Introduction
Cocoa (Theobroma cacao L.), of the family Malvaceae sensu lato, a perennial cash crops (Marita et al., Reference Marita, Nienhuis, Pires and Aitken2001) predominantly grows in the tropics of Central and South America, Asia and Africa. It had an estimated world output of 3.5 million tons in 2006 (de Almeida and Valle, Reference de Almeida and Valle2007) and 5.2 million metric tons in 2020 (WorldAtlas, 2021). The economic value of cocoa beans exported as whole, broken, raw or roasted was 8.6 billion US Dollars (USD) in 2017 (Eghbal, Reference Eghbal2018). It has remained the foremost non-oil source of foreign exchange to the major cocoa-producing countries in West and Central Africa. It is cultivated by more than five million growers in 50 countries and 40–50 million people derive their livelihood from it (CacaoNet, 2012; World Cocoa Foundation, 2012). The chocolate and cocoa powder from cocoa beans are good and nutritional sources of minerals especially potassium, magnesium, copper and iron (Afoakwa et al., Reference Afoakwa, Paterson and Fowler2007; Torres-Moreno, et al., Reference Torres-Moreno, Torrescasana, Salas-Salvadó and Blanch2015; Adeyeye, Reference Adeyeye2016).
Aikpokpodion et al. (Reference Aikpokpodion, Kolesnikova-Allen, Adetimirin, Guiltinan, Eskes, Motamayor and Schnell2010) presented the historical chronology of the introduction schemes of cocoa genetic resources into Nigeria as follows: (i). Upper Amazon and ‘Trinitario’ populations, which were introduced from Trinidad in 1944 (Posnette and Todd, Reference Posnette and Todd1951), (ii). 1965–1967 large-scale introduction of Upper Amazon cacao materials from Trinidad and Tabago (Aikpokpodion et al., Reference Aikpokpodion, Kolesnikova-Allen, Adetimirin, Guiltinan, Eskes, Motamayor and Schnell2010), (iii). Cacao Introduction Scheme sponsored by the Cocoa Alliance that included quite a large number of intra-Nanay, intra-Parinari, intra-Iquitos and inter-Pound selections (Atanda, Reference Atanda1977; Olatoye and Esan, Reference Olatoye and Esan1992) and (iv). some cocoa genetic resources from Costa Rica, Indonesia, Fernando Po, Kew Garden, Wageningen and Miami (Jacob et al., Reference Jacob, Atanda and Opeke1971). These four introductions have long remained the primary cocoa genetic resources for cultivar development in the breeding programmes and planting materials generation at the Cocoa Research Institute of Nigeria (CRIN), Ibadan, Nigeria. Cocoa genetic resources in the list of the various introductions have greatly dwindled; their erosion has been attributed to: old age of plants and plantations. Furthermore, their neglect has been obvious due to poor record keeping and inconsistent data on extant plants. Urbanization and pressure on land for alternative uses have also contributed to genetic erosion, especially in the out-station breeding field banks of CRIN. For example, the out-station field bank at Ibule-soro, Akure, Ondo state, Nigeria has been lost to urbanization.
CRIN has remained the only agricultural institute in Nigeria with the mandate for cocoa improvement, it therefore hosts cacao germplasm collections of Nigeria. Currently, there are four ex situ cocoa breeding field germplasm collections at CRIN headquarters in Ibadan, Nigeria. The four field banks include: (i) introduced cocoa genetic materials established at the International Clone (IC) field bank, (ii) hybrids generated from crosses among some local clones and established in the hybrid trial (HT) field bank, (iii) hybrids generated for regional (central and west Africa) variety evaluation trial (RVT) field bank and (iv) some old ‘C’ and ‘T’ clones that were established at the local clones trial (LC) field bank. The C-clones were mixtures of local Amelonado and red podded local Trinitario (Lockwood and Gyamfi, Reference Lockwood and Gyamfi1979) and T-clones were the approved Upper Amazon populations whose open pollinated progenies formed the F3-Amazon (Aikpokpodion et al., Reference Aikpokpodion, Motamayor, Adetimirin, Adu-Ampomah, Ingelbrecht, Eskes, Schnell and Kolesnikova-Allen2009).
High bean yield and disease (especially black pod) resistance had long been the primary breeding goal of cocoa in Nigeria. A large proportion of the cocoa genetic resources in the Nigerian cocoa field genebanks have remained unutilized for food quality improvement because their potentials for nutritional and functional food values have not been unveiled. The eight cocoa varieties released for Nigerian farmers in 2011 (CRIN, 2011) were promoted for high bean yield and resistance to black pod and pod rot diseases. Record of effort(s) for cocoa bean quality improvement in Nigeria is scarce. The present investigation for physico-chemical and nutraceutical diversity among 77 genotypes could present a platform for the initiation of a bean quality improvement programme for cocoa in Nigeria.
Significant variation exists among West African cocoa genotypes at phenotypic and genomic levels (Opoku et al., Reference Opoku, Bhattacharjee, Kolesnikova-Allen, Motamayor, Schnell, Ingelbrecht, Enu-Kwesi and Adu-Ampomah2007; Sonwa et al., Reference Sonwa, Nkongmeneck, Weise, Tchatat, Adesina and Janssens2007; Pokou et al., Reference Pokou, Ngoran, Lachenaud, Eskes, Montamayor, Schnell, Kolesnikova-Allen, Clement and Sangare2009; Aikpokpodion, Reference Aikpokpodion2010; Adewale et al., Reference Adewale, Adeigbe, Adenuga, Adepoju, Muyiwa and Aikpokpodion2013; Olasupo et al., Reference Olasupo, Adewale, Aikpokpodion, Muyiwa, Bhattacharjee, Gutierrez, Motamayor, Schnell, Eba and Zhang2018). The nutraceutical properties of the cocoa germplasm in Trinidad and Tobago and Cameroun have been documented (Bekele and Phillips-Mora, Reference Bekele, Phillips-Mora and Al-Khayri2019). Quite recently, Adeigbe et al. (Reference Adeigbe, Adewale and Muyiwa2021) noted that genotypes within each of the four cocoa field banks in Nigeria showed variation for some physicochemical properties. Utilization of plant genetic resources in breeding programmes is dependent on the understanding of their potentials (CacaoNet, 2012; Bekele and Phillips-Mora, Reference Bekele, Phillips-Mora and Al-Khayri2019). Cocoa beans have complex raw ingredients. More than 500 flavour compounds have been identified in cocoa products; to the manufacturing industries, understanding the source of each is a science on its own (Reed, Reference Reed2010). There are documented proximate and mineral composition in cocoa bean in Nigeria (Adeyeye et al., Reference Adeyeye, Akinyeye, Ogunlade, Olaofe and Boluwade2010; Ndife et al., Reference Ndife, Bolaji, Atoyebi and Umezuruike2013), however, the number of genotypes employed in such studies were less than three. The unavailability of nutritional and quality information of the already characterized (phenotypic – Aikpokpodion et al., (Reference Aikpokpodion, Motamayor, Adetimirin, Adu-Ampomah, Ingelbrecht, Eskes, Schnell and Kolesnikova-Allen2009) and genomic – Olasupo et al., (Reference Olasupo, Adewale, Aikpokpodion, Muyiwa, Bhattacharjee, Gutierrez, Motamayor, Schnell, Eba and Zhang2018)) Nigerian cocoa germplasm is an evident gap, thus substantiating the essence of the present investigation.
Improved consciousness about health among consumers has greatly increased global promotion of functional foods (Gaikwad et al., Reference Gaikwad, Rani, Kumar, Gupta, Babu, Bainsla and Yadav2020). Furthermore, the cocoa-based product manufacturers now seek cocoa with enhanced nutraceutical value (Bekele and Phillips-Mora, Reference Bekele, Phillips-Mora and Al-Khayri2019). Naturally occurring plant nutraceuticals (which are consumed as food or food parts) deliver benefits to consumers beyond basic nutrition, such as protecting organisms against oxidative stress, and playing other roles, such as: anti-microbial, anti-oxidant, anti-inflammatory and anti-cancer properties (Aguilar et al., Reference Aguilar, Villacorta, Contreras, González and León-Vargas2017; Rosa et al., Reference Rosa, Bonaccorsi di, Patti, Vasilyev and Cutone2022). Identifying physicochemical and nutraceutical status of the different cocoa genotypes in the active Nigerian cocoa breeding germplasm has become evidently necessary. The present investigation, therefore seeks to identify diversity for some biochemical and bioactive traits among genotypes within the germplasm and superior groups of genotypes for specific nutritional, phytochemical, mineral and physical properties.
Materials and methods
The Active cocoa breeding collections in CRIN
The Breeding and Improvement Division of CRIN, Ibadan, Nigeria, has four active cocoa breeding field banks. They are: LC trial field bank, containing 13 clones; HT field bank containing 23 hybrids; RVT field bank which hosts nine hybrids from Ghana, four from Cote d'Ivoire, three from Cameroon, two common crosses from Nigeria and six local control crosses; and the IC field bank which contain 17 introduced cocoa clones. The genotypes were the treatments in each of the field banks. They were laid out in a randomized complete block design with six replicates. Each field banks contained two rows of five plants. List of the genotypes held in the various active breeding field banks is presented in Table 1.
List of cocoa genotypes in the four active cocoa breeding field banks at the Cocoa Research Institute of Nigeria (CRIN), Ibadan, Nigeria
RVT, regional variety trial; HT, hybrids trial; IC, international clones; LC, local clones.
Materials for the study and analytical protocols
Pods were harvested during a cropping season and bulked from all the trees of a genotype. Twenty uniform fruits were then selected from the bulked pool of fruits for each genotype. Total number of pods/genotypes in the harvest was within 250–450. Pods were broken seeds were extracted and pooled on genotype basis. Pooled seeds of each of the 77 genotypes were separately fermented in trays. Fermented samples were sun-dried and milled into powder using standard procedures (ICCO, 2021). Duplicate samples, each of the 77 genotypes were determined following the recommendations of Fitzpatrick (Reference Fitzpatrick, Bhandari, Bansal, Zhang and Schuck2013) for powdered food samples. Twenty-one biochemical traits were analysed, including: physical properties (bulk density, dispersibility, re-hydration time, oil and water absorption capacity) and proximate (moisture, crude ash, fat, fibre and protein) contents which were determined following the procedures in Ooi et al. (Reference Ooi, Iqbal and Ismail2012). The pH was determined following the procedure in Vijayakumar and Adedeji (Reference Vijayakumar and Adedeji2017). After high pressure digestion, macro-elements (P, K, Ca and Mg) were analysed by atomic absorption spectrometer and micro-elements (Fe and Zn) were determined by sensitive atomic spectrometric techniques and the results were obtained using the working standards of AOAC (1990); 1000 ppm for each of the genotypes (Poitevin, Reference Poitevin2016). Caffeine and theobromine were determined following the procedure in Thomas et al. (Reference Thomas, Yen and Schantz2004) and flavonoid was analysed following the procedure of Lin and Tang (Reference Lin and Tang2007). Variability and character association of 17 of the 21 traits for each of the four breeding field banks have been earlier determined by Adeigbe et al. (Reference Adeigbe, Adewale and Muyiwa2021).
Data analysis
Basic descriptive statistics analysis including coefficient of range was carried out on the data and Pearson correlation analysis was conducted on the mean matrix table containing the 77 genotypes and the 21 variables. The replicated data on the 77 treatments and 21 variables was subjected to multivariate analysis of variance using the PROC GLM procedure of SAS version 9.4 (SAS, 2011). A mean data matrix comprising 77 genotypes by 21 variables was submitted to SAS using PROC DISTANCE to generate pairs of Gower genetic distances among the 77 genotypes (Gower, Reference Gower1971). The paired genetic distances were then submitted for principal component analysis (using PROC PRINCOMP) and clustering analysis (using PROC TREE, WARD minimum variance hierarchical clustering method (Ward, Reference Ward1963)) in SAS. Mean genetic distances were further generated for each cluster and the mean performances for the 21 traits in each cluster were determined. Significant differences between pairs of clusters were tested by paired t-test using SAS. Moreover, inter- and intra-cluster variability was further verified using the cluster means, standard error (SE) and coefficient of variation (CV) for each trait.
Results
Variability and potentials of the different genotypes
In Table 2, the 21 variables significantly (P ≤ 0.05) differentiated the 77 genotypes. The highest CV (10.8%) was recorded for water absorption capacity, whereas all other traits had low CV within 1.02–9.53% (Table 2). From Table 3, the best genotypes for crude fibre, crude ash, moisture content, theobromine, flavonoid, caffeine and rehydration time were, respectively: T12/11, T65/7, T10/15, T86/2, T12/15, T65/35 and T65/7. The highest proportion of crude protein, calcium, zinc and dispersibility was found in HYB17, HYB19, HYB5 and HYB22, respectively (Table 3). The highest butterfat, crude oil, potassium, iron, phosphorus, bulk density, water absorption capacity and oil absorption capacity were found, respectively, in RVTG6, RVTG5, RVTG19, RVTG10, RVTG20, RVTG10, RVTG18 and RVTG5. RVTG9 had the highest magnesium content (Table 3).
Analysis of variance showing the sources of variation, degrees of freedom, coefficient of variation and mean squares of the 21 biochemical traits
Prot, protein; MC, moisture; Theobr, theobromine; Flavo, flavonoids; Caffe, caffeine; Dispers, dispersibility; BD, bulk density; WAC, water absorption capacity; OAC, oil absorption capacity; RehydT, rehydration time; Cal, calcium; Pota, potassium; Phos, phosphorus and Mag, magnesium.
*, ** and *** – significance among the 77 genotypes at P ≤ 0.05, 0.01 and 0.001 respectively.
Descriptive statistics for the 21 biochemical traits employed for the diversity study of the 77 cocoa genotypes
a Genotypes are in parenthesis: RVTG1 – (P7 × PA150) × IMC47, RVTG3 – C303 × PA120, RVTG6 – MAN 15-2 × T85/799, RVTG7 – PA13 × P19, RVTG10 – T60/887 × ICS89, RVTG12 – T63/967 × T17/524, RVTG14 – UPA134 × SNK64, RVTG15 – PA4 × P7, RVTG18 – SNK614 × SCA24, RVTG19 – C77 × C67, RVTG20 – T65/7 × T79/501, RVTG21 – (P7 × PA150) × Amaz 15-15, RVTG22 – A1/154 × T85/185, HYB5 – P7 × T60/887, HYB6 – P7 × PA150, HYB17 – T65/7 × T53/8, HYB19 – T86/2 × T53/8, HYB22 – T82/27 × T16/17.
bCoR, coefficient of range (X max. – X min.)/(X max. + X min.) × 100.
Correlations among the various biochemical traits
In online Supplementary Table S1, 121 pairwise correlations were significant, of which 74 were positive and 47 were negative. Protein content in the cocoa bean had a positive and significant correlation with crude fat, dispersibility, bulk density, calcium, potassium, phosphorus, iron, magnesium, water and oil absorption capacity, but it had a significant negative correlation with crude fibre and theobromine content (online Supplementary Table S1). A significant positive correlation existed between all the mineral elements, except for zinc with phosphorus and potassium, then potassium with magnesium. Furthermore, all the mineral elements had a positive significant correlation with dispersibility (online Supplementary Table S1). The relationships between crude ash, moisture content, theobromine and flavonoid were positive and significant. Moreover, the correlation of bulk density with water and oil absorption capacity was positive and significant (online Supplementary Table S1). Each of the following traits: ash, moisture content, theobromine, flavonoid, caffeine and rehydration time had a negative and significant correlation with crude fat. Moreover, each of the six minerals except potassium equally had a negative and significant correlation with crude fibre (online Supplementary Table S1).
Variance contribution to principal components and genotypes' grouping pattern
In online Supplementary Table S2, the eigenvalues of the first three PC axes were higher than 2.5. The highest variance contribution (37.5%) was in PC1. The first three PC axes accounted for and explained 70% of the total variation among the 77 genotypes (online Supplementary Table S2). The Ward clustering method separated the 77 genotypes into four main clusters at the similarity coefficient above 0.10 (Fig. 1). The grouping pattern was very specific: cluster I contained all genotypes in the hybrid trial field bank and five others: APA 4, Playa Alta, RVTG18, RVTG19 and RVTG20. Clusters II, III and IV, respectively, had 100% members of the genotypes in the international clone field bank, regional variety field bank and local clone field bank (Fig. 1). From the cluster history (data not shown), HYB14 and HYB15 were the first pair of genotypes to unite at 0.000 point of similarity. Cluster II was most compact; the 15 genotypes it captured formed a single cluster at a similarity coefficient of 0.0082, with IMC47 and UF676 as the first pair of genotypes to unite at 0.0003 (Fig. 1). Cluster IV was equally highly compact; the 13 genotypes it contained formed a unit (a cluster) at 0.0232 similarity coefficient, whereas all the 28 genotypes formed Cluster I at 0.0286 similarity coefficient (Fig. 1). The 21 genotypes in Cluster III were loose, their grouping into a cluster was at a lower similarity coefficient (Fig. 1).
The grouping of the seventy seven cocoa genotypes by Ward clustering method.
Intra and inter-cluster variability
In Table 4, the cluster with the highest (0.933) intra-cluster similarity was Cluster II, the group of genotypes with the least (0.816) intra-cluster similarity was Cluster III (Table 4). Furthermore, similarities between pair of clusters revealed that cluster I and III had the highest inter-cluster similarity (0.63), whereas cluster III and IV had the least similarity (0.261). The six possible paired comparisons of the four clusters by t-test statistics revealed significant differences between each pair of the four clusters at P ≤ 0.05 (Table 4).
Level of similarity within each cluster (in parenthesis), similarities coefficients by Gower genetic distance method between pairs of cluster (lower diagonal) and probability showing significance in the paired comparison among the four clusters (upper diagonal)
*, ** and *** denote significance at 0.05, 0.01 and 0.001 probability level, respectively.
The mean performance of the grouped genotypes in each cluster for the 21 biochemical traits was presented in Table 5. Group of genotypes with the highest mean for crude protein, dispersibility, water absorption capacity, rehydration time, calcium, potassium and iron existed in Cluster I (Table 5). Genotypes in Cluster II had the highest mean for zinc and magnesium. Highest values for crude fat, butterfat, bulk density, oil absorption capacity and phosphorus occurred among genotypes in Cluster III, whereas the genotypes in Cluster IV had the highest mean values for crude fibre, ash, moisture content, theobromine, flavonoid and caffeine (Table 5).
Mean and variability of the 21 biochemical traits within each cluster (I to IV)
SE, standard error; CV, coefficient of variation; n, number of genotypes making up a cluster population; GS, genetic similarities.
Discussion
Existence of significant difference(s) among genotypes for the measured traits reveals the availability of exploitable variation for nutritional improvement of cocoa. Although the present study was based on a seasonal harvest, the result presents inferential platform for subsequent investigation of the same genotypes. The 21 studied variables were significantly useful in discriminating the 77 cocoa genotypes in the Nigerian field bank, hence they are polymorphic. The use of polymorphic variables for characterization according to Kaufman and Rousseeuw (Reference Kaufman and Rousseeuw1990) do enhances clarity in the grouping pattern of genotypes.
The noted high phenotypic diversity in the studied population for the various biochemical traits makes the cocoa field bank collections of Nigeria a promising valuable resources for nutraceutical breeding and improvement programmes. Aikpokpodion et al. (Reference Aikpokpodion, Motamayor, Adetimirin, Adu-Ampomah, Ingelbrecht, Eskes, Schnell and Kolesnikova-Allen2009) had much earlier reported high morpho-agronomic diversity in the Nigerian cocoa genetic resources. Identification of differences among the various genotypes agrees with the known axiom that different genotypes have unique genetic identities and hosting unique potentials which are primary resources in plant breeding.
Low CV is a good measure of similarity within sample and hence the reliability of generated descriptive statistics from the data, as such values does not imply diversity. In our study, percentage differences (between the minimum and the maximum values) among the 77 cocoa genotypes for the 21 traits ranged from 5.2% in moisture content to 81% in theobromine. Crude fibre, bulk density, water absorption capacity, caffeine, theobromine, iron, magnesium and zinc hosts wider variabilities having coefficient of range values ≥50%. The large variation observed in this study was expected because the different genotypes did not share the same genetic origin as earlier reported for Nigeria cocoa germplasm by Aikpokpodion et al. (Reference Aikpokpodion, Motamayor, Adetimirin, Adu-Ampomah, Ingelbrecht, Eskes, Schnell and Kolesnikova-Allen2009) and Olasupo et al. (Reference Olasupo, Adewale, Aikpokpodion, Muyiwa, Bhattacharjee, Gutierrez, Motamayor, Schnell, Eba and Zhang2018). These arrays of diversity among the extant cocoa genotypes in the field banks of CRIN, Nigeria offer great promises for subsequent cocoa bean nutritional and quality improvements.
Among the 77 genotypes in this study, significant positive correlations existed between some biochemical compounds. This promises to ease independent selection of genotypes for different traits and enhance faster advancement in genetic development of better bean quality in Nigerian cocoa. Summarily from the study, calcium strongly and positively associated with zinc, iron and magnesium, protein improvement in cocoa will positively affect the improvement of calcium, potassium, phosphorus, iron, magnesium and butterfat. In consonance with the report of Jiang et al. (Reference Jiang, Gao, Liao, Harberd and Fu2007), phosphorus and potassium were strongly correlated. Among the phytochemicals, selection for caffeine will simultaneously and positively affects flavonoid and theobromine, Meng et al. (Reference Meng, Jalil and Ismail2009) noted similar relationship.
West African cocoa has long been known to be non-acidic (Wood, Reference Wood, Wood and Lass1987), the pH range in the fermented beans in our study was 6.21–7.03, and this is in agreement with Jayeola and Oluwadun (Reference Jayeola and Oluwadun2010) who reported near alkaline to alkaline pH of 6.4–7.4 for some cocoa genotypes in Nigeria. However, our result differed from that of Ndife et al. (Reference Ndife, Bolaji, Atoyebi and Umezuruike2013) who obtained pH range of 5.65–6.15, the variation could be due to genotype, processing and component composition. The low acidity to alkalinity status of cocoa bean reported in this study hinted on the inherent health benefit in consuming cocoa products. The nutritional quality of cocoa beans was further revealed in this study, crude protein ranged between: 12.5–15.1%. This is higher than the reported crude protein content of 8.14% in cocoa powder by Ndife et al. (Reference Ndife, Bolaji, Atoyebi and Umezuruike2013) for unspecified cocoa genotype(s) from Akure, Ondo state, Nigeria. However, in the review by Rawel et al. (Reference Rawel, Huschek, Sagu and Homann2019), a mean crude protein of 11–13% was recorded, however, the work further stated that 11.8–15.7% could be possible depending on the geographical origin of cocoa genotypes. The identified variation among the 77 cocoa genotypes for crude protein in this study present opportunity for genetic improvement of the trait as inter-crossing among different genotypes could lead to the evolvement of heterotic progenies.
Diversity is critical to ensuring the future of the world cocoa production and to meeting the challenging and changing climate changes and consumer preferences (CacaoNet, 2012). The classification of the 77 genotypes to the four different groups revealed that there is similarity among genotypes within the same cluster. The hybrids had the best and outstanding performances for 65% of the traits. T63/967 × T17/524 was the most alkaline genotype, T65/7 × T53/8 had the highest protein, MAN 15-2 × T85/799 had the highest fat, GU147A × NA33 had the highest oil content, T82/27 × T16/17 structurally was most dispersible, T60/887 × ICS89 had both the highest bulk density and highest iron content, highest calcium, potassium, phosphorus and zinc was obtained in T86/2 × T53/8, C77 × C67, T65/7 × T79/501 and P7 × T60/887 respectively. The present study re-emphasizes the importance of cross breeding, a method which mostly result in heterosis among hybrids. Individual direct selection can be made on these genotypes for advancement or use as parent in di-hybrid cross breeding programme. For each of the clusters, the highest biochemical traits of significance were: high pH (i.e. low acidity), protein, Ca, K and Fe (cluster I, containing 82% hybrids, 7% introduced clones and 11% regional varieties), high zinc and magnesium (cluster II, containing introduced clones from international sources), high crude fat and phosphorus (cluster III, containing regional varieties) and high crude ash, fibre, theobromine, flavonoid and caffeine (cluster IV, containing the local clones). This provides a hint that the group of genotypes in each of the various field banks have specific uniqueness to which selection can be directed.
The 13 genotypes in the local clone field bank constitute the earliest introduction of cocoa genetic resources to Nigeria, comprising very few lower Amazon (e.g. N38), mostly upper Amazon and some Trinitario sub-species. The overall superiority in performances of the different genotypes for the various traits was across four field banks. The observed strict grouping of the thirteen cocoa clones in the local clone field bank with the highest content of theobromine, flavonoids and caffeine hints that some percentage of the earliest cocoa germplasm introduced to Nigeria are still intact and further reveal that the Nigerian cocoa germplasm still hold some genetic materials with strong chocolate flavour that is characteristic of the West African Amelonado. The other clusters where the content of theobromine, flavonoids and caffeine were lower must have undergone selections against the quality traits.
Aikpokpodion (Reference Aikpokpodion2010) informed that Nigerian cocoa farmers hosts significant genetic variability in their fields, genomic divergent within Nigerian cocoa genetic resources is wide (Olasupo et al., Reference Olasupo, Adewale, Aikpokpodion, Muyiwa, Bhattacharjee, Gutierrez, Motamayor, Schnell, Eba and Zhang2018), and the present report identified diversity for physicochemical and nutraceutical in the active breeding field banks of Nigeria cocoa, these presents good insurance against present and future threat to cocoa cultivation, production and quality improvement.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S1479262123000977
Acknowledgements
The authors wish to appreciate the staff of the Nursery and Vegetative Propagation Units of the Crop Improvement Division, CRIN, for their support in the course of this work.
Author's contributions
ABD and AOO – Conceptualization, investigation and methodology; AOO – Data generation; ABD – Formal analysis; ABD – original draft; AOO – review and editing.
Funding statement
None.
Competing interests
None.
Data availability
Data are available on reasonable request from the corresponding author.
Informed consent
None.
Open access
