molecular research is often limited in the regions where Robusta's genetic resources are found.\u003c/p\u003e \u003cp\u003eA substantial variation in traits linked to agronomic performance was observed across the genotypes. If the green bean traits and sensory quality would be prioritized, promising material was found within the \u0026lsquo;Lula\u0026rsquo; \u0026ndash; Wild hybrid and \u0026lsquo;Lula\u0026rsquo; material. Genotypes G0067 and G0035 from the \u0026lsquo;Lula\u0026rsquo; \u0026ndash; Wild and genotype G0077 from the \u0026lsquo;Lula\u0026rsquo; class had a total sensory quality score of 83 points or higher, and were characterized by large coffee beans (\u0026gt;\u0026thinsp;50% retained by screen 17), a high 100 bean weight, and a moderate peaberry rate. The relatively large coffee bean size of the \u0026lsquo;Lula\u0026rsquo; \u0026ndash; Wild material, combined with their high sensory quality scores, indicates the potential of this hybrid material for further breeding efforts. These genotypes exhibited a tree height above three meters, an agronomic trait that is less desired but can be managed through adapting cultivation practices. As a comparison, screen sizes of Ugandan Robusta accessions averaged around screen 15, but with a high level of morphological and bean size variability (Ngugi and Aluka \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Also genotype G0240 from the \u0026lsquo;Lula\u0026rsquo; class is noteworthy, as it had the largest screen size distribution in the studied material, with good coffee production and sensory quality.\u003c/p\u003e \u003cp\u003eIf agronomic production would be prioritized, promising material was found within the \u0026lsquo;Lula\u0026rsquo; \u0026ndash; subgroup A class. The PCA indicated that the \u0026lsquo;Lula\u0026rsquo; \u0026ndash; subgroup A hybrids were associated with higher coffee cherry production. More specifically, genotypes G0086 and G0010 had the highest total cherry production across two harvest years, with G0010 recording a maximum node of 52 coffee cherries. Genotype G0102 recorded the most fructifying nodes, which correlates with cumulative coffee production over time (Cilas et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The relatively high number of fructifying nodes in these \u0026lsquo;Lula\u0026rsquo; \u0026ndash; subgroup hybrids is an interesting characteristic. Congolese subgroup A material has its geographical origin in Gabon, the Republic of the Congo, and western DRC, from which the \u0026lsquo;Petit-Kwilu\u0026rsquo; variety and the Conilon material were derived. These coffee varieties exhibit high coffee production but small coffee bean dimensions, whereas the \u0026lsquo;Lula\u0026rsquo; material is known for its larger green bean size. Genotype G0087 (subgroup A) originated from the Luki region of western DRC and was labeled as \u0026lsquo;Petit-Kwilu\u0026rsquo; material, exhibiting a small coffee bean size. Hybridization between the Congolese subgroup A and \u0026lsquo;Lula\u0026rsquo; material could result in coffees with a high productivity and larger bean size, as was suggested by genotypes G0222, G0220, and G0010. On the other hand, genotype G0073 from this hybrid class scored exceptionally high in Total score and performed well in agronomic production but lagged in coffee bean size. These results indicate a promising starting point for future breeding activities between these Congolese subgroup A and \u0026lsquo;Lula\u0026rsquo; material. Inter-varietal hybridization between Congolese subgroup A (\u0026lsquo;Petit-Kwilu\u0026rsquo;) and subgroup BE (\u0026lsquo;Lula\u0026rsquo;) for the development of drought-resistant varieties with large beans was initiated at INERA (Montagnon et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) but it was never completed. Our \u0026lsquo;Lula\u0026rsquo; \u0026ndash; subgroup A hybrids are possibly the descendants of these initial inter-varietal crossing experiments. The direct parents of these hybrids are not known, so the level of heterosis could not be evaluated. Our results indicate that specific genotypes from \u0026lsquo;Lula\u0026rsquo; \u0026ndash; subgroup A class exhibited large bean sizes and performed well on sensory quality. It would be interesting to evaluate their performance in dry environments as a considerable range in SLA and stomatal density was reported in this hybrid class (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The discovered genotypes with promising morphological traits could be used as parent plants in breeding programs.\u003c/p\u003e \u003cp\u003eIn this context, no hybrid crossings between the Wild and Congolese subgroup A were found in the INERA Coffee Collection (Verleysen et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Future research could undertake targeted crossings between these two genetic classes, as the genetic distance of the parental material correlates with phenotypic performance (Ferr\u0026atilde;o et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Previous research on hybrid crossings between Conilon (Congolese subgroup A) and a Robusta variety (Congolese subgroup E) reported better agronomic and yield performance, with a high expression of heterosis in the hybrid material (Teixeira et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Carvalho et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Alkimim et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similar hybrid vigor observations were made in the hybrid material of Ghanaian accessions (Akpertey et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Conilon varieties of Brazil (Ferr\u0026atilde;o et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). These breeding experiments mainly focused on the already established genetic resources from the same origin groups. The ongoing introduction of local wild materials from the Yangambi rainforest into the INERA Coffee Collection will result in newly captured genetic diversity available for crossing experiments. These hybrids could potentially exhibit a desirable combination of traits.\u003c/p\u003e \u003cp\u003eThe multiplication of accessions at the INERA Coffee Collection through seedlings and open pollination resulted in the hybridization of the initial genetic material into many new and unique genetic fingerprints (Verleysen et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This resulted in a substantial variation in sensory quality, and promising sensory profiles within the different genetic classes of the collection were discovered (Bollen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similar observations were made in terms of agronomic potential. Promising material was found within the \u0026lsquo;Lula\u0026rsquo; \u0026ndash; Wild hybrid, \u0026lsquo;Lula\u0026rsquo;, and \u0026lsquo;Lula\u0026rsquo; \u0026ndash; subgroup A hybrid material. Ngugi and Aluka (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported similar observations in agronomic traits from crossings between cultivars and local wild material in Ugandan germplasm. The Wild and Congolese subgroup A material did not perform well, but their sample sizes were too small to draw solid conclusions. A more comprehensive screening of the INERA Coffee Collection is advised in this context, as only 70 of the 263 unique genetic identities reported in Verleysen et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) were studied. Furthermore, the initial genetic analysis included only a fraction of the collection\u0026rsquo;s total genetic resources, leaving many opportunities for further exploration. Additionally, the continuous introduction of accessions from plantations and wild populations expands the available genetic resources for phenotypic screening (Verleysen et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur study explored the morphological diversity and agronomic potential of Robusta genetic resources from the Democratic Republic of the Congo, arguably the main center of Robusta diversity. Considerable variation in morphological traits was observed, but no combination of morphological traits was found to classify and predict the genetic background of the genotype accurately. The yield traits exhibited the most variation, while the coffee cherry and seed dimensions exhibited the least variation. Genotypes with desired traits for green beans were identified in both \u0026lsquo;Lula\u0026rsquo; and \u0026lsquo;Lula\u0026rsquo; \u0026ndash; Wild hybrids, while promising yield traits were found in \u0026lsquo;Lula\u0026rsquo; \u0026ndash; Congolese subgroup A hybrids. Promising genotypes with low SLA and stomatal density were found in the \u0026lsquo;Lula\u0026rsquo; \u0026ndash; Wild class, which could have a fitness advantage in dry environments warranting further research. Future research should focus on designing field trials to explore the promising material. These trials should ideally be conducted at multiple locations with different environmental conditions. Crossing Wild and Congolese subgroup A material is proposed since hybrids from these two genetic classes are currently non-existent. Our findings highlight the agronomic potential of the underexplored Robusta coffee genetic resources from the Democratic Republic of the Congo. They also stress the importance of conserving and thoroughly screening these genetic resources.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCONFLICT OF INTEREST\u003c/h2\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e \u003cp\u003eThis study was funded by the Belgian Science Policy Office (BELSPO) contract no. B2/191/P1/COFFEEBRIDGE (CoffeeBridge Project) of the Belgian Research Action through Interdisciplinary Networks (BRAIN-be 2.0). Meise Botanic Garden coordinated the rehabilitation and characterization of the INERA Coffee Collection with the support of the European Union\u0026rsquo;s 11th Development Fund (FED/2016/381\u0026ndash;145) through the \u0026ldquo;Formation, Recherche et Environnement dans la Tshopo\u0026rdquo; (FORETS) project implemented by the Center for International Forestry Research (CIFOR). Local infrastructure was created in the framework of the Climcoff project funded by the Flemish Government, Dept. Environment \u0026ndash; SIDO.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR. B.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing \u0026ndash; original draft. J. K.: Data curation, Formal analysis, Investigation, Methodology. A. T.: Data curation, Formal analysis, Investigation, Methodology, Writing \u0026ndash; original draft. B. N. K.: Data curation, Formal analysis, Investigation, Methodology. E.A.T.: Data curation, Formal analysis, Investigation, Methodology, Project administration. F. W.: Conceptualization, Investigation, Methodology, Writing \u0026ndash; review \u0026amp; editing. F. V.: Conceptualization, Formal analysis, Investigation, Methodology, Writing \u0026ndash; review \u0026amp; editing. O.H.: Funding acquisition, Project administration, Supervision, Writing \u0026ndash; review \u0026amp; editing. P.S.: Funding acquisition, Project administration, Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe want to thank the Institut National pour l\u0026rsquo;Etude et la Recherche Agronomiques (INERA) for giving access to their collection and infrastructure; CIFOR for the logistic and administrative support during fieldwork in the Democratic Republic of the Congo; The Flemish Government for their support; Karin Vansteenwegen, Koen Wabbes, Rachel Ndezu and Jean-Claude Kazadi for their assistance with the morphological traits study. We would also like to thank the Congolese Minist\u0026egrave;re de l\u0026rsquo;Environnement et D\u0026eacute;veloppement Durable (MEDD) for granting research permits N\u0026deg;004/ANCCB-RDC/SG-EDD/BTB/2021, N\u0026deg;014/ANCCB-RDC/SG-EDD/BTB/11/2021, and N\u0026deg;025/ ANCCB-RDC/SG-EDD/BTB/11/2022.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe morphological data of the genotypes can be found in the Supplementary Materials. Individual measurements and information on the YOLOv8 deep learning detection model can be provided upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkpertey A, Anim-Kwapong E, Ofori A (2019) Assessment of Genetic Diversity in Robusta Coffee Using Morphological Characters. 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Genome Biol Evol 15:1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/gbe/evad065\u003c/span\u003e\u003cspan address=\"10.1093/gbe/evad065\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWCR (2023) The future of Robusta quality: considerations for genetic improvement\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Z, Zhou G (2008) Responses of leaf stomatal density to water status and its relationship with photosynthesis in a grass. J Exp Bot 59:3317\u0026ndash;3325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jxb/ern185\u003c/span\u003e\u003cspan address=\"10.1093/jxb/ern185\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Robusta, DRC, germplasm, morphology, phenotyping, agronomy traits","lastPublishedDoi":"10.21203/rs.3.rs-5305587/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5305587/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMeeting rising quality standards while at the same time addressing climate challenges will make the commercial cultivation of Robusta coffee increasingly difficult. Whereas breeding new varieties may be an important part of the solution, such efforts for Robusta lag behind, with much of its genetic diversity still unexplored. By screening existing field genebanks to identify accessions with desirable traits, breeding programs can be significantly facilitated. This study quantifies the morphological diversity and agronomic potential of 70 genotypes from the INERA Coffee Collection in Yangambi, Democratic Republic of the Congo. We measured 29 traits, comprising vegetative, reproductive, tree architecture, and yield traits. Classification models were applied to establish whether these traits could accurately classify genotypes based on their background. Furthermore, the agronomic potential and green bean quality of the genotypes were studied. While significant variation in morphological traits was observed, no combination of traits could reliably predict the genetic background of different genotypes. Genotypes with promising traits for green beans were identified in both \u0026lsquo;Lula\u0026rsquo; and \u0026lsquo;Lula\u0026rsquo; \u0026ndash; Wild hybrids, while promising yield traits were found in \u0026lsquo;Lula\u0026rsquo; \u0026ndash; Congolese subgroup A hybrids. Additionally, certain \u0026lsquo;Lula\u0026rsquo; \u0026ndash; Wild hybrids showed low specific leaf area and stomatal density, indicating potential fitness advantages in dry environments, warranting further study. Our findings highlight the agronomic potential of underexplored Robusta coffee genotypes from the Democratic Republic of the Congo and indicate the need for further screening to maximize their value.\u003c/p\u003e","manuscriptTitle":"Agro-morphological characterization of Coffea canephora (Robusta) genotypes from the INERA Yangambi Coffee Collection, Democratic Republic of the Congo","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-28 11:19:59","doi":"10.21203/rs.3.rs-5305587/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d2dc367e-d756-482d-81fe-b9594d10f289","owner":[],"postedDate":"October 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-30T13:53:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-28 11:19:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5305587","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5305587","identity":"rs-5305587","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}