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CROP BREEDING, GENETICS & CYTOLOGY

Diallel Analysis in Cassava Adapted to the Midaltitude Valleys Environment

^a International Center for Tropical Agriculture (CIAT), Apartado Aéreo 6713, Cali, Colombia
^b Univ. Nacional de Colombia, Carrera 32, Chapinero vía Candelaria, Palmira, Colombia

^* Corresponding author (h.ceballos{at}cgiar.org)

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Cassava (Manihot esculenta Crantz) is an important commodity for industrial processes in tropical countries as one of the few alternatives to compete with imported maize (Zea mays L.). To maintain this competitiveness, cassava breeding needs to be as efficient as possible. This study provides one of the first attempts to produce quantitative genetic data to aid breeding efficiency, through the analysis of a diallel set among nine parental clones adapted to the midaltitude valleys environment. Thirty clones represented each F₁ cross (with three exceptions). Evaluations were conducted in two contrasting environments with three replications in each location. The specific combining ability (SCA) effects were relatively more important than general combining ability (GCA) effects for root yield. In the case of harvest index, dry matter content (DMC) and plant type architecture GCA effects were about twice as large as those from SCA effects. Reaction to mites (Mononychellus tanajoa Bondar) and white flies (Aleurotrachelus socialis Bondar) (based on single-location data) showed the strongest influence of GCA effects on the expression of a given trait. Yield data demonstrates the excellent potential of this crop for the tropical production of starch and source of energy in animal feed that could compete with the international markets.

Abbreviations: DMC, dry matter content • GCA, general combining ability • SCA, specific combining ability

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
CASSAVA, ALONG WITH maize, sugarcane (Saccharum officinarum L.), and rice (Oryza sativa L.), constitute the most important sources of energy in the diet of most tropical countries of the world. The species originated in South America (Allem, 2002; Olsen and Schaal, 2001), and was domesticated about 5000 yr ago. Until recently, cassava and its products were little known outside the tropical and subtropical regions where it grows. Compared with other staple foods, little scientific effort has been made to improve the crop (Cock, 1985). The most common cassava product is the starchy root, but the foliage has an excellent nutritional quality for animal and human consumption and offers great potential. Cassava is the fourth most important basic food after rice, wheat, and maize, and is a fundamental component in the diet of millions of people (Food and Agricultural Organization and International Fund for Agricultural Development, 2000). Scott et al. (2000) estimated that, for the 1995 to 1997 period, annual production of cassava was about 150 million Mg, with a value of approximately $8.8 billion ($US).

Cassava is a rustic crop that grows well in conditions where few other crops survive: it is drought tolerant, can produce in degraded soils, and offers resistance to its most important diseases and pests. It is naturally tolerant to acidic soils and offers the convenient flexibility to be harvested when the farmers need it. Cassava has benefited by technological inputs in the area of breeding (Kawano et al., 1998; Kawano, 2003) to successfully satisfy the needs of farmers and processors. The general scheme for cassava breeding is indeed a phenotypic masal selection. Large numbers of segregating genotypes is evaluated in a lengthy process that requires as many as 6 yr for completion (Jennings and Iglesias, 2002). Individual genotypes (clones) are selected and then multiplied to take advantage of the vegetative propagation of the crop.

Little progress in understanding the inheritance of agronomic traits has been achieved. Few articles regarding the inheritance of quantitative traits have been published (Easwari Amma et al., 1995; Easwari Amma and Sheela, 1998; Losada Valle, 1990). In this regard, cassava shows a unique situation because a molecular map has been already developed (Fregene et al., 1997; Mba et al., 2001) and yet it is complemented with limited knowledge on traditional genetics. The objective of this study was to obtain information on the inheritance of traits with agronomic relevance in cassava so that a more scientifically based approach for improving them could be implemented.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Controlled pollinations were performed following the standard procedures described by Kawano (1980). Nine clones were planted and crossed in the breeding nurseries at CIAT experimental station in Palmira (Valle del Cauca Department, Colombia) to produce segregating progenies adapted to the midaltitude valleys environment. MECU 72 (a landrace from Ecuador) was one of the parents and has host plant resistance to the white fly (Bellotti et al., 1999, Bellotti and Arias, 2001).

Botanical seed produced from the crosses were planted in screen houses and transplanted to the field after 2 mo at CIAT station in Palmira. A total of 8639 genotypes (botanical seed) were produced out of the nine parents involved in this study (Table 1). The average number of seeds produced for each cross was about 240 (ranging from 26 to 791 seeds per cross). From the seed produced, a total of 3355 were germinated and about 90.5% (3037) produced seedlings vigorous enough to be transplanted (an average of 84.4 seedlings per cross, ranging from 22 to 113). A total of 2610 (85.9%) of the transplanted seedlings developed vigorous plants from which vegetative cuttings could be obtained. To represent each cross, 30 plants were randomly chosen. At harvest time, six vegetative cuttings from these 30 plants were obtained. Minor selection was unavoidable at this stage based on the capacity of the botanical seeds to produce vigorous seedlings and plants capable of producing six good quality vegetative cuttings. These factors determined the group of clones representing each F₁ cross in this study.

Trials were planted during August 2001 in two contrasting midaltitude valley locations in the Valle del Cauca Department, Colombia: Jamundí and Palmira (3°16' N and 76°32' W). Jamundí is at 889 m above sea level, has acid soils with a 5.1 pH, low P content (3.88 ppm), moderate Al saturation levels (12.7%), and FAr texture (Typic Dystrandept). Palmira is at 965 m above sea level, and has contrasting soil conditions with a 7.8 pH, adequate P availability (>60 mg kg^–1), very low Al saturation levels (<1%) and ArLi texture (Typic Pellustert). Irrigation was supplied as required at both locations; however, breaks in rainfall were typically less than 1 mo. No fertilizer was applied to the trial in Palmira. At Jamundí, the following fertilizers were applied 1 mo after planting: 200 kg ha^–1 DAP (18–16–0 NPK) and 100 kg ha^–1 K Cl (0–0–60 NPK).

The white fly is a frequent problem for cassava fields in the Cauca and Valle del Cauca Departments. In the case of Jamundí, there was severe white fly pressure during the evaluation of the diallel study. In the case of Palmira (CIAT Experimental Station), the cycle of this fly is interrupted every year by eliminating cassava for a period of 1 mo. As a result, very little A. socialis pressure is now observed in the cassava fields at this station.

A randomized complete block design was used for the field evaluation. Experimental plots consisted of 30 plants, from each of the 30 clones representing each cross (in the three cases where <30 clones represented a given F₁ cross, the experimental units were filled with a local check to maintain a uniform plot size and plant density). Therefore, the six vegetative cuttings obtained from each plant in the nursery at Palmira were distributed in the three replications at the two locations for the evaluation trials. The distance between plants was the standard 1 by 1 m, resulting in a 10000 plants ha^–1 density. Trials were harvested in May 2002, 10 mo after planting, following local crop management practices and breeding program procedures (Ceballos et al., 2004).

Plants were hand harvested individually and results averaged across the 30 clones of each F₁ cross. All the roots produced by each plant were weighted as well as the above ground biomass (stem and foliage). Harvest index was measured as the ratio between fresh root weight and total fresh biomass. Dry matter content in the roots was estimated using the specific gravity methodology (Kawano et al., 1987). Approximately five kilograms of roots were weighed in a hanging scale (W_A), and then the same sample was weighed with the roots submerged in water (W_W). Dry matter content was estimated using the following formula:

where W_A = weight in the air and W_W = weight in water.

Reaction to mites at Palmira and white flies at Jamundí were scored using a 1-to-5 scale where 1 = little damage and 5 = extensive damage. Plant type score took into consideration several important characteristics. The ideal plant (Score 1) has intermediate height (2–3 m), erect architecture with few branches and reduced branching angle, adequate capacity to produce vegetative cuttings, and limited incidence of foliar diseases (which in this particular environment are not frequent), and pests. Undesirable plant types (Score 5) include weak plants or excessively vigorous ones (>3 m), plants that branch profusely with wide angles, lodge, and/or show susceptibility to diseases and pests.

The ANOVA follows the Method 4 proposed by Griffing (1956). Genotypes and environments were considered fixed and random effects, respectively. Phenotypic correlations were estimated (Steel and Torrie, 1960) using the averages across locations for each F₁ cross. In addition to the variables described above, root type score, foliage yield, and dry matter yield were also considered for the estimation of phenotypic correlations. Root type score ranged from 1 (excellent) to 5 (poor) and took into consideration uniformity, size, shape, and general health. Fresh foliage yield was estimated from the above ground biomass, and dry matter yield is a combination of the fresh root yield and root DMC. In general, it is convenient to analyze fresh root yield and DMCs separately to avoid selecting clones with high dry-matter productivity based on high fresh root yields but low DMC. Different industries penalize or even reject cassava roots with low DMC.

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Average harvest index and DMCs in the two locations differed, but not fresh root productivity (Table 2). For the two variables measured at only one location (reactions to mites in Palmira and to white flies in Jamundí), differences were found among the averages of the 36 crosses (P < 0.01), with significant GCA effects for both traits and significant SCA effects for mite scores at Palmira (Table 2).

Significant genotype-by-environment interactions were found for the four variables (fresh root yield, harvest index, DMC, and plant type) that were measured in both locations (Table 2). Interactions between environment and GCA effects were important for fresh root yield, DMC, plant type, and harvest index. Interaction occurred between SCA effects and the environment for plant type and harvest index.

The proportion of the crosses sum of squares explained by GCA and SCA effects is contrasting for the different variables evaluated (Table 2). Most of the variability ( 85%) for the reactions to pests, was explained by GCA effects. The GCA effects accounted for 60 to 70% of the genetic variability measured for harvest index, DMC, and plant type score. The lowest amount of variation explained by GCA effects was for fresh root yield (41%).

Table 3 presents the GCA effects for each parental line in the study. MPER183, a landrace from Peru, showed the highest GCA value for fresh root yield, followed by SM 1219-2 and MECU72. SM 1278-2 had the lowest GCA value followed by SM 1636-4, HMC1, and SM 1673-10. Unfortunately, the good yield potential of MECU 72, MPER 183, and SM 1219-9 contrasts with their poor performance in relation to DMC. On the other hand, the poor yield potential of SM 1278-2 was compensated by its good DMC, whose GCA value was the second highest.

Mean fresh root productivity across the two locations was 46.7 Mg ha^–1 (Table 4) considering the plant densities used. Mean DMC in the roots was 33.63%, leading to an average dry matter productivity of 15.7 Mg ha^–1. These results highlight cassava's capacity to compete in the tropics with other commodities such as maize.

Parent 6 (SM 1741-1) was involved in four of the seven best crosses for harvest index (Table 4). This agrees with the GCA value for this parent. Similarly, Parent 8 (MECU 72) had the lowest GCA value for harvest index and was involved in five of the worst seven crosses for this trait. With the exception of cross 5 x 6 (SM 1673-10 x SM 1741-1), the highest averages for harvest index were not necessarily based on highly positive SCA values. This would suggest that, relatively speaking, GCA effects are more important for harvest index than for mean productivity. This finding is also supported by results from Table 2. Correlation coefficient between average harvest index for the 36 crosses and their respective SCA effects was 0.44.

Strong SCA effects were not clearly involved in high averages for DMC. Only seven of the eleven highest crosses for this trait had positive SCA effects. The remaining four crosses had negative SCA effects (Table 4). Results from the ANOVA (Table 2), however, indicated important SCA effects and negligible ones for additive effects. The correlation coefficient between DMC in the roots for the 36 crosses and their respective SCA effects was 0.61, further confirming the importance of nonadditive effects in DMC.

Table 5 presents the phenotypic correlations among F₁ family averages (combined across locations except for white flies and mite scores, which are based on single-location evaluations) for the most relevant traits evaluated in this study. There was a positive correlation (r = 0.76) between the scores to the two pests analyzed. This finding agrees with results presented in Table 3, where MECU 72 showed the lowest GCA values for both pests. The only contrasting reaction was found in parent SM 1741-1, which showed the highest positive GCA value for white flies (0.604), but had a slightly negative GCA effect for mites score (–0.007). The second largest correlation for white flies score was found with foliage yield. As expected, the correlation was negative, indicating that a low score for the insect (resistance) was associated with high foliage yield. Reaction to white flies was marginally associated with root yield (r = –0.20) and reduced harvest index (r = 0.44). This last finding may be largely due to the poor harvest index of the source of resistance to white flies (MECU 72), which showed the lowest GCA effect for harvest index (–0.048).

Foliage yield was associated with resistance to pests as already mentioned (Table 5). It was also correlated with fresh root yield (r = 0.68). Harvest index showed the highest correlations with resistance to pests and DMC (r 0.45 for the three variables) and, as expected, a negative correlation with foliage yield (–0.55). Roots with better aspect tended to come from genotypes with higher harvest index (r = –0.47).

This study for cassava germplasm adapted to midaltitude valleys was accompanied by two other studies targeting acid-soil savannas and subhumid environments, which are being published elsewhere. Together, the three studies will provide a valuable source of information about the inheritance of agronomically relevant traits. Although the genetic effects for each particular study were considered fixed, the combined information, when available, will be useful to start consolidating information that could be generalized to cassava at large. Preliminary results of the three studies can be found at CIAT's Annual Report for the cassava-breeding project (CIAT, 2003).

The statistical significance of GCA and SCA effects was clearly affected by their respective error terms (interactions with the environment). The GCA effects were not significant or only at the 0.05 probability level for DMC and fresh root yield, respectively. In both cases, the GCA x environment interaction was very high. On the other hand, the SCA interactions with the environment generally were not significant, and that resulted in highly significant SCA effects for all the variables evaluated in the two locations with the exception of plant type (which was the only characteristic that showed highly significant SCA x environment interaction).

To weight the relative importance of GCA and SCA effects in the expression of the different traits, the proportion of the crosses sum of squares explained by each effect is provided in Table 2. This information, together with the correlation between actual cross averages and the SCA effects listed in Table 4, can give a useful indication of the relative importance of GCA and SCA effects. In general, the higher the proportion of the sum of squares due to GCA effects (Table 2), the lower the correlations between cross averages and their respective SCA effects (Table 4). The extremes could be observed for fresh root yield and DMC, which had the lowest proportion of the cross sum of squares explained by the GCA effects (41 and 63%, respectively) and the highest correlations between cross means and SCA effects (0.61 for both variables). In contrast, the reaction to pests showed the highest importance of GCA effects (85% of the crosses sum of squares) and the lowest correlation coefficients (0.38) between cross means and SCA effects.

The results from these evaluations are also useful to highlight the potential for this crop as supplier of raw material for agro-industrial processes in addition to the well-recognized role as a food security crop. Dry matter productivity based on F₁ family averages (across the two environments) ranged from 9.9 to 20.9 Mg ha^–1. However, individual clone averages for the same trait ranged from 1.3 to 43.6 Mg ha^–1. These experimental results are supported by commercial productions exceeding 10 Mg ha^–1 of dry matter when appropriate clones and cultural practices are combined.

Phenotypic correlation between fresh root yield and harvest index was relatively low (r = 0.21). Kawano et al. (1998) and Kawano (2003) have demonstrated the importance of harvest index in cassava during the early stages of selection. Because of the low multiplication rate of the planting material of this crop, early stages of selection were based on single plants or single-row plots (typically with six plants). Selection based on fresh root yields (in nonreplicated trials) is drastically affected by the environment, whereas harvest index is not. In this study, however, each genotype was planted in three different replications at two locations. This particular randomization helped to reduce the influence of environment in the average fresh root yield. It was surprising to observe the excellent correlation between harvest index and DMC (r = 0.45), which helped to increase the correlation with dry matter yield (r = 0.31).

	ACKNOWLEDGMENTS

 
This research was conducted with the partial support of the Ministerio de Agricultura y Desarrollo Rural of Colombia.

Received for publication May 21, 2004.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 

• Allem, A.C. 2002. The origins and taxonomy of cassava. p. 1–16. In R.J. Hillocks et al. (ed.) Cassava: Biology, production and utilization. CABI Publ., Wallingford, Oxon, UK.
• Bellotti, A.C., and B. Arias. 2001. Host plant resistance to whiteflies with emphasis on cassava as a case study. Crop Prot. 20:813–823.[CrossRef]
• Bellotti, A.C., L. Smith, and S.T. Lapointe. 1999. Recent advances in cassava pest management. Annu. Rev. Entomol. 44:343–370.[Medline]
• Ceballos, H., C.A. Iglesias, J.C. Pérez, and A.G.O. Dixon. 2004. Cassava breeding: Opportunities and challenges. Plant Mol. Biol. 56(4):503–516.[CrossRef][Web of Science][Medline]
• CIAT. 2003. Annual report from IP3 project: Improved cassava for the developing world. CIAT, Cali, Colombia.
• Cock, J. 1985. Cassava. New potential for a neglected crop. Westview Press, Boulder, CO., USA.
• Easwari Amma, C.S., and M.N. Sheela. 1998. Genetic analysis in a diallel cross of inbred lines of cassava. Madras Agric. J. 85(5,6):264–268.
• Easwari Amma, C.S., M.N. Sheela, and P.K. Thankamma Pillai. 1995. Combining ability analysis in cassava. J. Root Crops 21(2):65–71.
• Food and Agricultural Organization and International Fund for Agricultural Development. 2000. La economía mundial de la yuca. Hechos, tendencias y perspectivas. Fondo Internacional de Desarrollo Agrícola. Organización de las Naciones Unidas para la Agricultura y la Alimentación, Rome.
• Fregene, M., F. Angel, R. Gomez, F. Rodriguez, P. Chavarriaga, W. Roca, J. Tohme, and M. Bonierbale. 1997. A molecular genetic map of cassava. Theor. Appl. Genet. 95:431–441.[CrossRef][Web of Science]
• Jennings, D.L., and C. Iglesias. 2002. Breeding for crop improvement. p. 149–166. In R.J. Hillocks et al. (ed.) Cassava: Biology, production and utilization. CABI Publ., Wallingford, Oxon, UK.
• Kawano, K. 1980. Cassava. p. 225–233. In W.R. Fehr and H.H. Hadley (ed.) Hybridization of crop plants. ASA and CSSA, Madison, WI.
• Kawano, K. 2003. Thirty years of cassava breeding for productivity—Biological and social factors for success. Crop Sci. 43:1325–1335.[Abstract/Free Full Text]
• Kawano, K., W.M. Gonçalvez Fukuda, and U. Cenpukdee. 1987. Genetic and environmental effects on dry matter content of cassava root. Crop Sci. 27:69–74.[Abstract/Free Full Text]
• Kawano, K., K. Narintaraporn, P. Narintaraporn, S. Sarakarn, A. Limsila, J. Limsila, D. Suparhan, V. Sarawat, and W. Watananonta. 1998. Yield improvement in a multistage breeding program for cassava. Crop Sci. 38:325–332.[Abstract/Free Full Text]
• Griffing, B. 1956. Concept of general and specific combining ability in relation to diallel crossing systems. Aust. J. Biol. Sci. 9:463–493.
• Losada Valle, T. 1990. Cruzamentos dialélicos em mandioca (Manihot esculenta Crantz). Ph.D. thesis. Escola Superior de Agricultura Luiz de Queiroz, Univ. de São Paulo. Piracicaba, Estado de São Paulo, Brazil.
• Mba, R.E.C., P. Stephenson, K. Edwards, S. Melzer, J. Mkumbira, U. Gullberg, K. Apel, M. Gale, J. Tohme, and M. Fregene. 2001. Simple sequence repeat (SSR) markers survey of the cassava (Manihot esculenta Crantz) genome: Towards an SSR-based molecular genetic map of cassava. Theor. Appl. Genet. 102:21–31.[CrossRef]
• Olsen, K.M., and B.A. Schaal. 2001. Microsatellite variation in cassava (Manihot esculenta, Euphorbiaceae) and its wild relatives: Further evidence for a southern Amanzonian origin of domestication. Am. J. Bot. 88:131–142.[Abstract/Free Full Text]
• Scott, G.J., M.K. Rosegrant, and C. Ringler. 2000. Global projections for root and tuber crops to the year 2020. Food Policy 25:561–597.[CrossRef]
• Steel, R.G.D., and J.H. Torrie. 1960. Principles and procedures of statistics. McGraw-Hill, New York.

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