*Literature reviewed on the molecular characterization of cassava (rev. ref.).*

**Table 2.**

**15**

*Identification of Cassava Varieties in Ex-Situ Collections and Global Farmer's Fields: An Update…*

variation, such as those found in clonal lineages, such as cassava, or highly inbred organisms, such as maize. However, one has to ask whether the large number of SNPs resolved with or without prior knowledge of the genome are more reliable than SSRs or SNP arrays built from expressed sequence tag databases with a high frequency of heterozygous loci in the population. Two SNP arrays have been built for cassava: the Illumina GoldenGate 1,190SNPs-assay by Ferguson et al. [59], and the Fluidigm® Dynamic 96 SNP Array™ SNPY-Chip by Becerra Lopez-Lavalle

**4. Current status of PCR-based DNA analysis for variety identification**

sity of cassava, and to establish the relationships among genotypes (**Table 2**). In 1994, Marmey et al. [126] showed the value of RADPs for analyzing the crop's genetic diversity, as well as for detecting duplicated accessions (10%) among collections. In 1996, Angel et al. [70] showed that RADPs give comparable results to RFLPs, offering a cost- and time-effective alternative to restriction and hybridization DNA analysis. Another powerful PCR-based molecular marker tool used in cassava [72] is the AFLP method used by Vos et al. [148], in which selected restriction fragments from the digestion of total DNA are reduced in complexity by PCR and resolved with 1 to 2 bp difference. Roa et al. [72] concluded that AFLPs were an effective and efficient molecular methodology with which to estimate genetic similarities in the genetic variability of cassava, and among other Manihot species. Of the 77 studies listed in **Table 2**, 13% used RAPDs, and 12% used AFLPs, including studies incorporating morphological descriptors [50, 51, 55, 64, 68, 72, 73, 111]. Both molecular marker methods have been shown to be powerful and able to provide genetic data that reflects the observed phenotypic differences, geographic origins, and pedigree background of the plants. The AFLP fingerprinting technique detected a larger number of duplicates in the African and LAC cassava landraces than RAPDs, suggesting that AFLPs are a suitable for estimating genetic similarity and dissimilarity [72]. The identification of duplicates across these studies ranged from 4 to 35%. AFLP data indicated that cassava varieties can become widespread and adopted by farmers under different names, leading germplasm

SSRs, which were used in 47% of the studies reviewed here (**Table 2**), and their use has been favored over that of RADPs or AFLPs in cassava. SSR markers are abundant and evenly distributed across the cassava genome, are co-dominant, highly polymorphic, and are not influenced by the environment [149]. Compared with AFLPs, SSRs are less technically challenging to implement. These marker system data can easily be shared across different laboratories, particularly if fingerprinting data is generated with fluorescently labeled SSR markers and resolved in capillary DNA-sequencing instruments. Overall, the authors consulted for this review agreed that SSR profiles generated for improved and landrace genomes were extremely useful in the conservation of diversity in Africa, Asia, LAC, and SEA, as well as for guiding the best crop improvement strategy. Studies involving the development of molecular tools to accelerate the introgression of observed phenotypic differences on disease resistance, such as cassava mosaic disease (CMD) have been extremely successful in identifying the SSRs that will best guide this effort. CMD resistance has been efficiently introgressed into LAC's breeding lines, and success-

Over the last decade, we have witnessed an important shift in the cassava research community in Africa and LAC, led by IITA and CIAT, toward

PCR-based DNA molecular markers have been used to assess the genetic diver-

*DOI: http://dx.doi.org/10.5772/intechopen.99110*

and co-workers at CIAT [4, 12, 105].

curators to consider them to be different varieties.

fully transferred to Africa [150–152].

*Identification of Cassava Varieties in Ex-Situ Collections and Global Farmer's Fields: An Update… DOI: http://dx.doi.org/10.5772/intechopen.99110*

variation, such as those found in clonal lineages, such as cassava, or highly inbred organisms, such as maize. However, one has to ask whether the large number of SNPs resolved with or without prior knowledge of the genome are more reliable than SSRs or SNP arrays built from expressed sequence tag databases with a high frequency of heterozygous loci in the population. Two SNP arrays have been built for cassava: the Illumina GoldenGate 1,190SNPs-assay by Ferguson et al. [59], and the Fluidigm® Dynamic 96 SNP Array™ SNPY-Chip by Becerra Lopez-Lavalle and co-workers at CIAT [4, 12, 105].

#### **4. Current status of PCR-based DNA analysis for variety identification**

PCR-based DNA molecular markers have been used to assess the genetic diversity of cassava, and to establish the relationships among genotypes (**Table 2**). In 1994, Marmey et al. [126] showed the value of RADPs for analyzing the crop's genetic diversity, as well as for detecting duplicated accessions (10%) among collections. In 1996, Angel et al. [70] showed that RADPs give comparable results to RFLPs, offering a cost- and time-effective alternative to restriction and hybridization DNA analysis. Another powerful PCR-based molecular marker tool used in cassava [72] is the AFLP method used by Vos et al. [148], in which selected restriction fragments from the digestion of total DNA are reduced in complexity by PCR and resolved with 1 to 2 bp difference. Roa et al. [72] concluded that AFLPs were an effective and efficient molecular methodology with which to estimate genetic similarities in the genetic variability of cassava, and among other Manihot species.

Of the 77 studies listed in **Table 2**, 13% used RAPDs, and 12% used AFLPs, including studies incorporating morphological descriptors [50, 51, 55, 64, 68, 72, 73, 111]. Both molecular marker methods have been shown to be powerful and able to provide genetic data that reflects the observed phenotypic differences, geographic origins, and pedigree background of the plants. The AFLP fingerprinting technique detected a larger number of duplicates in the African and LAC cassava landraces than RAPDs, suggesting that AFLPs are a suitable for estimating genetic similarity and dissimilarity [72]. The identification of duplicates across these studies ranged from 4 to 35%. AFLP data indicated that cassava varieties can become widespread and adopted by farmers under different names, leading germplasm curators to consider them to be different varieties.

SSRs, which were used in 47% of the studies reviewed here (**Table 2**), and their use has been favored over that of RADPs or AFLPs in cassava. SSR markers are abundant and evenly distributed across the cassava genome, are co-dominant, highly polymorphic, and are not influenced by the environment [149]. Compared with AFLPs, SSRs are less technically challenging to implement. These marker system data can easily be shared across different laboratories, particularly if fingerprinting data is generated with fluorescently labeled SSR markers and resolved in capillary DNA-sequencing instruments. Overall, the authors consulted for this review agreed that SSR profiles generated for improved and landrace genomes were extremely useful in the conservation of diversity in Africa, Asia, LAC, and SEA, as well as for guiding the best crop improvement strategy. Studies involving the development of molecular tools to accelerate the introgression of observed phenotypic differences on disease resistance, such as cassava mosaic disease (CMD) have been extremely successful in identifying the SSRs that will best guide this effort. CMD resistance has been efficiently introgressed into LAC's breeding lines, and successfully transferred to Africa [150–152].

Over the last decade, we have witnessed an important shift in the cassava research community in Africa and LAC, led by IITA and CIAT, toward

*Cassava - Biology, Production, and Use*

**14**

**Region**

SEA

U V

> **Total**

49

1570

29730

21

10

3

10 *Plant materials used: [1] Collection maintained at CIAT's GRU, [2] Collection maintained at IITA's GRU, [3] Collection maintained at ORSTOM (IRD-France), [4] Field collection in Tanzania and* 

*Nigeria, [5] Field collected in Kibaha and Ikiriguru, [6] Collection maintained at National Agricultural Research Institute, Maputo, [7] Field collected in Hoima, Kumi, and Luwero districts, [8] Field* 

*collected in Baka, Mkondezi, Chitala, Chitedze, and Makoka Agricultural Research Stations, [9] Collection maintained at Plant Genetic Resource Research Institute at Bunso in the Eastern Region of Ghana* 

*and the University of Cape Coast, [10] Collection maintained at MARI, [11] Collection maintained at CSIR-Crops Research Institute (CRI), [12] Collection maintained at Kenya Agricultural Research* 

*Institute (KARI), [13] Collection maintained at University of Kinshasa, [14] Collection maintained at CNRA, [15] Field collected in Ghana Brong Ahafo, Ashanti, and Eastern, [16] Field collected in* 

*North Central followed by South–South, Southwest, and Southeast regions, [17] Field collected in Southern and Central Benin, [18] Field collected in Apac, Arua, Kibaale, and Masindi, [19] Collection* 

*maintained at Njala Agricultural Research Centre (NARC), [20] Collection maintained at ICAR-Central Tuber Crops Research Institute, Sreekariyam, Thiruvanathapuram, [21] Tapioca and Castor* 

*Research Station (T&CRS) GRU, [22] Field collected in Campinas, [23] Field collected in Makushi Village, [24] Field Collected in Sao Paolo state, [25] Embrapa Cassava and Fruit Crops, Cruz das* 

*Almas, BA, [26] Field collection in Rewa, a Makushi community, [27] Field collection in Pernanbuco, [28] Field collection in Amazonian region, [29] Field collected in Maringá, [30] Field collection in* 

*Talamanca and Coto Brus, [31] Southeastern part of the Brazilian Atlantic Forest, [32] Genetics Department of ESALQ/USP, [33] Instituto Agronómico at Campinas, Sao Paul, [34] Field collected in* 

*Maringá, Paraná, [35] Field collected across the island and gene bank, [36] Banco Regional de Germoplasma de Mandioca do Cerrado (BGMC), [37] Embrapa's germplasm collection Belém, Pará, [38]* 

*Escola Superior deAgricultura Luiz de Queiroz, Sao Paulo University, [39] Field collected by SINCHI, [40] Cuban Cassava Germplasm Collection, [41] Field collected in Campo Grande, Mato Grosso do* 

*Sul, [42] Field collected in Maringá, Cianorte, and Toledo, Paraná, [43] Field Collected in Cauca, [44] Field collected in Southern of Minas Gerais State, [45] Field collected in Paraná and Santa Catarina* 

*(South), Mato Grosso do Sul (Midwest), and Minas Gerais (Southeast), [46] Embrapa - Acre, Rio Branco, [47] Germplasm Bank (BGM) of Maringá State University (UEM) Paraná state, [48] West* 

*Countries where the studies took place: [A] Colombia, [B] Kenya, [C] Ivory Coast, [D] Nigeria, [E] Tanzania, [F] Mozambique, [G] Uganda, [H] Malawi, [I] Ghana, [J] Congo, [K] Benin, [L] Sierra* 

*Bangka District, Bangka District, and South Bangka District, [49] Field collected across 32 villages and HLARC, RCRDC, and AGI's GRUs.*

*Leone, [M] India, [N] China, [O] France, [P] Brazil, [Q] Guyana, [R] Costa Rica, [S] Puerto Rico, [T] Cuba, [U] Indonesia, [V] Vietnam.*

**Table 2.**

*Literature reviewed on the molecular characterization of cassava (rev. ref.).*

37

3

1

1

8

1

13

10783

48

10

**X**

**X**

**X**

**Location**

**Source**

**No. of** 

**Morphological** 

**Isozymes**

**RFLPs**

**RAPDs**

**SSRs**

**ISSRs**

**SRAPs**

**ISTR**

**AFLPs**

**DaRTs**

**SNPs**

**No. of** 

**Rev.** 

**Duplicates**

0

> **X**

1535

[12]

[111]

**Ref.**

**cassava** 

**Descriptors**

**samples**

sequence-based nucleotide variation mining. In sub-Sahara Africa, Ferguson et al. [59] characterized and validated 1,190 SNPs using the V4.1 of the cassava genome [144] and Illumina's GoldenGate assay. They demonstrated that SNP markers could successfully measure the genetic variability of cassava, while accurately detecting duplicates in the IITA's gene bank collection. The SNP data of Ferguson et al. [59] allowed, the comparison of the genetic diversity between cassava varieties from the Americas and Africa, and showed that cassava from the Americas displayed greater genetic diversity than their counterparts in Africa. These researchers showed that the levels of genetic diversity in west, southern, eastern, and central Africa were similar. These two observations suggested a massive adoption by IITA of improved varieties developed for African farmers.

In 2015, Rabbi et al. [2] undertook a large varietal identification survey on 917 accessions using 56,489 SNP loci generated by next-generation sequencing [147], compared against 64 released cassava varieties and popular landraces in Ghana. Rabbi et al. [2] accomplished variety identification and ancestry estimation through two complementary cluster methods: distance-based hierarchical clustering, and model-based maximum likelihood admixture analysis. They found that 30% of the identified accessions from farmers' fields matched specific released varieties. A hierarchical clustering analysis revealed that the number of major varieties was 11, and 69% of the accessions belonged to one of the 11 groups, while the remaining accessions had two or more ancestries. Rabbi et al. [2] demonstrated that reduced subsets of SNP markers could reproduce the results obtained from the full set of markers, concluding that GBS can be performed at higher DNA multiplexing. However, these results, as well as those by Ferguson et al. [59], indicated that a large numbers of SNPs may not be needed to achieve accurate identification of cassava varieties, whether in farmers' fields or in formal germplasm collections.

Concurrently, CIAT and the Beijing Genome Institute (BGI) [153] committed to developing genomic resources in the post-genomic era, with the aim of increasing scientists' understanding of the evolution and distribution of cassava from its origin in the Americas to Africa and Asia. Next-generation sequence information from both wild and domesticated species offered cassava researchers the opportunity to investigate individual genes which have played a role in the domestication of cassava. Whole genome sequences allow researchers to exploit genomic variations associated with resistance to pests such as whiteflies or mites, and diseases such as frog skin disease and cassava brown streak disease, as well as to improve the nutritional value of the crops, such as by increasing the pro-vitamin A content. In 2013, CIAT's geneticists and bioinformaticians explored the genetic variation present in 150 LAC accessions, and identified a panel of 180 highly informative single nucleotide variants (SNVs, MAF > 0.25), with high discriminative power and a uniform genome distribution of 5 to 10 SNP per chromosome. These SNVs were transferred to a SNPtype™ allele-specific PCR assay and validated on the same set of samples (Fluidigm® Dynamic Array™, USA) (Becerra Lopez-Lavalle, personal communication).

Of the 180 SNVs identified by CIAT, a 96 SNPs Fluidigm® Dynamic Array™ (referred to as an "SNPY-CHIP") was first assembled and used by Peña-Venegas et al. [105], who aimed to validate the identity of 173 Amazonian cassava landraces classified as unique by indigenous growers. The cassava SNPY-CHIP allowed the classification of 44 genotypes into 21 duplicate-genotype clusters, confirming the uniqueness of 150 (87%) of the 173 materials identified as unique by indigenous people of the Colombian Amazon. The SNPY-CHIP array also allowed the exploration of the diversity and population structure of these materials. When the 150 unique genotypes characterized in this study were compared with genotypes from the CIAT core collection, the cassava genotypes from the Tikuna community of San Martín de Amacayacu (AMA) appeared to be closely related to Peruvian manioc

**17**

*Identification of Cassava Varieties in Ex-Situ Collections and Global Farmer's Fields: An Update…*

genotypes (PER). CIAT scientist demonstrated that these SNP markers have a very low genotyping error rate, and are easy to store and share in genotype databases. The information generated from the 99 accessions evaluated along with the 150 from the Peña-Venegas et al. [105] study allow us to assess the value of each SNP with a high MAF, indicating a genotyping success. The 99 cassava genotypes represents a good sampling of the global cassava germplasm collection. Of the 99 genotypes, 71 were from the Americas: five from Argentina, two from Bolivia, four from Brazil, 26 from Colombia, three from Costa Rica, three from Cuba, three from Ecuador, five from Guatemala, five from Mexico, two from Panama, two from Paraguay, five from Peru, one from Puerto Rico, and five from Venezuela; nine from Asia: two from China, three from Thailand, two from Indonesia, and two from Malaysia; three hybrids from ICA-CIAT (Colombia), three genotypes from Africa (TMS60444, C18 and TME3) and 13 samples of unknown origin (AM206-5, AM560-2, FLA 21, FLA61, FLA 19, GLA8, GM905-52, GM905-57, GM905-60, SM301-3, SG107-35, GUT64, and JAC3). These 99 genotypes exhibit good phenotypic differentiation and are likely to be of ancient origin in the Americas. Of the 272 genotypes analyzed, 249 (91%) were unique genotypes, showing the effectiveness of these SNPs for varietal identification and the identification of duplicates (9%). The SNP results unequivocally identified

all accessions, including those nominated as morphological duplicates.

confirmed the results obtained by SNP fingerprinting (**Figure 2**).

An ambitious variety adoption study using DNA fingerprinting (SNPY-CHIP)

and socioeconomic approaches was undertake by CIAT scientists in Vietnam [154]. The cassava germplasm found in Vietnam has very limited morphological description and molecular information, limiting its use for breeding. However, farming communities in Vietnam have maintained traditional knowledge about this genetic diversity through the vernacular names given to varieties. Depending on the context, however, informal naming of varieties can lead to either overestimates or underestimates of crop diversity. Ocampo et al. [12] studied the varietal composition found in Vietnamese cassava production regions using SNP markers. They procured 97 different varieties based on farmer identification, from a total of 1,570 cassava genotypes collected across six agro-ecological zones. Vietnamese farmers distinguished the different varieties mainly by the morphology of the vegetative parts, such as Bamboo Leaf, Long Leaf, Purple Bud, Red Bud, and Red

The accurate identification of cassava varieties in the Colombian Amazon using DNA-based SNPY-CHIP provided the opportunity to undertake large variety adoption studies using SNP-based DNA fingerprinting. This approach established the basis for the methodology of a multidisciplinary approach and for synergy of efforts between agricultural scientists and economists [4]. Floro et al. [4] estimated the level and determinants of adoption of improved varieties in the Cauca department of Colombia, using the SNPY-CHIP. They collected cassava samples from each variety identified by cassava growers, and interviewed 217 households in Cauca, Colombia. Four hundred and thirty six cassava samples were collected, and DNA fingerprinting was undertaken using the SNPY-CHIP. The genetic analysis allowed the identification of duplicated genetic material, as well as the improved hybrids developed by CIAT, thus reducing the 117 named varieties by farmers to 60 true genetic types found in CIAT's germplasm collection or its global cassava breeding program (**Figure 1**). A set of 60 unique genotypes was identified showing this set of genotypes are missing at CIAT's germplasm collection (**Figure 1**). DNA fingerprinting was therefore shown to be important in the procurement of new germplasm to introduce into breeding programs or furnish publicly funded gene banks with the most diverse and complete set of accessions. The cassava genetics research team at CIAT reorganized the 436 stem samples collected, and planted them back in the Cauca region in the Morales Municipality, to assess their morphological features. The morphology displayed by each of the 120 varietal plots

*DOI: http://dx.doi.org/10.5772/intechopen.99110*

*Identification of Cassava Varieties in Ex-Situ Collections and Global Farmer's Fields: An Update… DOI: http://dx.doi.org/10.5772/intechopen.99110*

genotypes (PER). CIAT scientist demonstrated that these SNP markers have a very low genotyping error rate, and are easy to store and share in genotype databases. The information generated from the 99 accessions evaluated along with the 150 from the Peña-Venegas et al. [105] study allow us to assess the value of each SNP with a high MAF, indicating a genotyping success. The 99 cassava genotypes represents a good sampling of the global cassava germplasm collection. Of the 99 genotypes, 71 were from the Americas: five from Argentina, two from Bolivia, four from Brazil, 26 from Colombia, three from Costa Rica, three from Cuba, three from Ecuador, five from Guatemala, five from Mexico, two from Panama, two from Paraguay, five from Peru, one from Puerto Rico, and five from Venezuela; nine from Asia: two from China, three from Thailand, two from Indonesia, and two from Malaysia; three hybrids from ICA-CIAT (Colombia), three genotypes from Africa (TMS60444, C18 and TME3) and 13 samples of unknown origin (AM206-5, AM560-2, FLA 21, FLA61, FLA 19, GLA8, GM905-52, GM905-57, GM905-60, SM301-3, SG107-35, GUT64, and JAC3). These 99 genotypes exhibit good phenotypic differentiation and are likely to be of ancient origin in the Americas. Of the 272 genotypes analyzed, 249 (91%) were unique genotypes, showing the effectiveness of these SNPs for varietal identification and the identification of duplicates (9%). The SNP results unequivocally identified all accessions, including those nominated as morphological duplicates.

The accurate identification of cassava varieties in the Colombian Amazon using DNA-based SNPY-CHIP provided the opportunity to undertake large variety adoption studies using SNP-based DNA fingerprinting. This approach established the basis for the methodology of a multidisciplinary approach and for synergy of efforts between agricultural scientists and economists [4]. Floro et al. [4] estimated the level and determinants of adoption of improved varieties in the Cauca department of Colombia, using the SNPY-CHIP. They collected cassava samples from each variety identified by cassava growers, and interviewed 217 households in Cauca, Colombia. Four hundred and thirty six cassava samples were collected, and DNA fingerprinting was undertaken using the SNPY-CHIP. The genetic analysis allowed the identification of duplicated genetic material, as well as the improved hybrids developed by CIAT, thus reducing the 117 named varieties by farmers to 60 true genetic types found in CIAT's germplasm collection or its global cassava breeding program (**Figure 1**). A set of 60 unique genotypes was identified showing this set of genotypes are missing at CIAT's germplasm collection (**Figure 1**). DNA fingerprinting was therefore shown to be important in the procurement of new germplasm to introduce into breeding programs or furnish publicly funded gene banks with the most diverse and complete set of accessions. The cassava genetics research team at CIAT reorganized the 436 stem samples collected, and planted them back in the Cauca region in the Morales Municipality, to assess their morphological features. The morphology displayed by each of the 120 varietal plots confirmed the results obtained by SNP fingerprinting (**Figure 2**).

An ambitious variety adoption study using DNA fingerprinting (SNPY-CHIP) and socioeconomic approaches was undertake by CIAT scientists in Vietnam [154]. The cassava germplasm found in Vietnam has very limited morphological description and molecular information, limiting its use for breeding. However, farming communities in Vietnam have maintained traditional knowledge about this genetic diversity through the vernacular names given to varieties. Depending on the context, however, informal naming of varieties can lead to either overestimates or underestimates of crop diversity. Ocampo et al. [12] studied the varietal composition found in Vietnamese cassava production regions using SNP markers. They procured 97 different varieties based on farmer identification, from a total of 1,570 cassava genotypes collected across six agro-ecological zones. Vietnamese farmers distinguished the different varieties mainly by the morphology of the vegetative parts, such as Bamboo Leaf, Long Leaf, Purple Bud, Red Bud, and Red

*Cassava - Biology, Production, and Use*

varieties developed for African farmers.

sequence-based nucleotide variation mining. In sub-Sahara Africa, Ferguson et al. [59] characterized and validated 1,190 SNPs using the V4.1 of the cassava genome [144] and Illumina's GoldenGate assay. They demonstrated that SNP markers could successfully measure the genetic variability of cassava, while accurately detecting duplicates in the IITA's gene bank collection. The SNP data of Ferguson et al. [59] allowed, the comparison of the genetic diversity between cassava varieties from the Americas and Africa, and showed that cassava from the Americas displayed greater genetic diversity than their counterparts in Africa. These researchers showed that the levels of genetic diversity in west, southern, eastern, and central Africa were similar. These two observations suggested a massive adoption by IITA of improved

In 2015, Rabbi et al. [2] undertook a large varietal identification survey on 917 accessions using 56,489 SNP loci generated by next-generation sequencing [147], compared against 64 released cassava varieties and popular landraces in Ghana. Rabbi et al. [2] accomplished variety identification and ancestry estimation through two complementary cluster methods: distance-based hierarchical clustering, and model-based maximum likelihood admixture analysis. They found that 30% of the identified accessions from farmers' fields matched specific released varieties. A hierarchical clustering analysis revealed that the number of major varieties was 11, and 69% of the accessions belonged to one of the 11 groups, while the remaining accessions had two or more ancestries. Rabbi et al. [2] demonstrated that reduced subsets of SNP markers could reproduce the results obtained from the full set of markers, concluding that GBS can be performed at higher DNA multiplexing. However, these results, as well as those by Ferguson et al. [59], indicated that a large numbers of SNPs may not be needed to achieve accurate identification of cassava

varieties, whether in farmers' fields or in formal germplasm collections.

Concurrently, CIAT and the Beijing Genome Institute (BGI) [153] committed to developing genomic resources in the post-genomic era, with the aim of increasing scientists' understanding of the evolution and distribution of cassava from its origin in the Americas to Africa and Asia. Next-generation sequence information from both wild and domesticated species offered cassava researchers the opportunity to investigate individual genes which have played a role in the domestication of cassava. Whole genome sequences allow researchers to exploit genomic variations associated with resistance to pests such as whiteflies or mites, and diseases such as frog skin disease and cassava brown streak disease, as well as to improve the nutritional value of the crops, such as by increasing the pro-vitamin A content. In 2013, CIAT's geneticists and bioinformaticians explored the genetic variation present in 150 LAC accessions, and identified a panel of 180 highly informative single nucleotide variants (SNVs, MAF > 0.25), with high discriminative power and a uniform genome distribution of 5 to 10 SNP per chromosome. These SNVs were transferred to a SNPtype™ allele-specific PCR assay and validated on the same set of samples (Fluidigm® Dynamic Array™, USA) (Becerra Lopez-Lavalle, personal communication). Of the 180 SNVs identified by CIAT, a 96 SNPs Fluidigm® Dynamic Array™ (referred to as an "SNPY-CHIP") was first assembled and used by Peña-Venegas et al. [105], who aimed to validate the identity of 173 Amazonian cassava landraces classified as unique by indigenous growers. The cassava SNPY-CHIP allowed the classification of 44 genotypes into 21 duplicate-genotype clusters, confirming the uniqueness of 150 (87%) of the 173 materials identified as unique by indigenous people of the Colombian Amazon. The SNPY-CHIP array also allowed the exploration of the diversity and population structure of these materials. When the 150 unique genotypes characterized in this study were compared with genotypes from the CIAT core collection, the cassava genotypes from the Tikuna community of San Martín de Amacayacu (AMA) appeared to be closely related to Peruvian manioc

**16**

#### **Figure 1.**

*Cluster analysis of 436 accessions constructed with the neighbor joining (NJ) method using shared alleles to define genetic distances.*

#### **Figure 2.**

*(A) The process of field-based varieties collection in Cauca, Colombia, (B&C) molecular-based selection, (D) cassava variety [CassVar] clustering for morphological validation based on molecular information and (E) agronomic performance evalaution of variety clusters.*

**19**

*Identification of Cassava Varieties in Ex-Situ Collections and Global Farmer's Fields: An Update…*

Branch. CIAT's SNPY-CHIP allowed for the characterization of 85 distinct genetic groups out of the 1570 genotypes collected, and indicated a 12.4% overestimation of varietal differences based on vernacular names given by local farmers. When compared against CIAT's global germplasm reference set, the allele diversity contained in 85 genetically distinct varieties represents a rich and diverse collection. Hence, a set of ten major varieties grown across Vietnam, named KM94, KM419, BRA1305, KM101, KM140, PER262, KM60, KM57, and two unidentified varieties with a high accounted for 82% of the frequency distribution, of which KM94 (KU50) and

**5. Conclusions: challenges and future perspective for the varietal and** 

general database, which could enable a global variety identification system.

Unlike SSRs, SNP alleles have been recommended for the construction of shared DNA fingerprinting databases [155]. CIAT's newly designed SNPY-CHIP has been used to genetically characterized approximately 2,100 cassava genotypes, collected from both farmers' fields and in ex-situ collections (**Table 2**). This set of 96 single SNPs are well-distributed throughout the cassava genome. These SNP markers have proven to be stable and repeatable, and have a high power of discrimination. The SNPY-CHIP alleles initially deployed in the Fluidigm® Dynamic Array™ technology (San Francisco, CA, USA) should be transferable across platforms, allowing for direct global data analysis, with SNP information coming from next-generation sequencing performed by other laboratories or research groups. CIAT, through CGIAR, has a collaborative agreement with Intertek-AgriTech (https://www.intertek.com/agriculture/agritech/) to access genotyping services, thus ensuring high quality, cost-effective data production. The emphasis on high quality breeding products stresses the need for quality control at all levels of the variety development pipeline, ensuring traceability and preservation of identity. This is the first step toward building a robust identification platform for the global conservation and use of cassava, as well as standardizing the administration and management of plant varieties. Considering the effectiveness the 96 SNPY-CHIP

This review has highlighted the potential of SNP-based variety identification in cassava, as a means to assess the rate of variety adoption, acquisition of novel genetic resources, and quality control of breeding products. Further progress toward a full characterization of varieties across all cassava growing regions, using SNP-based approaches, can be anticipated. Among the 21 morphological and 77 molecular-based variety identification studies used in this review (**Tables 1** and **2**), those based on morphological descriptors are lengthy, time consuming, labor intensive, and space demanding. As the number of varieties to be evaluated increases, the number of morphological descriptors available for the identification of new genotypes is limited. The basic principles of molecular marker technologies focus on the detection of polymorphisms, from protein or ribonucleic acid information. For cassava isozymes, RFLPs, RAPDs, SSRs, ISSRs, SRAPs, ISTR, AFLPs, DaRTs, and SNPs have been successfully used for detecting genetic variation in the crop (**Table 2**). Of these markers, SSRs are by far the most popular molecular method used by cassava scientists order to describe the differentiate among varieties and to measure the crop's genetic diversity. Nearly 17% of the 4,950 materials that underwent varietal identification were fingerprinted using SSR markers. However, SSR-based fingerprinting data has limited use outside the discrete experimental units evaluated in this review, thus limiting the opportunity to consolidate and globally use SSR genotyping information into a

*DOI: http://dx.doi.org/10.5772/intechopen.99110*

KM419 represented 48% of the genotypes investigated.

**cultivar identification of cassava**

*Identification of Cassava Varieties in Ex-Situ Collections and Global Farmer's Fields: An Update… DOI: http://dx.doi.org/10.5772/intechopen.99110*

Branch. CIAT's SNPY-CHIP allowed for the characterization of 85 distinct genetic groups out of the 1570 genotypes collected, and indicated a 12.4% overestimation of varietal differences based on vernacular names given by local farmers. When compared against CIAT's global germplasm reference set, the allele diversity contained in 85 genetically distinct varieties represents a rich and diverse collection. Hence, a set of ten major varieties grown across Vietnam, named KM94, KM419, BRA1305, KM101, KM140, PER262, KM60, KM57, and two unidentified varieties with a high accounted for 82% of the frequency distribution, of which KM94 (KU50) and KM419 represented 48% of the genotypes investigated.

#### **5. Conclusions: challenges and future perspective for the varietal and cultivar identification of cassava**

This review has highlighted the potential of SNP-based variety identification in cassava, as a means to assess the rate of variety adoption, acquisition of novel genetic resources, and quality control of breeding products. Further progress toward a full characterization of varieties across all cassava growing regions, using SNP-based approaches, can be anticipated. Among the 21 morphological and 77 molecular-based variety identification studies used in this review (**Tables 1** and **2**), those based on morphological descriptors are lengthy, time consuming, labor intensive, and space demanding. As the number of varieties to be evaluated increases, the number of morphological descriptors available for the identification of new genotypes is limited.

The basic principles of molecular marker technologies focus on the detection of polymorphisms, from protein or ribonucleic acid information. For cassava isozymes, RFLPs, RAPDs, SSRs, ISSRs, SRAPs, ISTR, AFLPs, DaRTs, and SNPs have been successfully used for detecting genetic variation in the crop (**Table 2**). Of these markers, SSRs are by far the most popular molecular method used by cassava scientists order to describe the differentiate among varieties and to measure the crop's genetic diversity. Nearly 17% of the 4,950 materials that underwent varietal identification were fingerprinted using SSR markers. However, SSR-based fingerprinting data has limited use outside the discrete experimental units evaluated in this review, thus limiting the opportunity to consolidate and globally use SSR genotyping information into a general database, which could enable a global variety identification system.

Unlike SSRs, SNP alleles have been recommended for the construction of shared DNA fingerprinting databases [155]. CIAT's newly designed SNPY-CHIP has been used to genetically characterized approximately 2,100 cassava genotypes, collected from both farmers' fields and in ex-situ collections (**Table 2**). This set of 96 single SNPs are well-distributed throughout the cassava genome. These SNP markers have proven to be stable and repeatable, and have a high power of discrimination. The SNPY-CHIP alleles initially deployed in the Fluidigm® Dynamic Array™ technology (San Francisco, CA, USA) should be transferable across platforms, allowing for direct global data analysis, with SNP information coming from next-generation sequencing performed by other laboratories or research groups. CIAT, through CGIAR, has a collaborative agreement with Intertek-AgriTech (https://www.intertek.com/agriculture/agritech/) to access genotyping services, thus ensuring high quality, cost-effective data production. The emphasis on high quality breeding products stresses the need for quality control at all levels of the variety development pipeline, ensuring traceability and preservation of identity. This is the first step toward building a robust identification platform for the global conservation and use of cassava, as well as standardizing the administration and management of plant varieties. Considering the effectiveness the 96 SNPY-CHIP

*Cassava - Biology, Production, and Use*

**18**

**Figure 2.**

**Figure 1.**

*define genetic distances.*

*(A) The process of field-based varieties collection in Cauca, Colombia, (B&C) molecular-based selection, (D) cassava variety [CassVar] clustering for morphological validation based on molecular information* 

*Cluster analysis of 436 accessions constructed with the neighbor joining (NJ) method using shared alleles to* 

*and (E) agronomic performance evalaution of variety clusters.*

markers, in 2021, we transferred them to the Intertek genotyping platform with the support from the Excellence in Breeding (EiB, https://excellenceinbreeding.org/ toolbox/collection/236), and 93 markers passed the validation stage with 345 diverse accessions from the genebank and breeding progenitors. These markers are publicly available in the EiB low-density genotyping platform for quality control and variety identification for the cassava community. A databased with more than 2,100 accessions genotyped using these 96 SNP markers has been developed and maintained in the cassava program at CIAT, which will enhance the variety identification and genetic diversity analysis for the global cassava community.

As growing emphasis is placed on quality, at all levels, and on traceability and the preservation of the identity of varieties, accurate identification of the varieties of cassava grown by farmers will improve its management and production, and facilitate tracking and replacing specific varieties. Breeders can replace varieties susceptible to pests and diseases with more tolerant or resistant varieties. Knowledge of the distribution of susceptible varieties will help policy makers to target breeding for the development of resistant of tolerant varieties for full varietal replacement and seed system development.

#### **Acknowledgements**

The author acknowledge the support of Ricardo Labarta, Tatiana Ovalle and Janneth Gutierrez in helping the author to demonstrate the use of molecular marker to generate cassava adoption estimates necessary for impact assessment studies, adoption in farmer's fields. Without them, the work considered in this paper would not have been possible. The author also appreciate the comments and suggestions from the anonymous reviewers. The funded was provided by CGIAR Research Program on Roots, Tubers and Bananas (RTBs) (Grant No: 02-2019-RTB II-CIAT) and USAID (Grant: MTO No. 069033).

#### **Conflict of interest**

The authors declare no conflict of interest.

#### **Author details**

Luis Augusto Becerra Lopez-Lavalle\*, Adriana Bohorquez-Chaux and Xiaofei Zhang International Center for Tropical Agriculture (CIAT), Palmira, Valle del Cauca, Colombia

\*Address all correspondence to: l.a.becerra@cgiar.org

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**21**

*Identification of Cassava Varieties in Ex-Situ Collections and Global Farmer's Fields: An Update…*

Cali, Colombia: International Center for

Tropical Agriculture. p. 54, 2019.

[9] Becerra López-Lavalle LA, Ovalle TM, Marín DV, Patricia GJ, Moreno MP, Moreno M, et al. Catálogo de variedades de yuca, Cauca-Colombia. In: International Center for Tropical A, editor. Palmira, Colombia: International

Center for Tropical Agriculture,

[10] CGIAR. Cassava to support a multibillion-dollar industry. https:// www.cgiar.org/innovations/cassava-to-

support-a-multibillion-dollar-

[11] Labarta R, Wossen T, Phuong Le D. The adoption of improved cassava varieties in South and Southeast Asia. The 9th ASAE International Conference. Bangkok, Thailand. Asian Society of Agricultural Economists (ASAE), 2017.

[12] Ocampo J, Ovalle T, Labarta R, Le DP, de Haan S, Vu NA, et al. DNA fingerprinting reveals varietal composition of Vietnamese cassava germplasm (*Manihot esculenta* Crantz) from farmers' field and gene bank collections. Plant Molecular Biology. 2021. DOI: 10.1007/s11103-021-01124-0.

[13] Kim H, Mai NTT, Mai NB, Howeler R. Cassava conservation and sustainable development in Vietnam. A new future for cassava in Asia: Its use as food, feed and fuel to benefit the poor. Proceedings of the 9th regional workshop, held in Quangxi, China,

[14] Malik AI, Kongsil P, Nguyễn VA, Ou W, Sholihin SP, Srean P, et al.

50 years of history and future directions. Breeding Science. 2020,70:145-66. DOI: 10.1270/

Cassava breeding and agronomy in Asia:

2020. 82 p.

industry/, 2021.

p. 1445-54.

2014. p. 35-56.

jsbbs.18180.

*DOI: http://dx.doi.org/10.5772/intechopen.99110*

[1] Kennedy G, Raneri JE, Stoian D, Attwood S, Burgos G, Ceballos H, et al.

Roots, tubers and bananas: Contributions to food security. In: Ferranti P, Berry EM, Anderson JR, editors. Encyclopedia of Food Security and Sustainability. Oxford: Elsevier,

[2] Rabbi IY, Kulakow PA, Manu-Aduening JA, Dankyi AA, Asibuo JY, Parkes EY, et al. Tracking crop varieties using genotyping-bysequencing markers: A case study using cassava (*Manihot esculenta* Crantz). BMC Genetics. 2015,16:115. DOI: 10.1186/s12863-015-0273-1.

[3] United Nations. Sustainable

[4] Floro VO, Labarta RA, Becerra López-Lavalle LA, Martinez JM, Ovalle TM. Household determinants of the adoption of improved cassava varieties using DNA fingerprinting to identify varieties in farmer fields: A case

study in Colombia. Journal of

DOI: 10.1111/1477-9552.12247.

University Press, 1959.

Publishing, 2002.

Agricultural Economics. 2018,69:518-36.

[5] Isendahl C. The domestication and early spread of manioc (*Manihot esculenta* Crantz): A brief synthesis. Latin American Antiquity. 2011,22:452- 68. DOI: 10.7183/1045-6635.22.4.452.

[6] Jones WO. Manioc in Africa. Manioc in Africa. California, USA: Stanford

[7] Hillocks RJ, Thresh JM, Bellotti AC. Cassava: Biology, Production and Utilization. Wallingford: CABI

[8] Le Thi DP, Labarta RA, de Haan S, Maredia M, Becerra López Lavelle LA, Nhu L, et al. Characterization of

Cassava Production Systems in Vietnam.

goals, 2015. Accessed 2020.

development goals. https://sdgs.un.org/

2019. 231-56 p.

**References**

*Identification of Cassava Varieties in Ex-Situ Collections and Global Farmer's Fields: An Update… DOI: http://dx.doi.org/10.5772/intechopen.99110*

#### **References**

*Cassava - Biology, Production, and Use*

diversity analysis for the global cassava community.

replacement and seed system development.

and USAID (Grant: MTO No. 069033).

The authors declare no conflict of interest.

**Acknowledgements**

**20**

**Author details**

**Conflict of interest**

and Xiaofei Zhang

Colombia

Luis Augusto Becerra Lopez-Lavalle\*, Adriana Bohorquez-Chaux

\*Address all correspondence to: l.a.becerra@cgiar.org

provided the original work is properly cited.

International Center for Tropical Agriculture (CIAT), Palmira, Valle del Cauca,

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

markers, in 2021, we transferred them to the Intertek genotyping platform with the support from the Excellence in Breeding (EiB, https://excellenceinbreeding.org/ toolbox/collection/236), and 93 markers passed the validation stage with 345 diverse accessions from the genebank and breeding progenitors. These markers are publicly available in the EiB low-density genotyping platform for quality control and variety identification for the cassava community. A databased with more than 2,100 accessions genotyped using these 96 SNP markers has been developed and maintained in the cassava program at CIAT, which will enhance the variety identification and genetic

As growing emphasis is placed on quality, at all levels, and on traceability and the preservation of the identity of varieties, accurate identification of the varieties of cassava grown by farmers will improve its management and production, and facilitate tracking and replacing specific varieties. Breeders can replace varieties susceptible to pests and diseases with more tolerant or resistant varieties. Knowledge of the distribution of susceptible varieties will help policy makers to target breeding for the development of resistant of tolerant varieties for full varietal

The author acknowledge the support of Ricardo Labarta, Tatiana Ovalle and Janneth Gutierrez in helping the author to demonstrate the use of molecular marker to generate cassava adoption estimates necessary for impact assessment studies, adoption in farmer's fields. Without them, the work considered in this paper would not have been possible. The author also appreciate the comments and suggestions from the anonymous reviewers. The funded was provided by CGIAR Research Program on Roots, Tubers and Bananas (RTBs) (Grant No: 02-2019-RTB II-CIAT)

[1] Kennedy G, Raneri JE, Stoian D, Attwood S, Burgos G, Ceballos H, et al. Roots, tubers and bananas: Contributions to food security. In: Ferranti P, Berry EM, Anderson JR, editors. Encyclopedia of Food Security and Sustainability. Oxford: Elsevier, 2019. 231-56 p.

[2] Rabbi IY, Kulakow PA, Manu-Aduening JA, Dankyi AA, Asibuo JY, Parkes EY, et al. Tracking crop varieties using genotyping-bysequencing markers: A case study using cassava (*Manihot esculenta* Crantz). BMC Genetics. 2015,16:115. DOI: 10.1186/s12863-015-0273-1.

[3] United Nations. Sustainable development goals. https://sdgs.un.org/ goals, 2015. Accessed 2020.

[4] Floro VO, Labarta RA, Becerra López-Lavalle LA, Martinez JM, Ovalle TM. Household determinants of the adoption of improved cassava varieties using DNA fingerprinting to identify varieties in farmer fields: A case study in Colombia. Journal of Agricultural Economics. 2018,69:518-36. DOI: 10.1111/1477-9552.12247.

[5] Isendahl C. The domestication and early spread of manioc (*Manihot esculenta* Crantz): A brief synthesis. Latin American Antiquity. 2011,22:452- 68. DOI: 10.7183/1045-6635.22.4.452.

[6] Jones WO. Manioc in Africa. Manioc in Africa. California, USA: Stanford University Press, 1959.

[7] Hillocks RJ, Thresh JM, Bellotti AC. Cassava: Biology, Production and Utilization. Wallingford: CABI Publishing, 2002.

[8] Le Thi DP, Labarta RA, de Haan S, Maredia M, Becerra López Lavelle LA, Nhu L, et al. Characterization of Cassava Production Systems in Vietnam. Cali, Colombia: International Center for Tropical Agriculture. p. 54, 2019.

[9] Becerra López-Lavalle LA, Ovalle TM, Marín DV, Patricia GJ, Moreno MP, Moreno M, et al. Catálogo de variedades de yuca, Cauca-Colombia. In: International Center for Tropical A, editor. Palmira, Colombia: International Center for Tropical Agriculture, 2020. 82 p.

[10] CGIAR. Cassava to support a multibillion-dollar industry. https:// www.cgiar.org/innovations/cassava-tosupport-a-multibillion-dollarindustry/, 2021.

[11] Labarta R, Wossen T, Phuong Le D. The adoption of improved cassava varieties in South and Southeast Asia. The 9th ASAE International Conference. Bangkok, Thailand. Asian Society of Agricultural Economists (ASAE), 2017. p. 1445-54.

[12] Ocampo J, Ovalle T, Labarta R, Le DP, de Haan S, Vu NA, et al. DNA fingerprinting reveals varietal composition of Vietnamese cassava germplasm (*Manihot esculenta* Crantz) from farmers' field and gene bank collections. Plant Molecular Biology. 2021. DOI: 10.1007/s11103-021-01124-0.

[13] Kim H, Mai NTT, Mai NB, Howeler R. Cassava conservation and sustainable development in Vietnam. A new future for cassava in Asia: Its use as food, feed and fuel to benefit the poor. Proceedings of the 9th regional workshop, held in Quangxi, China, 2014. p. 35-56.

[14] Malik AI, Kongsil P, Nguyễn VA, Ou W, Sholihin SP, Srean P, et al. Cassava breeding and agronomy in Asia: 50 years of history and future directions. Breeding Science. 2020,70:145-66. DOI: 10.1270/ jsbbs.18180.

[15] Edmond KK, Guy BK, Modeste KK, Michel KA, Gisèle KA-y, Pierre ZG. Enzymatic polymorphism of genetic diversity in cassava (*Manihot esculenta* Crantz) accessions in Côte d'Ivoire. Greener Journal of Biochemistry and Biotechnology. 2015,2:9-17. DOI: 10.15580/GJBB.2015.1.090814351.

[16] Agre AP, Bhattacharjee R, Rabbi IY, Alaba OA, Unachukwu NN, Ayenan MAT, et al. Classification of elite cassava varieties (*Manihot esculenta* Crantz) cultivated in Benin Republic using farmers' knowledge, morphological traits and simple sequence repeat (SSR) markers. Genetic Resources and Crop Evolution. 2018,65:513-25. DOI: 10.1007/ s10722-017-0550-0.

[17] Agre AP, Dansi A, Rabbi IY, Battachargee R, Dansi M, Gedil M, et al. Agromorphological characterization of elite cassava (*Manihot esculenta* Crantz) cultivars collected in Benin. International Journal of Current Research in Biosciences and Plant Biology. 2015,2:1-14.

[18] Hershey C. A Global Conservation Strategy for Cassava (*Manihot esculenta*) and Wild *Manihot* species, 2010.

[19] Boni N, Pamelas OM, Michel KA, Dibi KEB, Zohouri GP, Essis BS, Dansi AA. Morphological Characterization of Cassava (*Manihot esculenta* Crantz) Accessions Collected in the Centre-west, South-west and West of Côte d'Ivoire. Greener Journal of Agricultural Sciences. 2014,4:220-31.

[20] Ha CD, Ngoc Quynh LT, Hien NT, Ly Thu PT, Ham LH, Dung LT. Morphological characterization and classification of cassava (*Manihot esculenta* Crantz) in Vietnam. TAP Chi Sinh Hoc. 2016,38:8. DOI: 10.15625/0866-7160/v38n3.8570.

[21] Fukuda W, Guevara C, Kawuki R, Ferguson ME. Selected Morphological and Agronomic Descriptors for the Characterization of Cassava. Ibadan, Nigeria: IITA, 2010.

[22] Pujol B, Mühlen G, Garwood N, Horoszowski Y, Douzery EJP, McKey D. Evolution under domestication: Contrasting functional morphology of seedlings in domesticated cassava and its closest wild relatives. New Phytologist. 2005,166:305-18. DOI: 10.1111/j.1469-8137.2004.01295.x.

[23] Zago BW, Barelli MAA, Hoogerheide ESS, Corrêa CL, Delforno GIS, da Silva CJ. Morphological diversity of cassava accessions of the south-central mesoregion of the State of Mato Grosso, Brazil. Genetics and Molecular Research. 2017,16:1-10. DOI: 10.4238/ gmr16039725.

[24] Boampong E, Gyan D, Dorgbetor W. Morphological characterization of local and exotic cassava germplasm. Journal of the Ghana Science Association. 2016,17:45-52.

[25] Afonso SDJ, Alfredo TJDC, Canário JA, de Oliveira ANA, Abrantes AT. Genetic diversity of cassava varieties (*Manihot esculenta* Crantz) in the agro-morphological conditions of Malanje, Angola. IOSR Journal of Agriculture and Veterinary Science. 2020,13:36-44. DOI: 10.9790/2380-1304023644.

[26] João Afonso SD, Silva Ledo CAd, Cunha Moreira RF, e Silva SdO, Jesus Leal VDd, Silva Conceição ALd. Selection of descriptors in a morphological characteristics considered in cassava accessions by means of multivariate techniques. IOSR Journal of Agriculture and Veterinary Science. 2014,7:13-20.

[27] Koefender J, Golle D, Manfio C, Horn R, Schoffel A, De Oliveira J, et al. Caracterización morfológica Y agronómica de accesos en yuca en la

**23**

*Identification of Cassava Varieties in Ex-Situ Collections and Global Farmer's Fields: An Update…*

Energy Procedia. 2015,65:100-6. DOI:

[34] Kanagarasu S, Ganeshram S, Prem Joshua J, John Joel A. Exploration, collection and characterization of cassava landraces (*Manihot esculenta* Crantz.) grown in western ghats region. Electronic Journal of Plant Breeding.

Naïtormbaïdé M, Mbaïguinam JMM, Guisse A. Agro-morphological characterization of cassava (*Manihot esculenta* Crantz) cultivars from Chad. Agricultural Sciences. 2016,7:479-92.

10.1016/j.egypro.2015.01.039.

2014,5:310-6.

[35] Nadjiam D, Sarr PS,

DOI: 10.4236/as.2016.77049.

s10722-014-0209-z.

[36] Lebot V, Malapa R, Sardos J. Farmers' selection of quality traits in cassava (*Manihot esculenta* Crantz) landraces from Vanuatu. Genetic Resources and Crop Evolution. 2015,62:1055-68. DOI: 10.1007/

[37] Agre AP, Bhattacharjee R, Dansi A, Becerra Lopez-Lavalle LA, Dansi M, Sanni A. Assessment of cassava (*Manihot esculenta* Crantz) diversity, loss of landraces and farmers preference

criteria in [southern Benin using farmers' participatory approach. Genetic Resources and Crop Evolution.

2017,64:307-20. DOI: 10.1007/

[38] Oliveira EJd, Oliveira Filho OSd, Santos VdS. Selection of the most informative morphoagronomic descriptors for cassava germplasm. Pesquisa Agropecuária Brasileira. 2014,49:891-900. DOI: 10.1590/ S0100-204X2014001100008.

[39] Bhattacharjee R, Dumet D, Ilona P, Folarin S, Franco J. Establishment of a cassava (*Manihot esculenta* Crantz) core collection based on agro-morphological descriptors. Plant Genetic Resources.

2012,10:119-27. DOI: 10.1017/

S1479262112000093.

s10722-015-0352-1.

*DOI: http://dx.doi.org/10.5772/intechopen.99110*

region noroeste Del Rio grande do SUL, brasil. Holos. 2018,34:2-15. DOI: 10.15628/holos.2018.7747.

[28] Silva RS, Moura EF, Farias-Neto JT, Ledo CA, Sampaio JE. Selection of morphoagronomic descriptors for the characterization of accessions of cassava of the Eastern Brazilian Amazon. Genetics and Molecular Research. 2017,16:gmr16029595. DOI: 10.4238/

[29] Temegne NC, Mouafor BI, Ngome AF. Agro-morphological characterization of cassava (*Manihot esculenta* Crantz) collected in the humid

forest and guinea savannah agroecological zones of Cameroon. Greener Journal of Agricultural Sciences.

[30] Djaha KE, Abo K, Bonny BS, Kone T, Amouakon WJL, Kone D, Kone M. Agromorphological characterization of 44 accessions of cassava (*Manihot esculenta* crantz) grown in Côte d'Ivoire. International Journal of Biological and Chemical

[31] Ronaldo SG, Cleverson FdA, Jose RdSC, Ronaldo MJ, Fabio TD, Francisco CdSS, Derly JHdS. Genetic diversity in sweet cassava from the Brazilian Middle North Region and selection of genotypes

Agricultural Research. 2016,11:3710-9.

based on morpho-agronomical descriptors. African Journal of

DOI: 10.5897/AJAR2016.11267.

[32] Oliveira EJ, Oliveira Filho OS, Santos VS. Classification of cassava genotypes based on qualitative and quantitative data. Genetics and Molecular Research. 2015,14:906-24.

[33] Laila F, Zanetta CU, Waluyo B, Amien S, Karuniawan A. Early identification of genetic diversity and distance from Indonesia cassava

potential as food, industrial and biofuel based on morphological characters.

Sciences. 2017,11:174-84.

gmr16029595.

2016,6:209-25.

*Identification of Cassava Varieties in Ex-Situ Collections and Global Farmer's Fields: An Update… DOI: http://dx.doi.org/10.5772/intechopen.99110*

region noroeste Del Rio grande do SUL, brasil. Holos. 2018,34:2-15. DOI: 10.15628/holos.2018.7747.

*Cassava - Biology, Production, and Use*

[15] Edmond KK, Guy BK, Modeste KK, Michel KA, Gisèle KA-y, Pierre ZG. Enzymatic polymorphism of genetic diversity in cassava (*Manihot esculenta* Crantz) accessions in Côte d'Ivoire. Greener Journal of Biochemistry and Biotechnology. 2015,2:9-17. DOI: 10.15580/GJBB.2015.1.090814351.

and Agronomic Descriptors for the Characterization of Cassava. Ibadan,

[22] Pujol B, Mühlen G, Garwood N, Horoszowski Y, Douzery EJP, McKey D.

Evolution under domestication: Contrasting functional morphology of seedlings in domesticated cassava and

its closest wild relatives. New Phytologist. 2005,166:305-18. DOI: 10.1111/j.1469-8137.2004.01295.x.

[23] Zago BW, Barelli MAA, Hoogerheide ESS, Corrêa CL, Delforno GIS, da Silva CJ.

Morphological diversity of cassava accessions of the south-central

Brazil. Genetics and Molecular Research. 2017,16:1-10. DOI: 10.4238/

[25] Afonso SDJ, Alfredo TJDC, Canário JA, de Oliveira ANA, Abrantes AT. Genetic diversity of cassava varieties (*Manihot esculenta* Crantz) in the agro-morphological conditions of Malanje, Angola. IOSR Journal of Agriculture and Veterinary

Science. 2020,13:36-44. DOI: 10.9790/2380-1304023644.

Science. 2014,7:13-20.

[26] João Afonso SD, Silva Ledo CAd, Cunha Moreira RF, e Silva SdO, Jesus Leal VDd, Silva Conceição ALd. Selection of descriptors in a morphological characteristics considered in cassava accessions by means of multivariate techniques. IOSR Journal of Agriculture and Veterinary

[27] Koefender J, Golle D, Manfio C, Horn R, Schoffel A, De Oliveira J, et al.

Caracterización morfológica Y agronómica de accesos en yuca en la

gmr16039725.

2016,17:45-52.

mesoregion of the State of Mato Grosso,

[24] Boampong E, Gyan D, Dorgbetor W. Morphological characterization of local and exotic cassava germplasm. Journal of the Ghana Science Association.

Nigeria: IITA, 2010.

[16] Agre AP, Bhattacharjee R, Rabbi IY,

Ayenan MAT, et al. Classification of elite cassava varieties (*Manihot esculenta* Crantz) cultivated in Benin Republic

sequence repeat (SSR) markers. Genetic

Battachargee R, Dansi M, Gedil M, et al. Agromorphological characterization of elite cassava (*Manihot esculenta* Crantz)

[18] Hershey C. A Global Conservation Strategy for Cassava (*Manihot esculenta*)

[19] Boni N, Pamelas OM, Michel KA, Dibi KEB, Zohouri GP, Essis BS, Dansi AA. Morphological

Characterization of Cassava (*Manihot esculenta* Crantz) Accessions Collected in the Centre-west, South-west and West of Côte d'Ivoire. Greener Journal of Agricultural Sciences. 2014,4:220-31.

[20] Ha CD, Ngoc Quynh LT, Hien NT, Ly Thu PT, Ham LH, Dung LT. Morphological characterization and classification of cassava (*Manihot esculenta* Crantz) in Vietnam. TAP Chi

[21] Fukuda W, Guevara C, Kawuki R, Ferguson ME. Selected Morphological

Sinh Hoc. 2016,38:8. DOI: 10.15625/0866-7160/v38n3.8570.

and Wild *Manihot* species, 2010.

Alaba OA, Unachukwu NN,

using farmers' knowledge, morphological traits and simple

s10722-017-0550-0.

Biology. 2015,2:1-14.

Resources and Crop Evolution. 2018,65:513-25. DOI: 10.1007/

[17] Agre AP, Dansi A, Rabbi IY,

cultivars collected in Benin. International Journal of Current Research in Biosciences and Plant

**22**

[28] Silva RS, Moura EF, Farias-Neto JT, Ledo CA, Sampaio JE. Selection of morphoagronomic descriptors for the characterization of accessions of cassava of the Eastern Brazilian Amazon. Genetics and Molecular Research. 2017,16:gmr16029595. DOI: 10.4238/ gmr16029595.

[29] Temegne NC, Mouafor BI, Ngome AF. Agro-morphological characterization of cassava (*Manihot esculenta* Crantz) collected in the humid forest and guinea savannah agroecological zones of Cameroon. Greener Journal of Agricultural Sciences. 2016,6:209-25.

[30] Djaha KE, Abo K, Bonny BS, Kone T, Amouakon WJL, Kone D, Kone M. Agromorphological characterization of 44 accessions of cassava (*Manihot esculenta* crantz) grown in Côte d'Ivoire. International Journal of Biological and Chemical Sciences. 2017,11:174-84.

[31] Ronaldo SG, Cleverson FdA, Jose RdSC, Ronaldo MJ, Fabio TD, Francisco CdSS, Derly JHdS. Genetic diversity in sweet cassava from the Brazilian Middle North Region and selection of genotypes based on morpho-agronomical descriptors. African Journal of Agricultural Research. 2016,11:3710-9. DOI: 10.5897/AJAR2016.11267.

[32] Oliveira EJ, Oliveira Filho OS, Santos VS. Classification of cassava genotypes based on qualitative and quantitative data. Genetics and Molecular Research. 2015,14:906-24.

[33] Laila F, Zanetta CU, Waluyo B, Amien S, Karuniawan A. Early identification of genetic diversity and distance from Indonesia cassava potential as food, industrial and biofuel based on morphological characters.

Energy Procedia. 2015,65:100-6. DOI: 10.1016/j.egypro.2015.01.039.

[34] Kanagarasu S, Ganeshram S, Prem Joshua J, John Joel A. Exploration, collection and characterization of cassava landraces (*Manihot esculenta* Crantz.) grown in western ghats region. Electronic Journal of Plant Breeding. 2014,5:310-6.

[35] Nadjiam D, Sarr PS, Naïtormbaïdé M, Mbaïguinam JMM, Guisse A. Agro-morphological characterization of cassava (*Manihot esculenta* Crantz) cultivars from Chad. Agricultural Sciences. 2016,7:479-92. DOI: 10.4236/as.2016.77049.

[36] Lebot V, Malapa R, Sardos J. Farmers' selection of quality traits in cassava (*Manihot esculenta* Crantz) landraces from Vanuatu. Genetic Resources and Crop Evolution. 2015,62:1055-68. DOI: 10.1007/ s10722-014-0209-z.

[37] Agre AP, Bhattacharjee R, Dansi A, Becerra Lopez-Lavalle LA, Dansi M, Sanni A. Assessment of cassava (*Manihot esculenta* Crantz) diversity, loss of landraces and farmers preference criteria in [southern Benin using farmers' participatory approach. Genetic Resources and Crop Evolution. 2017,64:307-20. DOI: 10.1007/ s10722-015-0352-1.

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[54] Mezette TF, Blumer CG, Veasey EA. Morphological and molecular diversity

**25**

*Identification of Cassava Varieties in Ex-Situ Collections and Global Farmer's Fields: An Update…*

Identiffcation of duplicates in cassava germplasm banks based on single. Scientia Agricola. 2019,76:328-36. DOI:

10.1590/1678-992X-2017-0389.

[61] Moura EF, Farias Neto JTd, Sampaio JE, Silva DTd, Ramalho GF. Identification of duplicates of cassava accessions sampled on the North Region of Brazil using microsatellite markers. Acta Amazonica. 2013,43:461-7. DOI: 10.1590/S0044-59672013000400008.

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Evolution. 2004,51:205-9. DOI:

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Harisankar P, Sundaresan S. Isozyme analysis of indigenous cassava germplasm for identification of

duplicates. Genetic Resources and Crop

10.1023/B:GRES.0000020862.61748.26.

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Andrade LR, Mueller LA, de Oliveira EJ, Bauchet GJ. Comprehensive genotyping of a Brazilian cassava (*Manihot esculenta* Crantz) germplasm bank: Insights into diversification and domestication. TAG. Theoretical and Applied Genetics. Theoretische und Angewandte Genetik.

S1415-47571998000100018.

[65] Ogbonna AC, Braatz de

2021,134:1343-62. DOI: 10.1007/

[66] Fregene M, Bernal A, Duque M, Dixon A, Tohme J. AFLP analysis of African cassava (*Manihot esculenta*, Crantz) germplasm resistant to the cassava mosaic disease (CMD). Theoretical and Applied Genetics.

s00122-021-03775-5.

*DOI: http://dx.doi.org/10.5772/intechopen.99110*

molecular characterization of industrial

potential for adaptation to the conditions of cerrado of Central Brazil. Semina: Ciências Agrárias. 2013,34:567-82.

among cassava genotypes. Pesquisa Agropecuária Brasileira. 2013,48:510-8.

[55] Vieira EA, de Freitas Fialho J, Faleiro FG, Bellon G, da Fonseca KG, Silva MS, et al. Phenotypic and

purpose cassava accessions with

[56] Mamba-Mbayi G, Nkongolo K,

Senevirathna RWKM, Ranaweera LT, Wijesundara WWMUK, Jayarathne HSM, Weebadde CK, Sooriyapathirana SDSS. Characterization of cassava (*Manihot esculenta* crantz) cultivars in Sri Lanka using morphological, molecular and organoleptic parameters. Tropical Agricultural Research. 2019,30:51-70.

[58] Elibariki G, Njahira M, Wanjala B, Hosea K, Ndunguru J. Genetic diversity and identification of duplicates in selected Tanzanian farmer-preferred cassava landraces using simple sequence repeat (SSR) markers. Inernational Journal of Research in Plant Science.

[59] Ferguson ME, Hearne SJ, Close TJ, Wanamaker S, Moskal WA, Town CD, et al. Identification, validation and highthroughput genotyping of transcribed gene SNPs in cassava. TAG. Theoretical and Applied Genetics. Theoretische und Angewandte Genetik. 2012,124:685-95. DOI: 10.1007/s00122-011-1739-9.

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Oliveira EJ, Brito AC, de Andrade LRB, do Carmo CD, Morgante CV, et al.

Kalonji-Mbuyi A. Molecular relatedness and morpho-agronomic characteristics of congolese accessions of cassava (*Manihot esculenta* Crantz) for breeding purposes. British Biotechnology Journal.

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2014,4:551-65.

2013,3:81-7.

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among cassava genotypes. Pesquisa Agropecuária Brasileira. 2013,48:510-8.

*Cassava - Biology, Production, and Use*

Enseñanza, 2010.

[40] Torres Vargas LA. Caracterización morfológica de 37 accesiones de yuca (*Manihot esculenta* Crantz) del banco de germoplasma del Centro Agronómico Tropical de Investigación y Enseñanza (CATIE). Turrialba: Costa Rica, Centro Agronómico Tropical de Investigación y

[48] Gulick P, Hershey C,

in Ghana. African Journal of Biotechnology. 2013,10:13900-8.

Sciences. 2010,10:616-23.

[51] Rimoldi F, Vidigal Filho PS, Kvitschal MV, Gonçalves-Vidigal MC, Prioli AJ, Prioli SMAP, Costa TRd. Genetic divergence in sweet cassava cultivars using morphological

agronomic traits and RAPD molecular markers. Brazilian Archives of Biology and Technology. 2010,53:1477-86.

[52] Raghu D, Senthil N, Saraswathi T,

Venkatachalam R, et al. Morphological and simple sequence repeats (SSR) based finger printing of South Indian cassava germplasm. International Journal of Integrative Biology. 2007,1:141-8.

Raveendran M, Gnanam R,

[53] Balyejusa Kizito EB, Chiwona-Karltun L, Egwang T, Fregene M, Westerbergh A. Genetic diversity and variety composition of cassava on small-scale farms in Uganda: An interdisciplinary study using genetic

markers and farmer interviews. Genetica. 2007,130:301-18. DOI: 10.1007/s10709-006-9107-4.

[54] Mezette TF, Blumer CG, Veasey EA. Morphological and molecular diversity

Esquinas-Alcazar JT. Genetic Resources of Cassava and Wild Relatives. Rome: International Board for Plant Genetic Resources (IBPGR). p. 56, 1983.

[49] Asare P, Galyuon I, Sarfo J, Tetteh J. Morphological and molecular based diversity studies of some cassava (*Manihot esculenta* crantz) germplasm

[50] Benesi I. Ethnobotany, morphology and genotyping of cassava germplasm from Malawi'IRM. Benesi,"MT. Labuschagne," L. Herselman And N. Mahungu'Chitedze Research Station, PO Box 158, Lilongwe, Malawi "Department of Plant Sciences, University of the Free State, PO Box 339, Bloemfontein 9300, South Africa. Journal of Biological

[41] Vieira EA, Fialho JdF, Silva MS. Morphologic Characterization of Colored Cassava Accessions. Boletim de pesquisa e desenvolvimento Volume 241:15 p. Embrapa Cerrados, 2009.

[42] N'Zue B, Sangare A, Letourmy P, Zohouri G, Vernier P. Morphological characterization of the national cassava *Manihot esculenta* Crantz collection in Côte d'Ivoire. African Journal of Root

[43] de Queiroz Tavares Filho LF, da Silva Ledo CA, da Cunha Alves AA, Santos AS, Gonçalves LSA. Diversidade genética entre cultivares de mandioca e espécies

caracterização morfológica. Revista Raízes e Amidos Tropicais. 2009,5:697-701.

[44] Nassar NMA. Cassava, *Manihot esculenta* Crantz, genetic resources: Their collection, evaluation, and manipulation. In: Sparks DL, editor. Advances in Agronomy. Academic Press,

[45] Hershey C, Henry G, Best R,

[46] Fukuda WMG, Guevara CL.

Brasil: Embrapa. p. 38, 1998.

Q2.03. Ciat. 1983.

Rome, Italy: IFAD, 1997.

Iglesias C. Cassava in Latin America and the Caribbean: Resources for Global Development. Regional Review Report.

Descritores morfológicos e agronômicos para a caracterização de mandioca (*Manihot esculenta* Crantz). Embrapa Mandioca e fruticultura-documentos (INFOTECA-E). Cruz das Almas, Bahia,

[47] Dominguez CE. Morphology of the casava plant: Study guide. vol 04EC-

and Tuber Crops. 2009,7:32.

silvestres de *Manihot* mediante

1999. 179-230 p.

**24**

[55] Vieira EA, de Freitas Fialho J, Faleiro FG, Bellon G, da Fonseca KG, Silva MS, et al. Phenotypic and molecular characterization of industrial purpose cassava accessions with potential for adaptation to the conditions of cerrado of Central Brazil. Semina: Ciências Agrárias. 2013,34:567-82.

[56] Mamba-Mbayi G, Nkongolo K, Narendrula R, Djim PT, Kalonji-Mbuyi A. Molecular relatedness and morpho-agronomic characteristics of congolese accessions of cassava (*Manihot esculenta* Crantz) for breeding purposes. British Biotechnology Journal. 2014,4:551-65.

#### [57] Dissanayake UHK,

Senevirathna RWKM, Ranaweera LT, Wijesundara WWMUK, Jayarathne HSM, Weebadde CK, Sooriyapathirana SDSS. Characterization of cassava (*Manihot esculenta* crantz) cultivars in Sri Lanka using morphological, molecular and organoleptic parameters. Tropical Agricultural Research. 2019,30:51-70. DOI: 10.4038/tar.v30i4.8328.

[58] Elibariki G, Njahira M, Wanjala B, Hosea K, Ndunguru J. Genetic diversity and identification of duplicates in selected Tanzanian farmer-preferred cassava landraces using simple sequence repeat (SSR) markers. Inernational Journal of Research in Plant Science. 2013,3:81-7.

[59] Ferguson ME, Hearne SJ, Close TJ, Wanamaker S, Moskal WA, Town CD, et al. Identification, validation and highthroughput genotyping of transcribed gene SNPs in cassava. TAG. Theoretical and Applied Genetics. Theoretische und Angewandte Genetik. 2012,124:685-95. DOI: 10.1007/s00122-011-1739-9.

[60] de Albuquerque HYG, de Oliveira EJ, Brito AC, de Andrade LRB, do Carmo CD, Morgante CV, et al.

Identiffcation of duplicates in cassava germplasm banks based on single. Scientia Agricola. 2019,76:328-36. DOI: 10.1590/1678-992X-2017-0389.

[61] Moura EF, Farias Neto JTd, Sampaio JE, Silva DTd, Ramalho GF. Identification of duplicates of cassava accessions sampled on the North Region of Brazil using microsatellite markers. Acta Amazonica. 2013,43:461-7. DOI: 10.1590/S0044-59672013000400008.

[62] Sumarani GO, Pillai SV, Harisankar P, Sundaresan S. Isozyme analysis of indigenous cassava germplasm for identification of duplicates. Genetic Resources and Crop Evolution. 2004,51:205-9. DOI: 10.1023/B:GRES.0000020862.61748.26.

[63] Wossen T, Girma G, Abdoulaye T, Rabbi I, Olanrewaju A, Bentley J, et al. The Cassava Monitoring Survey in Nigeria. Ibadan, Nigeria: International Institute of Tropical Agriculture (IITA). p. 66, 2017.

[64] Colombo C, Second G, Valle TL, Charrier A. Genetic diversity characterization of cassava cultivars (*Manihot esculenta* Crantz).: I) RAPD markers. Genetics and Molecular Biology. 1998,21:105-13. DOI: 10.1590/ S1415-47571998000100018.

[65] Ogbonna AC, Braatz de Andrade LR, Mueller LA, de Oliveira EJ, Bauchet GJ. Comprehensive genotyping of a Brazilian cassava (*Manihot esculenta* Crantz) germplasm bank: Insights into diversification and domestication. TAG. Theoretical and Applied Genetics. Theoretische und Angewandte Genetik. 2021,134:1343-62. DOI: 10.1007/ s00122-021-03775-5.

[66] Fregene M, Bernal A, Duque M, Dixon A, Tohme J. AFLP analysis of African cassava (*Manihot esculenta*, Crantz) germplasm resistant to the cassava mosaic disease (CMD). Theoretical and Applied Genetics.

2000,100:678-85. DOI: 10.1007/ s001220051339.

[67] Xia L, Peng K, Yang S, Wenzl P, Vicente MCd, Fregene M, et al. DArT for high-throughput genotyping of cassava (*Manihot esculenta*) and its wild relatives. Theoretical and Applied Genetics. 2005,110:1092-8. DOI: 10.1007/s00122-005-1937-4.

[68] Elias M, McKey D, Panaud O, Anstett MC, Robert T. Traditional management of cassava morphological and genetic diversity by the Makushi Amerindians (Guyana, South America): Perspectives for on-farm conservation of crop genetic resources. Euphytica. 2001,120:143-57. DOI: 10.1023/ A:1017501017031.

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10.1007/s001220050922.

Breeding. 1999,5:263-73.

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**33**

**Chapter 2**

**Abstract**

*Andri Frediansyah*

increasing productivity.

phyllosphere, rhizosphere

**1. Introduction**

The Microbiome of Cassava

The plant microbiome, like the plant, influences the processes that lead to plant development, health, and crop productivity. Cassava is a perennial herbaceous plant native to South America that has been cultivated for centuries as a staple food throughout the world. Not only is cassava a good source of carbohydrates, but it also has a high tolerance for a variety of phenotypic conditions, and the majority of cassava plants are susceptible to a variety of diseases. Thus, using cassava as a model, this chapter discusses the plant microbiome. We discuss the structure and function of the microbiome, as well as the technique for studying microbiomes. Additionally, we conducted a systematic review of references pertaining to the microbiome of the cassava plant using cultivation-dependent or cultivation-independent methods. Numerous significant genera of bacteria and fungi are found in cassava's phyllosphere and rhizosphere, including groups of gram-negative bacteria, gram-positive Actinobacteria, and gram-positive non Actinobacteria. Additionally, we identified critical organisms in the phyllosphere and rhizosphere. Cassava endophytes also produce antifungal secondary metabolites such as pumilacidins and surfactin. The investigation of their phenotypes and interactions with the cassava plant will aid in

**Keywords:** cassava microbiome, metagenomic, plant microbiome, staple crop,

The microbiome was defined for the first time as the ecological niche within the human body where symbionts, pathogens, and commensal or neutral microorganisms coexist [1]. It is then widely used in a variety of habitats infested with microorganisms, including plants and their microbes. As with the plant itself, the plant microbiome influences the various processes that contribute to plant development, health, and crop productivity [2]. These connections have an effect on both nutrient absorption and susceptibility to biotic and abiotic stress [3]. Furthermore, factors such as regional landscape, plant species and cultivars, genotypes, soil, soil-borne microorganisms, climate and other environmental factors, farming management practices, and crop safety all influence the microbiome's dynamic and distribution [4–6]. Moreover, microbes associated with plants colonized both the plant's surface and internal tissue. They are frequently referred to as the plant's second genome due to their presence in the inner plant bodies as well [7]. Additionally, the complexity of nearly all plant microbiomes including its rhizosphere is still unknown [8].

(*Manihot esculanta*)

#### **Chapter 2**

*Cassava - Biology, Production, and Use*

Gkanogiannis A, Tohme J. Capturing next-generation genome wide molecular markers in cassava helps to untangle the Crop's genetic improvement history. Plant and animal genome Conference Volume XXVI. San Diego, California, USA: PAG, 2018, January 13-17, 2018. p. 1. genome sequencing of cassava. In: Russell N, Peng J, Yang B, editors. Shenzhen: EurekAlert, 2011.

[154] Ocampo J, Ovalle TM, Labarta R, Le DP, De Haan S, Nguyen AV, et al. DNA fingerprinting reveals varietal composition of Vietnamese cassava germplasm (*Manihot esculenta* Crantz) from farmers' field and gene bank collections. Plant Molecular Biology.

[155] Button P. The international Union for the Protection of New Varieties of Plants (UPOV) recommendations on variety denominations. In: Acta

Horticulturae International Symposium on the Taxonomy of Cultivated Plants 799 Groendijk-Wilders N, editor.

In press.

2008:191-200.

[147] Hamblin MT, Rabbi IY. The effects

properties of genotyping-by-sequencing libraries: A study in cassava (*Manihot esculenta*). Crop Science. 2014,54:2603- 8. DOI: 10.2135/cropsci2014.02.0160.

of restriction-enzyme choice on

[148] Vos P, Hogers R, Bleeker M, Reijans M, van de Lee T, Hornes M et al. AFLP: A new technique for DNA fingerprinting. Nucleic Acids Research.

1995,23:4407-14. DOI: 10.1093/

[149] Mba Rec., Mba REC, Stephenson P, Edwards K, Melzer S, Nkumbira J, Gullberg U et al. Simple sequence repeat (SSR) markers survey of the cassava (*Manihot esculenta* Crantz) genome: Towards an SSR-based molecular genetic map of cassava. Theoretical and Applied Genetics. 2001,102:21-31.

[150] Fregene M, Okogbenin E, Egesi C, Ogbe FO, Eke-Okoro N. UMUCASS 33. In: CIAT, ed. ICfTA, editor. Nigeria: Nigerian Seed Portal Initiative, 2010.

[151] Fregene M, Okogbenin E, Egesi C, Olasanmi B, Akinbo O, Kulakow P, et al. UMUCASS 41. In: CIAT, ed. ICfTA, editor. Nigeria: Nigerian Seed Portal

[152] Ferguson M, Rabbi I, Kim D-J, Gedil M, Lopez-Lavalle LAB,

Okogbenin E. Molecular markers and their application to cassava breeding: Past, present and future. Tropical Plant Biology. 2012,5:95-109. DOI: 10.1007/

announce collaboration for large-scale

Initiative, 2012. CR36-5 p.

s12042-011-9087-0.

[153] BGI, CIAT. BGI and CIAT

nar/23.21.4407.

CR41-10 p.

**32**
