Subscribe Now Subscribe Today
Research Article

Genetic Variability in Cassava as it Influences Storage Root Yield in Nigeria

O.O. Aina, A.G.O. Dixon and E.A. Akinrinde
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Genetic variability in shoot and root characteristics among 20 broad-based cassava genotypes was studied in four agroecological zones in Nigeria to determine its effects on root yield. Seventeen agronomic parameters were evaluated on a plot size of 40 m2, at spacing of 1x1 m in a Randomized Complete Block Design (RCBD) in four replicates. Analysis of variance (ANOVA) showed significant differences (p< 0.001) among genotypes within and across locations for most parameters. Significant genotype by environment (GxE) interaction effects was also observed. Estimates of genetic variances for phenotypic and genotypic coefficients of variation were higher for root characters than for shoot characters. PCV ranged from 4.3 to 36.5%; GCV ranged from 3.2 to 22.6%. Broad sense heritability (H2) estimates were high for root characters, ranging from 75 to 88.6%, but low for shoot characters, ranging between 10.6 and 38%. Consequently, considerable scope exists for the improvement of economic traits, such as storage root yield. Shoot traits have little control on storage root yield while root traits with high genetic influence had major control on storage root yield. Number of roots, root size and Harvest Index are major yield determinants to be considered when selecting for root yield in cassava.

Related Articles in ASCI
Similar Articles in this Journal
Search in Google Scholar
View Citation
Report Citation

  How to cite this article:

O.O. Aina, A.G.O. Dixon and E.A. Akinrinde , 2007. Genetic Variability in Cassava as it Influences Storage Root Yield in Nigeria. Journal of Biological Sciences, 7: 765-770.

DOI: 10.3923/jbs.2007.765.770



Cassava (Manihot esculenta Crantz) is the most important staple crop in the humid tropics serving as food for more than 250 million people in tropical Africa and 800 million people worldwide (FAO, 2004). Cassava is a reliable source of energy and can also be bred for enhanced nutritional quality such as micronutrients and proteins (CIAT, 2004). Cassava has moved from being a subsistence crop to a fully commercial crop, due to its income generating capacity and enormous potentials for industry, animal feed and human consumption. It has a comparative advantage over many other crops and can produce outstanding yields under harsh environmental conditions where other crops would fail (Nweke et al., 1994).

Yield improvement, a major thrust in crop improvement programmes, can be achieved by exploiting the genetic variability within a germplasm and the wide variability that exists among cassava genotypes for shoot and root characteristics can be utilized for improving storage root yield. The expression of yield depends on the functioning and interaction of many physiological components that vary among genotypes, thus making it difficult to improve yield directly. Progress in breeding for yield characters depends largely on the nature and magnitude of genetic variability. Genetic variation in a population, however, depends on the number and genetic diversity of parents used to form the population.

Selection for morphological characters will become useful when the performance of that character is correlated with the primary character hence, phenotypic correlation among characters provides a preliminary indication of the association between such characters. Number of storage roots, storage root size and Harvest Index showed high correlated responses with storage root yield (Ntawuruhunga et al., 2001). Earlier reports, however, indicated that root yield was significantly correlated to both shoot and root characters (Mahungu, 1983).

Recent studies on the genetic variability among cassava genotypes showed remarkable intravarietal variation (Asante and Offei, 2003; Zaldivar et al., 2004). This study was conducted to determine how genetic variation in shoot and root characteristics of cassava genotypes influences storage root yield in four agroecological zones of Nigeria.


Twenty broad-based cassava genotypes were evaluated at four agroecological zones in Nigeria; Ibadan (forest savanna transition), Mokwa (southern Guinea savanna) Zaria (northern Guinea savanna) and Mallamadori (Sudan savanna) for two cropping seasons (1999/2000 and 2000/2001) in a randomized complete block design with four replicates. The climatic and soil characteristics vary for each of the agroecological zones (Table 1).

Table 1:

Climatic and soil characteristics of experimental sites in Nigeria

Masl-meters above sea level, Source: Ibadan: Geo-Spatial Lab (IITA), Ibadan, Oyo State; Mokwa: NCRI, substation, Mokwa, Niger State; Zaria: ABU Teaching and Research farm, Zaria, Kaduna State; Mallamadori: IITA experimental plot, Mallamadori, Jigawa State

Plants were established under rainfed conditions using healthy stakes 25 cm long at a spacing of 1x1 m in 40 m2 plots of 4 rows of 10 plants per row. Weeding was done manually when necessary and neither fertilizer nor pesticides was applied. Morphological characterization data were collected at 3, 6 and 9 Months After Planting (MAP), for plant height, stem girth, number of nodes, height to first branching, leaf size and leaf retention. Yield data were collected at harvest, 12 MAP on parameters such as fresh root weight (kg), fresh shoot weight (kg), total number of roots, root size categorized based on storage root weight and size into small sized roots (size 3), medium sized roots (size 5) and large sized roots (size 7). Harvest Index (HI) and root dry matter percentage (RTDM) were also determined.


All data generated were analyzed using the Statistical Analytical System (SAS), version, (1996). Analysis of variance (ANOVA) was carried out on plot mean basis using the Generalized Linear Model (GLM) procedures for randomized complete block design. Estimates of variance components were obtained by equating the observed mean squares from ANOVA with their Expected Mean Squares (EMS). Phenotypic variances for varied combination of years and locations are calculated as derived from the formulae by Obilana and Fakorede (1981). Broad sense heritability, phenotypic (PCV) and genotypic (GCV) coefficients of variation were computed using the formula by Singh and Chaudhary (1985). Principal component analysis (PCA) was done using the PROC PRINCOMP procedures of SAS (1996). Linear regression among traits was determined with PROC REG procedures of SAS.

Variance component estimation: Variances were estimated from EMS as follows:

σ2g = (Msg + Msgly - Msgl - Msgy) /rly
σ2gl = (Msgl – Msgly) /ry
σ2gy = (Msgy – Msgly) /rl
σ2gly = (Msgly – Mse) /r
σ2e = Mse
σ2g = genotypic variance
σ2gl = variance due to genotype and location
σ2gly = variance due to genotype, location and year
σ2e = error variance,
r = replicate,
Ms = Mean Squares

Phenotypic variance (σ2ph) across locations was calculated as follows:

r = number of replicates,
l = number of locations,
y = number of years (Obilana and Fakorede, 1981)

Phenotypic coefficients of variation

Genotypic coefficients of variation

Broad sense heritability (H2)


H2 = Broad sense heritability
σ2p = Phenotypic variance
σ2g = Genotypic variance
x = mean of the population

(Singh and Chaudhary, 1985).


The analysis of variance across locations (Table 2) revealed highly significant differences (p<0.001 among genotypes for most parameters evaluated. The percentage coefficient of variation (CV%) ranged from 17.2 (stem girth) to 86.0 (size 7 roots). Plant height followed by leaf size had the widest ranges in mean values while shoot weight and stem girth had the smallest There was highly significant (p<0.001) mean squares (MS) among genotypes for most traits within and across locations (Table 3).

Table 2:

Phenotypic variability among twenty cassava genotypes for seventeen characters

**, *** significant level at P< 0.01 and 0.001, Stem with leaves = length of stem with leaves, First branch ht = height to first branching, Stands at harvest= number of stands harvested, Size 3= small sized roots, Size 5= medium sized roots, Size 7= large sized roots ranges.

Table 3:

Mean squares from combined analysis of variance for 20 genotypes for shoot and root characters

*,**, *** significant level at P< 0.05, 0.01and 0.001 respectively, ns= not significant

Table 4:

Estimates of genetic variances, phenotypic (PCV) and genotypic (GCV) coefficient of variation and broad sense heritability for 17 characters

*,**, *** significant level at p<0.05, 0.01 and 0.001, Stands at harvest = number of stands harvested, Size 3 = small sized roots, Size 5 = medium sized roots, Size 7 = large sized roots

Table 5:

Eigenvalues for the first four principal components and eigenvectors for 15 characters

Estimates of genetic variances, PCV and GCV and broad sense heritability (H2) for various traits across the four agroecological zones (Table 4) showed wide variability among genotypes for genetic parameters. PCV, GCV and H2 values were higher for root characters than for shoot characters, PCV ranged from 4.27% (number of nodes) to 36.5% (shoot weight) and GCV ranged from 3.2% (number of nodes) to 32.4% (shoot weight). H2 ranged from 10.6% (number of stands harvested) to 88.6% (size 3 roots) and was high (>70%) for plant height (73.2%), number of roots (75.0%), root dry matter (79.8%) and small sized roots (88.6%). Moderate H2 (50-69%) obtained for Harvest Index, root yield, stem number and number of nodes; heritability estimates were low (<50%) for stem with leaves, stem girth, leaf size, first branching height and number of stands at harvest.

Fig. 1:

Relationship between root yield and other yield traits for 20 genotypes

Results of the PCA (Table 5) showed that eigenvalues of the first four Principal Components (PC) accounted for a total variation of 67.8%, with all the PCS having eigenvalues greater than 1. PC 1 alone accounted for 35.1% of the total variance which was mostly controlled by root parameters. PC 2 accounted for a smaller fraction of total variance (13.2%) and was largely controlled by shoot parameters. PC 3 accounted for 11% of total variance and was largely due to the influence of both shoot and root characters, PC 4 accounted for the least variation (9.0%), which was largely due to root characteristics. The coefficient of determination (R2) for linear regression ranged from 0.17 for number of plants harvested to 0.70 for medium sized roots (Fig. 1).


The highly significant MS that were observed among genotypes within and across locations, as well as the significant genotype by environmental interaction (GxE) indicated significant variability in the performance of genotypes across the different agroecological zones due to differential response of the genotypes to diverse edaphic, climatic and biotic conditions in each agroecology. The range of the CV% indicated low to very high variation among the genotypes. Root characters that showed high CV% suggest wide variability for such characters which could be utilized in improving storage root yield in cassava. The widest range observed for plant height in this study was in line with the findings of Naskar et al. (1989). A high estimate of H2 was also observed for plant height, indicating high genetic variability for this trait. Plant height, however, does not show any correlated response with root yield, indicating that the high genetic variance that was observed for plant height has little or no direct linkage with root yield. The study of Makame (1995), however, showed that plant height had the narrowest variation out of all the variables that were evaluated.

Higher PCV values than GCV values indicated the considerable role of the environment on the expression of these characters. Similar observations were made by Kamalam et al. (1978) and Birader et al. (1978). Number of nodes and stem girth had the lowest PCV and GCV while shoot weight, large sized roots and small sized roots had the highest, indicating the possibility of improving these characters through selection in a breeding programme. The degree of success in selection however, depends on the magnitude of heritable variations determined through heritability and also on genetic advance (Panse, 1957). Estimated H2 that ranged from low to very high suggested that characters with low heritability have high environmental influence while those with high heritability have high genetic influence. Most of the shoot characters, with the exception of plant height, with low to moderate H2, therefore showed a high level of environmental influence and hence cannot be improved upon through breeding efforts. Most of the root characters with moderate to H2 indicated that these characters, with high genetic influence, can be improved upon and direct selection for these traits based on their phenotypic performance can be successfully achieved through breeding. PC 1, which accounts for the highest proportion of the total variance, is largely controlled by root parameters. PC2 is largely controlled by shoot parameters; PC3 is controlled by both root and shoot parameters. The larger proportion of the observed variability was being controlled largely by root parameters. The high direct effect observed for root number and root size from path analysis indicates that they both play a major role in determining yield. Hence, selection should be concentrated on producing large sized storage roots as well as on increasing the number of roots produced, because a preponderance of small-sized roots will result in a negative effect on yield. Environmental factors that could result in a reduction in number and size of storage roots should be investigated and eliminated through any possible means. The preponderance of small-sized roots will have an adverse effect on final yield as an increase in the number of roots is not accompanied with a proportional increase in final weight. Increasing production of shoot growth over root growth may also result in a negative effect on final storage root yield. Remarkable intravarietal variation observed among cassava genotypes that were evaluated in this study fell in line with the findings of Asante and Offei (2003) and Zaldivar et al. (2004).


Assessment of variability is therefore a prerequisite for crop improvement to judge the potential of the population as a base for genetic improvement. Significant variability has been observed among cassava cultivars and selection of desirable characters will lead to progress in plant genetic improvement. Root number and size with high heritability contributed significantly to yield in cassava and could be used as important selection criteria in the enhancement of genetic improvement of cassava.


The research reported here is a part of the results of a thesis work carried in Nigeria at the International Institute of Tropical Agriculture, IITA in collaboration with Department of Agronomy, University of Ibadan, Nigeria. Cassava breeding unit, IITA provided funds and material support to carry out this research.

Asante, I.K. and S.K. Offei, 2003. RAPD-based genetic diversity study of fifty cassava (Manihot esculenta Crantz) genotypes. Euphytica, 131: 113-119.
Direct Link  |  

Birader, R.S., P.G. Rajendran and N. Hrishi, 1978. Genetic variability and correlation studies in: Cassava (Manihot esculenta Crantz). J. Root Crops, 4: 7-10.

CIAT., 2004. Annual report project IP3: Improved cassava for the developing world. CIAT, Cali, Colombia.

FAO., 2004. Food and agriculture organization of the United Nations Rome. FAOSTAT Database Collections.

Kamalam, P., R.S. Birader, N. Hrishi and P.G. Rajendran, 1978. Path analysis and correlation studies in sweetpotato (Ipomoea batatas Lam). J. Root Crops, 3: 5-11.

Mahungu, N.M., 1983. Relationships among selected agronomic characters and their effects on storage toot yield of cassava (Manihot esculenta Crantz). Ph.D. Thesis. University of Ibadan, Nigeria, pp: 193.

Makame, M., 1995. Genetic variation, stability of performance of cassava clones and their responses to intercropping with sweet potato in Zanzibar. Ph.D. Thesis. University of Ibadan, Nigeria, pp: 248.

Naskar, S.K., D.P. Singh and G. Srinivasan, 1989. Performance of cassava varieties in the laterite soils of Bhubaneswar and their yield stability. J. Root Crops, 15: 29-31.

Nweke, I., A.G.O. Dixon, R. Asiedu and S.A. Folayan, 1994. Cassava varietal needs of farmers and the potential for production growth in Africa. Cosca Working Paper No. 10. Collaborative Study of Cassava in Africa. International Institute of Tropical Agriculture, Ibadan, Nigeria.

Obilana, A. and M.A.B. Fakorede, 1981. Heritability: A treatise. Samaru J. Agric. Res., 1: 72-82.

Panse, V.G., 1957. Genetics of qualitative characters in relation to plant breeding. Indian J. Genet. Plant Breed., 17: 318-328.

Rubaihayo, P.R., J.B.A. Whyte, A.G.O. Dixon and D.S.O. Osiru, 2001. Inter-relationships among traits and path analysis for yield components of cassava: A search for storage root yield indicators. Afr. Crop Sci. J., 9: 599-606.
CrossRef  |  Direct Link  |  

SAS Institute, 1996. SAS Software Release 6.12. SAS Institute Inc., Cary, North Carolina, USA.

Singh, R.K. and B.D. Chaudhary, 1985. Biometrical Methods in Quantitative Genetic Analysis. Kalyani Publishers, New Delhi, Pages: 300.

Zaldivar, M.E., O.J. Rocha, G. Aguilar, L. Castro, E. Castro and R. Barrantes, 2004. Genetic variation of cassava (Manihot esculenta Crantz) cultivated by Chibehan Amerindians of Costa Rica. Econ. Bot., 58: 204-213.
Direct Link  |  

©  2020 Science Alert. All Rights Reserved