AMMI Stability Analysis and Estimation of Genetic Parameters for Growth and Yield Components in Cassava in the Forest and Guinea Savannah Ecologies of Ghana

Adjebeng-Danquah, Joseph; Manu-Aduening, Joseph; Gracen, Vernon Edward; Asante, Isaac Kwadwo; Offei, Samuel Kwame

doi:https://doi.org/10.1155/2017/8075846

International Journal of Agronomy

On this page

Abstract Introduction Materials and Methods Results Discussion Conclusion Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2017 | Article ID 8075846 | https://doi.org/10.1155/2017/8075846

AMMI Stability Analysis and Estimation of Genetic Parameters for Growth and Yield Components in Cassava in the Forest and Guinea Savannah Ecologies of Ghana

Joseph Adjebeng-Danquah,¹Joseph Manu-Aduening,²Vernon Edward Gracen,^3,4Isaac Kwadwo Asante,⁴and Samuel Kwame Offei⁴

Academic Editor: Kent Burkey

Received13 Mar 2017

Accepted12 Jun 2017

Published13 Jul 2017

Abstract

Twenty cassava genotypes were arranged in a randomised complete block design with three replications and assessed for growth and yield stability using the additive main effect and multiplicative interaction (AMMI) analysis. Highly significant () effects of genotype, environment, and genotype environment interaction were observed for all traits studied. The AMMI analysis of variance indicated that genotype accounted for 51% of the total sum of squares for height at first branching followed by environment (33%) and interaction (15%). For fresh root yield, environment effects accounted for 37% of the total sum of squares, whilst genotype and interaction accounted for 32% and 29%, respectively. Genotypic variances for harvest index (HI), plant height, storage root yield, and dry matter content contributed a greater proportion of the phenotypic variance indicating stronger genetic control. This suggests better chance of progress in the genetic improvement of these traits. Genotype MM96/1751 combined high yield with stability according to the yield stability index ranking across environments. On the other hand genotypes UCC 2001/449 and 96/1708 though high yielding were unstable according to AMMI stability value scores. However they can be tested further in more environments to ascertain their specific adaptability for release to farmers for cultivation to boost cassava production and ensure food security.

1. Introduction

Cassava (Manihot esculenta Crantz) is grown in a wide range of environments but severe yield losses occur in traditional cultivars grown by poor farmers [1–3]. Root yield losses in poor drought-prone environments can be as high as 80% when compared with root yield in optimum environments [4]. This is because genotypes exhibit different levels of phenotypic expression under different environmental conditions resulting in crossover performances [5]. Crossover performances by genotypes in different environments result from differential responses under varying environmental conditions [6, 7]. This was defined as () interaction [8]. Studies on cassava’s performance in different environments have indicated significant effect for root yield and yield components [9–12].

interaction also results from differences in the sensitivities of genotypes to the conditions in the target environment [13] leading to inconsistent performances of genotypes across environments. This limits the efficiency of selection of superior genotypes [14]. In interaction, the magnitude of the observed genetic variances changes from one environment to the next and tends to be larger in better environments than poorer environments [15]. Breeding cassava for yield involves different attributes of genotypes that are subject to influences by variation in environmental conditions [16]. The objective of most cassava yield improvement programmes is to identify and select high yielding and stable genotypes across several environments and seasons [11]. The efficiency and success of such selection depend on the consistency of performances of genotypes in varying environments [17]. For this reason, genotypes are tested in diverse environments to assess their adaptability and stability [5]. Genotypes whose effects are not significant are said to be stable [14].

Several methods have been used to assess the effect and stability in crop performances. Eberhart and Russell [18] proposed joint regression analysis to estimate the average performance of a genotype in different environments relative to the mean performance of all genotypes in the same environment. Multiplicative models which include the additive main effect and multiplicative interaction (AMMI) model have also been used [19, 20]. The AMMI model fits the sum of several multiplicative terms rather than only one multiplicative term in assessing the performance of genotypes in different environments [21]. AMMI analysis can be used to determine stability of the genotypes across locations using the PCA (principal component axis) scores and AMMI stability value (ASV) [22]. The ASV is based on the AMMI model’s IPCA1 and IPCA2 (interaction principal components axes 1 and 2, resp.) scores for each genotype [23]. Genotypes having the least ASV are considered as widely adapted genotypes. Similarly, IPCA2 score near zero indicates more stable genotypes whilst large values represent more responsive and less stable genotypes.

Stability per se does not give much information about the level of yield so Farshadfar et al. [24] and Tumuhimbise et al. [25] used yield stability index (YSI) and genotype stability index (GSI) which combined high yield performance with stability. Both the YSI and the GSI are based on the sum of the ranking due to ASV scores and yield or performance ranking. Lower YSI and GSI values indicate genotypes that combine high yield or performance with stability [24, 26, 27]. The study was conducted to assess the performance and stability of cassava genotypes for fresh root yield and other yield components. Specific objectives were to identify high yielding stable cassava genotypes and estimate the variance components as well as broad sense heritability of root yield and other traits.

2. Materials and Methods

The experiments were conducted during the 2012/2013 and 2013/2014 growing seasons. The trials were conducted at the research fields of the CSIR-Savanna Agricultural Research Institute located at Nyankpala (9°25′N, 0°58′W) in the Guinea Savannah Agroecological Zone and the Crops Research Institute located at Fumesua (6°41N 1°28W) in the Forest Agroecological Zone of Ghana. The Guinea Savannah Zone which covers over 40% of the land area of Ghana is characterised by high temperatures and low humidity for most parts of the year [28]. The rainfall pattern is monomodal and erratic with an annual mean of 1100 mm. The rains usually begin in April-May, end in October, and are followed by a long dry season (5–7 months) which normally begins in November and lasts till April. Intermittent dry spells usually occur even during the rainy season. This experimental site represented the low potential environment.

The site at the CSIR-Crops Research Institute (CSIR-CRI) was located at Fumesua within the Forest Agroecology which is characterised by a bimodal rainfall pattern, major and minor rainy seasons. The major rainy season begins in February and ends in August and then is followed by the minor season which begins in September and ends in November. This site represented the high potential environment for assessing the genotypes for high yield potential.

Twenty cassava genotypes were used for the experiment (Table 1). These included six local landraces collected from farmers’ fields, seven drought tolerant genotypes obtained from the International Institute of Tropical Agriculture (IITA), Nigeria, and seven genotypes from the International Center for Tropical Agriculture (CIAT), Columbia. The genotypes were arranged in a randomised complete block design with three replications under each growing condition. A plot consisted of four rows with ten plants in a row giving a population of 40 plants in each plot.

At each site the land was ploughed, harrowed, and laid out in a randomised complete block with three replications. Ridges were manually raised for the trials at CSIR-SARI, Nyankpala, but planting was done on the flat at Fumesua based on the regular practice. The 20 cassava genotypes represent the treatments. Each plot was made up of four rows with 10 plants in a row. Mature cassava cuttings measuring 25–30 cm were planted using a spacing of 1 m × 1 m on the top of ridge or flat as the case may be at an angle of 45° to the ground surface [29]. No fertilizer was applied to the trials but weeds were manually controlled using cutlasses and hoes as and when necessary.

Monthly data were recorded on growth parameters including plant height (cm), stem girth (cm), and plant height at first branching (cm) which were collected at three months after planting and continued till harvest at 12 months. Harvesting was done at 12 months after planting. Eight plants from the two inner rows were sampled for yield determination. Storage root yield was estimated per plot and extrapolated to tons per hectare. For storage root dry matter content determination, roots were first chopped into pieces (about 1 cm thick) and mixed thoroughly. Afterwards three 100 g subsamples were taken and oven-dried at 80°C for 48 hours and weighed. Dry matter content for each sample was obtained by expressing the dry weight as a percentage of the original fresh weight of the sample taken. An average of the three samples was calculated to obtain the dry matter content of the respective genotypes. Cassava mosaic disease (CMD) severity was scored using a scale of 1–5 where 1 denotes no symptom and 5 denotes highly diseased plants [30].

The data were subjected to analysis of variance using GenStat software version 12.1 [31] to determine the significance of the main effects and interactions. Combined analysis of all growth parameters as well as root yield and yield components from the different growing environments was done. Two growing environments (Nyankpala and Fumesua) and two growing seasons (2013 and 2014) were used.

Variance components (genotypic, phenotypic, and environment as well as variances) were also estimated from their respective mean squares obtained from the analysis of variance table according to Ntawuruhunga and Dixon [16]. Broad sense heritability () was estimated according to Padi [32] as follows:where is genotypic variance; is phenotypic variance; is variance; is pooled error; is number of environments; is number of replications.

Genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) were estimated according to Singh and Chaudhary [33] as follows: where is grand mean.

Expected genetic advance, GA, was calculated as where GA is expected genetic advance. is Selection differential (2.06 at 5% selection intensity). is phenotypic standard deviation.

Genetic advance as a percentage of mean (GAM) was also estimated using the following formula:

2.1. Stability Analysis

Additive main effect and multiplicative interaction (AMMI) model in GenStat 12.1 [31] was used to determine the stability of the genotypes across environments. The AMMI model first fits the additive effects for the genotypes and the growing environments (two growing environments and two seasons) and multiplicative term for interactions.

The AMMI model according to Farshadfar et al. [24] is presented aswhere is the yield of the th genotype in the th environment, is the mean of the th genotype minus the grand mean, is the square root of the eigenvalue of the PCA axis , and are the principal component scores for PCA axis of the th genotype and the th environment, respectively, and is the residual. The environment and genotypic PCA scores are expressed as unit vector times the square root of ; i.e., environment PCA ; 0.5; genotype PCA .

AMMI stability value (ASV) was calculated for each genotype according to the relative contributions of the principal component axis scores (IPCA1 and IPCA2) to the interaction sum of squares.

The AMMI stability value (ASV) as described by Purchase et al. [23] was calculated as follows:where is the weight given to the IPCA1-value by dividing the IPCA1 sum of squares (from the AMMI analysis of variance table) by the IPCA2 sum of squares. The larger the IPCA score is, either negative or positive, the more adapted a genotype is to a certain environment. Smaller ASV scores indicate a more stable genotype across environments [24].

Yield stability index was also calculated using the sum of the ranking based on yield and ranking based on the AMMI stability value. where RASV is the rank of the genotypes based on the AMMI stability value; RY is the rank of the genotypes based on yield across environments (RY).

YSI incorporates both mean yield and stability in a single criterion. Low values of both parameters show desirable genotypes with high mean yield and stability [25, 34].

3. Results

The environments differed in total rainfall amounts, mean temperature, and relative humidity (Table 2). The total rainfall recorded in 2014 was higher than that recorded in 2013 for both locations. For Fumesua in 2014, 1620.00 mm was recorded which was higher than in 2013 (1147.08 mm). Higher amount of rainfall was also recorded at Nyankpala in 2014 (1181.04 mm) than Nyankpala in 2013 (991.68 mm). Mean monthly temperature recordings at Nyankpala were higher than at Fumesua in both years. The environments at Fumesua (FUM13; 75.4% and FUM14; 81.3%) also had higher relative humidity than Nyankpala in both years (Nyank2013; 68.68% and Nyank2014; 68.5%).

There were highly significant () effect of genotypes and environment for all the traits studied (Table 3). effect was significant () for storage root dry matter content, very significant () for plant height, and highly significant () for stem diameter, plant height at first branching, storage root yield, harvest index, and cassava mosaic disease score.

The AMMI analysis of variance indicated highly significant () effects of genotype, environment, and interaction for all traits (Table 4). Genotypic factors accounted for larger proportion of the treatment sum of squares for plant height at first branching, dry matter content, harvest index, and cassava mosaic disease score environment and interactions. Genotype effect accounted for 15.72% of the treatment sum of squares (SS) for plant height (cm) whilst environment and interaction accounted for 77.48% and 6.80%, respectively. The first two interaction principal component axes (IPCA1 and IPCA2) accounted for 85.10% of the interaction sum of squares. For stem diameter, 32.10% of the treatment sum of squares was due to genotype effect whilst environment and interaction effect accounted for 30.38% and 37.52%, respectively. The IPCA1 accounted for 74.63% with IPCA2 accounting for 13.95%. A greater proportion of the treatment sum of squares for plant height at first branching was due to genotype effect (51.34%) followed by environment effect (33.38%) and interaction effect (15.27%). The first two interaction principal component axes accounted for a total of 79.42%.

Genotype effects accounted for 53.99% of the treatment sum of squares for cassava mosaic disease severity score whilst environment and interaction effects accounted for 21.20% and 24.81%, respectively. The IPCA1 accounted for 85.80% of the interaction sum of squares with IPCA2 accounting for 10.93%. For storage root yield, environment effect (37.55%) contributed a greater proportion of the treatment sum of squares compared with genotype effect (32.77%) and interaction (29.68%). The first two interaction principal component axes (IPCA1 and IPCA2) accounted for 65.55% and 31.12% of the interaction sum of squares respectively. The influence of genotype (54.10%) on root dry matter content was greater than both interaction (36.10%) and environment effect (9.78%). Genotype effect (46.22%) on harvest index was greater than the effect of interaction (28.38%) and environment (25.39%). The first two principal component axes contributed a total of 91.13% of the interaction sum of squares.

A large proportion of the phenotypic variance () for plant height was accounted for by the genotypic variance () (Table 5). Genotypic variances () for plant height and height at first branching were higher than the variance (). Storage root yield and dry matter content were mostly influenced by their compared with . Error variances () for all traits were higher than their phenotypic variances () except for plant height, height at branching, storage root yield, and harvest index.

The estimates of broad sense heritability varied for all the traits and were especially low for stem diameter (43.08%) and root yield (43.46%) (Table 5). Relatively high broad sense heritability estimates were observed for plant height (82.84%), height at first branching (78.32%), and storage root dry matter content (77.70%). Phenotypic coefficient of variation for all traits was higher than the corresponding genotypic coefficient of variation (GCV). Wide differences between PCV and GCV were observed for height at branching and storage root yield. PCV ranged from 7.06% to 38.89% for stem diameter and height at first branching respectively. GCV varied from 5.14% (stem diameter) to 36.19% (height at first branching). Moderate PCVs (10–20) were observed for plant height and harvest index whereas high PCV (>20) was recorded for height at first branching. Stem diameter and root dry matter content had the lowest PCVs (<10). The analysis of the expected genetic advance as percentage of the mean (GAM) indicated that height at first branching could be improved by 65.98% whilst only 6.49% progress could be made in the improvement of stem diameter. Root yield could also be improved by 27.31% whilst progress of 17.06 and 11.86% could be made in harvest index and dry matter content, respectively.

The mean performance of all genotypes across the six environments showed significant () genetic variability for plant height, height at branching, and cassava mosaic disease severity score (Table 6). CTSIA 65 had the highest plant height (249.67 cm) with genotype 00/0203 having the shortest plants across all environments. The lowest height at branching genotype was I91934 (36.38 cm) whilst CTSIA 65 had the highest (136.65 cm). Average stem diameter ranged from 1.84 cm (CTSIA 72) to 2.61 cm (96/409). Cassava mosaic disease scores varied significantly among the different genotypes (Table 6). MM96/1751 and 00/0203 had the lowest overall mean CMD severity score (1.00) whilst CTSIA 230 had the highest. (3.13). Average root yield for all genotypes across the four environments was 24.79 t/ha (Table 5). UCC2001/449 had the highest overall root yield of 35.97 t/ha with CTSIA 48 having the lowest root yield (11.87 t/ha). Three genotypes (UCC2001/449, MM96/1751, and 96/1708) had significantly higher root yields than the best farmer preferred variety, Pontisange, whilst four genotypes had significantly better root yield than the second best farmer preferred variety, Biabasse. Harvest index also varied from 0.43 (CTSIA 110) to 0.66 (00/0203 and MM96/1751). The average harvest index for all genotypes across environments was 0.56. Storage root dry matter ranged between 28.24% (I91934) and 38.99% (CTSIA 110) with an average of 32.38%.

Additive main effect and multiplicative interaction (AMMI) stability value (ASV) ranked the genotypes based on the least score (Table 7). Low scores represent the most stable genotypes. Based on the ASV, the most stable genotype for root yield was ATR 007 since it had the highest ASV ranking. 96/1708 was ranked the least stable because it had the highest ASV. In terms of root yield UCC2001/449 had the highest rank with CTSIA 48 being ranked the least. The sum of the yield and stability rankings (YSI) ranked MMM96/1751 as the genotype that combined high yield with stability. 96/1708 though high yielding was unstable because of its low rank according to the YSI. Three genotypes MMM96/1751, 00/0203, and UCC2001/449 can be considered as high yielding and stable.

Additive main effect and multiplicative interaction analysis identified four highest yielding genotypes in each of the four environments (Table 8). Four genotypes 96/1708, UCC2001/449, TME 419, and I91934 were selected for the high potential environment, Fumesua 2014. In environment FUM13, MM96/1751, UCC2001/449, 00/0203, and Biabasse were selected as the top genotypes. The top four genotypes in Nyankpala 2013 were 96/1708, 00/0203, MM96/1751, and TME 435. In the least favourable environment (Nyankpala 2014), genotypes I91934, UCC2001/449, 96/1708, and MM96/1751 were selected.

4. Discussion

The performance of cassava is subject to strong influence of genotype, environment, and interaction [10–12, 16]. Significant genotypic variations were observed for growth parameters such as plant height, stem diameter, and height at first branching indicating opportunity for selection. Similar variations in response to cassava to different environments have been reported [9, 12, 35]. Fresh root yield was significantly affected by genotype and the growing environments with much higher root yields being obtained at Fumesua than Nyankpala in both years probably due to the favourable environmental conditions typical of forest agroecologies compared with the guinea savannah ecologies. This indicates that cassava responds favourably to better environments. Similar observations were made by Turyagyenda et al. [4] and Okogbenin and Fregene [9] who observed higher yields under optimum conditions than under limited moisture. Genotype environment interaction was observed over the different environments as indicated by crossover performances for some of the genotypes. This led to variations in the mean ranks of the genotypes in the different environments [8, 36]. This implies different adaptation by the different genotypes suggesting the need to identify and select location specific genotypes for different environments. Alternatively yield stability analysis can be performed to identify genotypes whose performance remains stable over several years and environments [35]. In this study, genotypes performed better in the optimum environments than under stress conditions.

Harvest index was higher in the stress environments than in the humid environments (Fumesua in 2013 and 2014). Okogbenin and Fregene [9] reported higher harvest index under limited moisture conditions than under optimum conditions. This was possibly due to the tendency of cassava to grow more vigorously and produce more above ground biomass relative to their root growth in such environments [3, 37]. This could be attributed to differential partitioning of dry matter into the above ground parts and storage roots [1, 37]. Genotype CTSIA 65 had the tallest plants but had the lowest root yield suggesting that dry matter partitioning was probably directed to top growth instead of storage root bulking. Okogbenin and Fregene [9] stated that certain cassava genotypes allocate initial dry matter production for shoot growth before storage root development commences. Genotypes that are able to balance initial dry matter accumulation between shoot and root would be ideal for moisture limited environments.

Genotypic variance accounted for a large proportion of the observed phenotypic variance for HI and plant height indicating inherent genetic variation for these traits [38]. On the contrary, Tewodros et al. [39] found lower genotypic variances for these traits indicating strong environmental influences. Though estimates of genetic components indicated large genotypic variance () for a number of traits, their error variances were higher than the phenotypic variances in most cases implying strong environmental influence. Progress in their improvement requires evaluation in several environments to minimise the effect of interactions that may influence progress through selection [17]. Root yield and dry matter content were influenced more by their genotypic variances than the interaction variances (). This indicates the presence of a significant genetic potential and therefore provides opportunity remarkable improvement with selection. Similar observations were made by Ntawuruhunga and Dixon [16] who observed large environmental variance for fresh root yield in cassava.

Broad sense heritability estimates for dry matter content and plant height were similar to the findings of Ntawuruhunga and Dixon [16]. Heritability estimates influence the amount of progress that can be made in selection for a trait of interest [40]. However, broad sense heritability alone does not always give a full indication of genetic gain that can be made through selection since it includes both additive and nonadditive components of the variation [41, 42]. It is necessary to estimate the genetic advance as a percentage of the mean to determine the actual progress. When the genetic advance as a percentage of the mean was estimated, it was found that though storage root yield had a relatively low heritability (43%) compared with plant height (82%), they had the same almost the same GAM. This suggests that plant height had greater nonadditive variance and environmental influence compared with storage root yield [41].

Stability analysis methods are often used by breeders to identify genotypes that have stable performance and respond positively to improvements in environmental conditions [24]. AMMI stability value (ASV) indicates the stability of genotypes. Genotypes having low ASV are considered more stable whilst those with high values are less stable genotypes [22]. CTSIA 110, MM96/1751, and TME 435 were the most stable for root yield. Stability alone for yield performance does not warrant selection since a consistently low yielding genotype can still be stable [43]. In some cases the most stable genotypes do not always have the best yield performance [44]. Therefore, high root yield is considered with stability in the estimation of yield stability index (YSI). The YSI which is similar to genotype stability index (GSI) proposed by Fardshadfar [26] integrates both yield and stability across environments into a single index, to select varieties. The YSI sums the rank of mean yield across environments with the rank of the ASV of genotypes [25, 27]. Genotypes with lower YSI are desirable since they combine high mean yield performance with stability [24, 25, 27, 34]. Based on the YSI, genotypes MM96/1751, Pontisange, 00/0203, ATR 007, and UCC2001/449 were selected as combining high yield performance with stability. Genotypes such as 96/409, TME 435, and I91934 had intermediate yields with moderate stability. However genotypes 96/1708 and TME 419, although high yielding, had high ASV scores resulting in high YSI scores. However they can be recommended for specific environments where they performed well. The range of variation in the favourable environments (Fumesua 2013 and 2014) was larger than in the poor environments indicating that genotypes were better able to exploit their full potential yield in the good environments [15].

5. Conclusion

interaction was significant for most of the traits indicating the need to test the genotypes in multiple environments before effective selection can be made. The high broad sense heritability estimates obtained for plant height and height at first branching suggest that significant progress can be made through selection for improvement of genotypes for these traits. Six genotypes (UCC2001/449, 96/1708, MM96/1751, 00/0203, 96/409, and I91934) had significantly higher root yield than the best local farmer preferred genotype, Pontisange. These genotypes can be evaluated in more environments to assess their adaptability and possible recommendation for release to farmers for cultivation.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

Acknowledgments

The authors are grateful to the Generation Challenge Programme and the Alliance for Green Revolution in Africa for sponsoring this study. The staff of the Root and Tuber Crops Improvement Sections of the CSIR-Savanna Agricultural Research Institute, Nyankpala, and the CSIR-Crops Research Institute are also deeply acknowledged for their efforts and assistance during the study period.

References

M. A. El-Sharkawy, “Cassava biology and physiology,” Plant molecular biology, vol. 56, no. 4, pp. 481–501, 2004.
View at: Publisher Site | Google Scholar
O. O. Aina, A. G. O. Dixon, and E. A. Akinrinde, “Effect of soil moisture stress on growth and yield of cassava in Nigeria,” Pakistan Journal of Biological Sciences, vol. 10, no. 18, pp. 3085–3090, 2007.
View at: Publisher Site | Google Scholar
M. A. El-Sharkawy, “Physiological characteristics of cassava tolerance to prolonged drought in the tropics: Implications for breeding cultivars adapted to seasonally dry and semiarid environments,” Brazilian Journal of Plant Physiology, vol. 19, no. 4, pp. 257–286, 2007.
View at: Publisher Site | Google Scholar
L. F. Turyagyenda, E. B. Kizito, M. Ferguson et al., “Evaluation of Ugandan cassava germplasm for drought tolerance,” International Journal of Agriculture and Crop Science, vol. 5, no. 3, pp. 212–226, 2013.
View at: Publisher Site | Google Scholar
P. C. Haldavankar, G. D. Joshi, S. G. Bhave, R. G. Klandekar, and S. S. Sawant, “Stability of yield and yield attributing phenotypic characters in sweet potato,” Journal of Root Crops, vol. 35, no. 1, pp. 28–35, 2009.
View at: Google Scholar
J. Crossa and P. L. Cornelius, “Sites regression and shifted multiplicative model clustering of cultivar trial sites under heterogeneity of error variances,” Crop Science, vol. 37, no. 2, pp. 406–415, 1997.
View at: Publisher Site | Google Scholar
J. Mkumbira, N. M. Mahungu, and U. Gullberg, “Grouping locations for efficient cassava evaluation in Malawi,” Experimental Agriculture, vol. 39, no. 2, pp. 167–179, 2003.
View at: Publisher Site | Google Scholar
A. G. Dixon and E. N. Nukenine, “Genotype $*$ environment interaction and optimum resource allocation for yield and yield components of cassava,” African Crop Science Journal, vol. 8, no. 1, pp. 1–10, 2000.
View at: Publisher Site | Google Scholar
E. Okogbenin and M. Fregene, “Genetic analysis and QTL mapping of early root bulking in an F1 population of non-inbred parents in cassava (Manihot esculenta Crantz),” Theoretical and Applied Genetics, vol. 106, no. 1, pp. 58–66, 2002.
View at: Publisher Site | Google Scholar
O. O. Aina, A. G. O. Dixon, and E. A. Akinrinde, “Trait association and path analysis for cassava genotypes in four agroecological zones of Nigeria,” Journal of Biological Sciences, vol. 7, no. 5, pp. 759–764, 2007.
View at: Publisher Site | Google Scholar
C. N. Egesi, P. Ilona, F. O. Ogbe, M. Akoroda, and A. Dixon, “Genetic variation and genotype $*$ environment interaction for yield and other agronomic traits in cassava in Nigeria,” Agronomy Journal, vol. 99, no. 4, pp. 1137–1142, 2007.
View at: Publisher Site | Google Scholar
O. O. Aina, A. G. O. Dixon, P. Illona, and E. A. Akinrinde, “G $*$ E interaction effects on yield and yield components of cassava (landraces and improved) genotypes in the savannah regions of Nigeria,” African Journal of Biotechnology, vol. 8, no. 19, pp. 4933–4945, 2009.
View at: Google Scholar
D. S. Falconer and T. F. C. Mackay, Introduction to Quantitative Genetics, Longman, New York, NY, USA, 1996.
G. Ssemakula and A. G. O. Dixon, “Genotype $*$ environment interaction, stability and agronomic performance of carotenoid-rich cassava clones,” Scientific Research and Essays, vol. 2, no. 9, pp. 390–399, 2007.
View at: Google Scholar
M. Przystalski, A. Osman, E. M. Thiemt et al., “Comparing the performance of cereal varieties in organic and non-organic cropping systems in different European countries,” Euphytica, vol. 163, no. 3, pp. 417–433, 2008.
View at: Publisher Site | Google Scholar
P. Ntawuruhunga and A. G. O. Dixon, “Quantitative variation and interrelationship between factors influencing cassava yield,” Journal of Applied Biosciences, vol. 26, pp. 1594–1602, 2010.
View at: Google Scholar
E. Otoo, C. R. Okonkwo, and R. Asiedu, “Stability studies of hybrid yam genotypes in Ghana,” Journal of Food, Agriculture and Environment, vol. 4, no. 1, pp. 234–238, 2006.
View at: Google Scholar
S. A. Eberhart and W. A. Russell, “Stability parameters for comparing varieties,” Crop Science, vol. 6, pp. 36–40, 1966.
View at: Google Scholar
H. G. Gauch, “Model selection and validation for yield trials with interaction,” Biometrics, vol. 44, no. 3, pp. 705–715, 1988.
View at: Publisher Site | Google Scholar
F. A. van Eeuwijk, “Linear and bilinear models for the analysis of multi-environment trials: I. An inventory of models,” Euphytica, vol. 84, no. 1, pp. 1–7, 1995.
View at: Publisher Site | Google Scholar
R. Bernardo, Breeding for Quantitative Traits in Plants, Stemma Press, Woodbury, Minn, USA, 2010.
View at: Publisher Site
H. G. Hagos and F. Abay, “AMMI and GGE biplot analysis of bread wheat genotypes in the northern part of Ethiopia,” Journal of Plant Breeding and Genetics, vol. 1, pp. 12–18, 2013.
View at: Google Scholar
J. L. Purchase, H. Hatting, and C. S. van Deventer, “Genotype $*$ environment interaction of wheat in South Africa: stability analysis of yield performance,” South African Journal of Plant and Soil, vol. 17, no. 3, pp. 101–107, 2000.
View at: Publisher Site | Google Scholar
E. Farshadfar, N. Mahmodi, and A. Yaghotipoor, “AMMI stability value and simultaneous estimation of yield and yield stability in bread wheat (Triticum aestivum L.),” Australian Journal of Crop Science, vol. 5, no. 13, pp. 1837–1844, 2011.
View at: Google Scholar
R. Tumuhimbise, R. Melis, P. Shanahan, and R. Kawuki, “Genotype $*$ environment interaction effects on early fresh storage root yield and related traits in cassava,” The Crop Journal, vol. 2, no. 5, pp. 329–337, 2014.
View at: Publisher Site | Google Scholar
E. Farshadfar, “Incorporation of AMMI stability value and grain yield in a single non parametric index (GSI) in bread wheat,” Pakistan Journal of Biological Sciences, vol. 11, no. 14, pp. 1791–1796, 2008.
View at: Publisher Site | Google Scholar
F. Baraki, Y. Tsehaye, and F. Abay, “AMMI analysis of genotype $*$ environment interaction and stability of sesame genotypes in Northern Ethiopia,” Asian Journal of Plant Sciences, vol. 13, no. 4, pp. 178–183, 2014.
View at: Publisher Site | Google Scholar
EPA, “National action programme to combat drought and desertification,” Environmental Protection Agency Accra-Ghana, 2003.
View at: Google Scholar
I. J. Ekanayake, “Cassava procedures for growth analysis,” IITA Procedures Manual, 24 pages, 1996.
View at: Google Scholar
W. Msikita, B. James, E. Nnodu, J. Legg, K. Wydra, and F. Ogbe, Disease Control in Cassava Farms: IPM Field Guide for Extension Agents, IITA publication, 2000.
R. W. Payne, D. A. Murray, S. A. Harding, D. B. Baird, and D. M. Soutar, Genstat for Windows: Introduction, VSN International, Hemel Hempstead, UK, 12th edition, 2009.
View at: Publisher Site
F. K. Padi, “Genotype $*$ environment interaction and yield stability in a cowpea-based cropping system,” Euphytica, vol. 158, no. 1-2, pp. 11–25, 2007.
View at: Publisher Site | Google Scholar
K. Singh and B. Chaudhary, Biometrical Methods in Quantitative Genetic Analysis, Kalyani publishers/Lyall Bk Depot, 1979.
View at: Publisher Site
L. K. Bose, N. N. Jambhulkar, K. Pande, and O. N. Singh, “Use of AMMI and other stability statistics in the simultaneous selection of rice genotypes for yield and stability under direct-seeded conditions,” Chilean Journal of Agricultural Research, vol. 74, no. 1, pp. 1–9, 2014.
View at: Publisher Site | Google Scholar
R. W. Mutegi, Towards identifying the physiological and molecular basis of drought tolerance in cassava (Manihote sculenta Crantz) [Ph.D. thesis], Georg-August University Gottingen, 2009.
M. Malosetti, J.-M. Ribaut, and F. A. van Eeuwijk, “The statistical analysis of multi-environment data: Modeling genotype-by-environment interaction and its genetic basis,” Frontiers in Physiology, vol. 4, article 44, 2013.
View at: Publisher Site | Google Scholar
A. A. C. Alves, “Cassava Botany and Physiology,” in Cassava: Biology, Production and Utilization, R. J. Hillocks, J. M. Thresh, and A. Bellotti, Eds., pp. 67–90, CABI, Wallingford, UK, 2002.
View at: Publisher Site | Google Scholar
E. Tsegaye, N. Dechassa, and E. V. D. Sastry, “Genetic variability for yield and other agronomic traits in sweet potato,” Journal of Agronomy, vol. 6, no. 1, pp. 94–99, 2007.
View at: Publisher Site | Google Scholar
M. Tewodros, W. Getachew, B. Tadesse, and W. Tesfaye, “Genetic diversity of cassava (Manihot esculenta Crantz) genotypes in Ethiopia,” Greener Journal of Agricultural Sciences, vol. 3, no. 9, pp. 636–642, 2013.
View at: Publisher Site | Google Scholar
T. Sabesan, R. Suresh, and K. Saravanan, “Genetic variability and correlation for yield and grain quality characters of rice grown in coastal saline low land of Tamil Nadu,” Electronic Journal of Plant Breeding, vol. 1, pp. 56–59, 2009.
View at: Google Scholar
T. Pradeepkumar, D. Bastian, M. Joy, N. V. Radhakrishnan, and K. C. Aipe, “Genetic variation in tomato for yield and resistance to bacterial wilt,” Journal of Tropical Agriculture, vol. 39, pp. 157-158, 2001.
View at: Google Scholar
A. K. Singh, P. Sharma, and P. K. Singh, “Studies on genetic characteristic of upland rice (Oryza sativa L.),” International Journal of Agriculture, Environment and Biotechnology, vol. 6, no. 4, pp. 515–520, 2013.
View at: Publisher Site | Google Scholar
W. Yan and N. A. Tinker, “Biplot analysis of multi-environment trial data: Principles and applications,” Canadian Journal of Plant Science, vol. 86, no. 3, pp. 623–645, 2006.
View at: Publisher Site | Google Scholar
E. Oliveira and I. Godoy, “Pod yield stability analysis of runner peanut lines using AMMI,” Cropp Breeding and Applied Biotechnology, vol. 6, no. 4, pp. 310–317, 2006.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2017 Joseph Adjebeng-Danquah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

6927

Downloads

3098

Citations