AMMI and GGE biplot analysis of potato (Solanum tuberosum L.) genotypes for dry season performance of marketable tuber yield

doi:10.21203/rs.3.rs-2459606/v1

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AMMI and GGE biplot analysis of potato (Solanum tuberosum L.) genotypes for dry season performance of marketable tuber yield

https://doi.org/10.21203/rs.3.rs-2459606/v1

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Background

Potato is the 4^th most important crop in the World and among the key food security crop in Ethiopia. But its productivity in Ethiopia is much lower than the crop potential due to genetic and environmental factors. Hence, multi-environment evaluation of potato genotypes is important to determine the magnitude of genotype, environment, and their interaction effect on marketable yield and to identify which genotypes would be appropriate in which production areas. Taking this objective into consideration, 13 potato genotypes were evaluated for two dry cropping seasons (2018 and 2019) in three locations in northwestern Ethiopia.

Results

The analysis of variance detected differences (P < 0.01) among genotypes (33.8%), environments (40.2%), and genotype-by-environment interaction (24.3%) for marketable tuber yield. Belete, Gera, and Guassa genotypes were the top 3 genotypes on the ideal testing environment (Dt2) axis. The mean and stability graph of the GGE biplots revealed Belete and Guassa as the most stable and high-yielding genotypes followed by Gera and Jalenie. Furthermore, based on ideal genotype ranking, Genotype Belete was in the first concentric circle and ranked first via yield stability index (YSI) with the highest marketable tuber yield performance (33.14 t/ha).

Conclusions

The findings indicated that genotype Belete appears to be an ideal genotype for the environment. Thus, using Belete genotype increases the marketable yield by 165% as compared to the local genotype. Therefore, promoting this genotype helps boost potato production and productivity in the study environment as well as similar agroecological areas.

Horticulture

AMMI

GGE biplot

Marketable tuber yield

Potato

Stability

Potato (Solanum tuberosum L.) has a shorter growing period which could fit it into a land and other environmental resources for intensive cropping [1]. Growers can use it for rain-fed and dry-season production systems and have more than one crop on the same land per year [2].

Potato is used as a food and cash crop for small-scale farmers. About 70% of agricultural land in Ethiopia is at an altitude of 1800–2500 m.a.s.l with an annual rainfall of more than 600 mm and is suitable for potato production [1, 3]. North and Central Gondar zones are among the major potato production zones in the northwestern part of Ethiopia [1, 4].

Currently, the demand for potato tuber continues to increase in conjunction with the need for expanding diet diversity, requests for prepared food items, and a need for inexpensive foods [5; 6]. Recently, due to increased urbanization, the consumption of chips, crisps, and roasted potato are common.

Lack of well-adapted varieties is among the challenges for farmers [1, 4]. The growth and production of potato is determined by the genotype (G) and depends on the effects of the environment (E) and their interaction. The performance of released potato varieties vary across ecologies due to soil type, moisture and temperature, fertility, and the onset, intensity and duration of rain, and whether growers have access to irrigation [1]. Investigation and integration of genotype and genotype by environment interaction in selecting superior genotypes in crop performance trials have been long-standing interests of breeders [7, 8].

The additive and multiplicative interaction (AMMI) and genotype plus genotype-by-environment interaction biplot (GGE-biplot) models have been recommended to determine genotype-by-environment interaction effects [9, 10, 11, 12, 13, 14]. The AMMI model is a hybrid analysis that incorporates both the additive and multiplicative components. In AMMI the main effect (additive) is separated from interaction by analysis of variance (ANOVA) and the multiplicative component is further decomposed by the interaction principal component [15]. The GGE-biplot analysis utilizes both genotype and genotype-by-environment interaction effects and graphically displays GEI for visual evaluation of relationships among genotypes, environments and the interaction of the two [14]. The present study was undertaken to determine the extent of genotype by environment interaction for marketable tuber yield and to identify adaptive and high yielding potato variety among potato genotypes.

The experiment was conducted during the dry seasons of the years 2018–2019 in Dabat and Debark districts from North Gondar administration Zone and Wogera District from Central Gondar administration Zone (Table 1).

Table 1

Geographic location, temperature and rainfall conditions in the 3 research locations
Location	year	Environmental Code	Altitude (m.a.s.l)	Global position		District T°c	District rainfall mm/year	Source
				Latitude	Longitude
Dabark	2018	Dk1	2791	13.1242177	37.8537516	18–29	400–1200	(33)
	2019	Dk2
Dabat	2018	Dt1	2648	12.9134220	37.7363922	18–35	800–1400	(34)
	2019	Dt2
Wegera	2018	W1	2728	12.7439518	37.5572364	14–33	1000–1200	(35)
	2019	W2

Experimental material and design

13 potato varieties (Table 2) were studied in experiments arranged as a randomized complete block design (RCBD) with three replications. Medium-sized potato tubers (35–45 mm in diameter) were planted on a 3m X 3m plot size with a spacing of 75 cm between rows and 30 cm between plants within a raw. The blocks were separated1.5 meters width whereas the space between each plot within a block was 1 meter. The soil was tilled 3 times and DAP and Urea Fertilizers were applied at a rate of 195 and 165 kg/ha, respectively [16]. There was 4 rows/plot for each treatment. Data were collected from the middle two rows while the remaining 2 outermost rows and terminal plants were considered as guards. A healthy tuber with > 30 mm diameter was graded as a marketable tuber (MTY) [17, 18]. Irrigation water was applied through furrow system at an interval of 8–10 days according to local traditions. Other cultural practices like earthing up and weeding were carried out 3 times each during the growing period.

Table 2

List of genotypes
Genotype	Accession Code	Code in current study	Release		Recommended altitude (m.a.s.l)
Genotype	Accession Code	Code in current study	Year	Center	Recommended altitude (m.a.s.l)
Hundie	CIP-90147.8	Hu	2006	SARC	2400–3350
Bubu	CIP-384321.3	Bu	2010	Haremaya University	1700–2000
Guasa	CIP-384321.9	Gu	2002	ADARC	2000–2800
Zengena	CIP-380479.6	Ze	2001	AWARC	2000–2800
Dagim	CIP-396004.337	Da	2013	ADARC	2000–2800
Belete	CIP-393371.58	Be	2009	HARC	1600–2800
Motie	KP-90147-41	Mo	2011	SARC	2400–3350
Jalenie	CIP-37792-5	Ja	2002	HARC	1600–2800
Ararsa	CIP-90138.12	Ar	2006	SARC	2400–3350
Gera	KP-90134.2	Ge	2003	ShARC	2200–3200
Gudenie	CIP-386423.13	Gd	2006	HARC	1600–2800
Local Cultivar		Lo		Local Caltivar	1600–3100
Gorebela	CIP-382173.12	Go	2002	ShARC/Sheno	1700–2400
Source: [16, 36]
AWARC - Awassa Agricultural Research Center, HARC - Holetta Agricultural Research Center
HU - Haramaya University, SARC - Sinnana Agricultural Research Center, ShARC - Sheno Agricultural Research Center, ADARC - Adet Agricultural Research Center

Statistical analysis

The collected data on marketable tuber yield for the 13 genotypes of potato were checked for the homogeneity of variances using Bartlett’s test before analyses. The Combinations of locations [3] and years [2] were considered as Environment (i.e., six) [19, 20, 21].

The presence of significant genotype main effects (G), environment main effects (E) and the multiplicative genotype by environment interaction effect as well as further partitioning of the interaction effect to principal components were performed using the AMMI model [17] via the generalized linear model of SAS statistical software (version 9.4). Likewise. R statistical software (version 4.1.2) was used to construct the GGE biplots. GGE Biplot graphically presents the multi-environment data in two dimensions through principal components PC1 and PC2, which are unitless measures and are depicted on the x- and y-axis of a biplot, respectively. The percentage of total variation explained by PC1 and PC2 is presented on the biplot, which indicates its validity of approximation of G and GEI components for the trait investigated [22]. In the present study, the Biplot used to generate graphs of the Mean yield and stability performance of genotypes, ‘which-wins-where’ pattern, ranking of genotypes, Discriminativeness vs Representativeness’ pattern, Ranking of Environment, Ranking of genotypes relative to the ideal environment, ranking of test environment relative to high yielding genotype.

The AMMI analyses of Variance

A combined analysis of variance for marketable tuber yield of the 13 tested potato genotypes across 6 environments is presented in Table 3. The AMMI analysis of variance [additive main effects] showed that genotype, environment, and their GEI significantly affected marketable tuber yield. From the total variance, the environment (E)main effect alone accounted for 40.2%, whereas genotype (G) and GEI effects accounted 33.8% and 24.3%, respectively. The result indicated that the environmental main effect was the predominant source of variation, followed by the Genotype and GEI effect. A previous report on potato also indicated that the environmental effect accounted for the largest part of the total variation [17, 18]. The amount of variance contributed by genotype by environment interaction indicates that there was a noticeable G × E interaction effect in these potato genotypes’ multi-environment data, leading to substantial differences in genotypic responses across the test environments.

Table 3

AMMI analysis of variance of marketable tuber yield of 13 potato genotypes
Source	Df	Sum of squares (SS)	Mean square (MS)	% SS explained	Proportion %
ENV	5	3930.7	785.6^**	40.2
REP(ENV)	12	173.2	14.49^ns	1.7
GEN	12	3304.1	275.36^**	33.8
GEN*ENV	60	2373.5	39.56^**	24.3
PC1	16	1180.9	73.81^**		49.8
PC2	14	700.3	50.02^**		29.5
PC3	12	297.1	24.76		12.5
PC4	10	137.8	13.78		5.8
PC5	8	57.3	7.17		2.4
Residuals	144	1861.9	12.93
Total	293	14017.8	47.84
R² = 84%	CV = 13.95		G. Mean = 25.75
ENV = Environment, REP = Replication, GEN = Genotype, PC = principal component

AMMI multiplicative component further partitioned the GEI into five interaction principal component axes (IPCAs). The first two principal components cumulatively explained a larger portion of the variation and contribute more to the accurate prediction of the genotype by environment interaction than the remaining PCAs which does not help in the accurate prediction rather they may contribute to noise [23]. Furthermore, the first two PCA axes showed a significant contribution to the GEI in the AMMI model. Therefore, the dataset obtained from the interaction of 13 potato genotypes tested at 6 environments was best predicted by the first two PCAs.

GGE Biplot analysis

Ranking of Genotypes Based on Yield and Stability Performance

The yield adaptability and stability of the potato genotypes were visually assessed using the GGE biplot analysis. The biplot created from the two PCAs, PCA1 plotted on the x-axis and PCA2 on the y-axis, provides a graphical representation to summarize information on the main effects and interactions effect of both genotypes and environments simultaneously. The best genotype can be defined as the one with the highest yield and stable across environments. A stability study helps to identify the ability of genotypes to avoid critical fluctuation in yield over a range of environmental settings. The stability of the genotype is explained by the arrowed line which passes through the biplot origin and is perpendicular to the average Environmental coordinate (AEC) which is explained by the average scores of the two principal components of all environments [24]. This single arrow line is used to indicate the mean yield performance of the genotypes. The arrow sign on the average Environmental coordinate abscissa line directed the ranking of genotypes in ascending order with greater mean performance for a trait [25]. Genotypes located near the origin were the more stable and not responsive to environments that would rank the same in all environments [26].

Genotypes or Environments placed on the right side of the midpoint of the perpendicular line have higher yields than those placed on the left side of the perpendicular line which is a grand mean value, reflecting better adaptation to that environment. The current result [Figure 1] showed that Genotype Belete produced a higher marketable tuber yield followed by Gera, Guassa, and Jalenie. Genotypes Ararsa, Local, Bubu, Dagim, Zengena, Motie and Hundie had lower mean marketable yield than the grand mean.

The most stable genotypes across environments were Belete, Dagim, Local, Guassa and Motie since they have the shortest distance from the average environment abscissa [22]. Genotypes Jalenie, Gudenie, Gera, Ararsa and Bubu were relatively stable genotypes. Genotype Gorebella, Hundie and Zengena had the longest distance from the average environment abscissa indicating their large contribution to the genotype-by-environment interaction and they were unstable across environments.

Simultaneous consideration of both the mean yield and stability performance is crucial to select the best variety for production. Hence, based on the biplot result, Belete and Guassa were the most stable and high-yielder genotypes while Gorebella was the least stable genotype with intermediate mean yield performance. Gera, Jalenie and Gudenie were the among moderately stable and good yielder genotypes whereas Ararsa and Local exhibited low tuber yield but relatively stable genotypes.

The polygon view of GGE biplot “Which -won- Where”,

The polygon view of the GGE biplot is the best way for the identification of winning genotypes by visualizing the patterns of genotypes and environments interaction and helps to show the mega environments present in the target environment [24; 27]. In the GGE biplot, a polygon was formed by linking the vertex genotypes with straight lines and the remaining genotypes were confined within the polygon and had shorter vectors, suggesting that relatively they are less responsive to interaction with the environment. The equality lines, which originate from the center of the biplot and are perpendicular to the sides of the polygon, divide the graph into different sectors.

According to Yan and Kang [24], the vertex genotypes having the longest distance from the center are the best or poorest in some or all environments and are more responsive to environmental change, and are considered as especially adapted genotypes. Hence, the current result (Fig. 2) showed, genotypes fell into five sections and Belete, Gorebella, Local, Hundie and Ararsa were vertex genotypes. These genotypes were superior in the environment lying within their respective sector or the most inferior genotypes in some of the environments because they were farthest from the origin of the biplot [27]. However, all test environments were grouped into one mega-environment with the winner genotype Belete and a mega-environment is defined as a group of locations that consistently share the best set of genotypes [13]. Such type of result was reported previously on the yield of potato genotypes [28].

Ranking the Genotype

AMMI stability value (ASV) and GGE biplot were used to determine the ideal Genotype. An ideal genotype should have both high mean tuber yield performance and high stability across environments [22, 24]. The genotype with lower AMMI stability value (ASV) and Yield stability index (YSI) values is considered more stable. However, the most stable genotypes would not necessarily give the best yield performance [19], hence there is a need to consider both mean yield and stability value/index.

In the GGE biplot, an ideal genotype is always placed at the center of the concentric circles and relatively nearer the AEA arrowhead pointed to the positive direction. It has the greatest vector length, the highest mean performance, and the highest stability [29]. The arrow sign on the AEA line directed the ranking of genotypes in increasing order with a greater value of traits evaluated. The ideal genotype can be used as a benchmark and the genotypes located near the ideal genotype are highly desirable compared to the other genotypes. Thus, the genotypes were ranked as follows: Be > Gu > Ge > Ja > Go > Gd > Hu > Da > Za > Mo > Bu > Lo > Ar.

On the other hand, the YSI and ASV (Table 4) showed that the first-ranked genotype was Belete based on YSI followed by Guassa, Gera and Jalenie. Based on the ASV Guassa was the first-ranked genotype followed by Motie, Local, Jalenie Bubu and Belete. The mean marketable tuber yield value of genotypes indicate that the Local genotype (20.07 t.ha^− 1) was the lowest yielder while Belete (33.13t.ha^− 1, which is 165% of the local genotype) was the maximum yielder followed by Guassa (30.07 t.ha^− 1) and Gera (30.01 t.ha^− 1).

Table 4

AMMI stability value, Yield stability index and Mean marketable tuber yield (MTY) of 13 potato genotypes
Genotype	YSI	rYSI	ASV	rASV	MTY
Belete	7	1	2.02	6	33.13a
Guassa	3	2	0.48	1	30.07b
Gera	12	3	2.54	9	30.01b
Jalenie	8	4	1.85	4	27.97bc
Gorebella	18	5	3.86	13	27cd
Gudenie	16	6	2.89	10	26.77cde
Hundie	18	7	2.89	11	25.13def
Dagim	20	8	3.02	12	24.56efg
Zengena	17	9	2.54	8	23.87fg
Motie	12	10	0.85	2	23.41fg
BUBU	16	11	1.98	5	22.6g
Ararsa	19	12	2.21	7	20.12h
Local	16	13	0.89	3	20.07h
LSD value					2.3679
YSI = Yield stability index, ASV = AMMI stability value, rASV = Rank of AMMI stability value, rYSI = Rank of yield stability index, means = average genotype by environment

Therefore, genotype Belete can be regarded as an ideal genotype for the tested environments based on the present GGE Biplot, mean performance, YSI and ASV. The genotypes Guassa and Gera could be noted as the leading desirable genotype followed by Jalene since they are located near to the ideal genotype Belete. The lowest yielding varieties (Local and Ararsa) were not desirable because they are located far from the ideal variety. Previous research has reported that tuber yield was used to rank potato genotypes using AMMI stability value/index and GGE Biplot [17, 18, 28].

Discriminativeness vs. representativeness pattern of GGE biplot.

The determination of a best suited test environment is crucial. Some environments may not provide unique information, as they are always similar to some other environment(s) in separating and ranking the genotypes. PC1 and PC2 were used to obtain the ideal test environments. The ideal location should both be able to differentiate the genotypes and represent the target location [22]. Discriminativeness is the capacity of the environment to differentiate the genotypes while representativeness is the ability of an environment to represent the conditions of all other evaluated environments [29, 30].

The environment having a smaller angle with the AEA abscissa is more representative of other test environments than those that have larger angles. Higher vector length, which is proportional to the standard deviation within the respective environment, indicates a greater discriminating ability of the environment. A short vector means the environment is not well represented by PC1 and PC2. Thus, environments with representativeness and discriminating ability are good for selecting generally adapted genotypes; while the environment with discriminating ability, but not representativeness, is good for selecting specifically adapted genotypes [22, 31].

In the current result (Figure. 4) the ranking order of 6 testing environments in terms of being the most representative environment (based on the angle between the environment vector and AEC) was Dt2 (rank of 1) followed by W1(rank 2), Dk2 (rank 3), W2 (rank 4), Dk1 (rank 5) and Dt1 (rank 6). On the other hand, the ranking order of environments in terms of their ability to discriminate genotypes (based on the length of their vectors) was Dk1 (rank of 1) followed by Dt2 (rank 2), Dk2 (rank 3), Dt1 (rank 4), W1 (rank 5) and W2 (rank 6).

These 6 environments may be classified into one of three groups. The short vector [W2], category-1 environment, provided little or no information on genotypes. Dt2, W1 and Dk2 were designated as the category-2, environments with long vectors and small angles with the AEA abscissa, indicating that these environments were ideal for promising genotype selection because of their notable representativeness and discriminating power. Yan and Rajcan [13] noted that a long environmental vector with a short angle is a model environment, which is effective and productive to consider assessing the test environment. If budgetary constraints allow only a few test environments, group 2 test environments can be the first choice [22]. The 3rd environmental category includes environment Dt1 and Dk1 which had long vectors and large angles with the AEA abscissa; they cannot be used in selecting superior genotypes, but are useful for detecting and culling unstable genotypes [22].

Ranking of Environment

The purpose of the test-environment evaluation is to identify test environments that effectively identify superior genotypes based on the distances on the GGE biplot. The ideal environment is located in the first concentric circle in the environment-focused biplot [31]. Figure 5 showed that Dt2 was positioned in the first concentric circle and could be regarded as the most suitable to select widely adapted genotypes. Oppositely, the environment Dt1 and Dk1 were noted as the poorest environment to select genotypes across the environment. The above result clarified that the performance of genotypes varied depending on the testing environment. A similar observation was made by other authors on different crops [17, 22, 28, 32].

Ranking of genotypes relative to the ideal environment

Figure 6 illustrates a graphic comparison of the relative performance of all varieties relative to the Dt2. The Dt2 axis, which is the line that passes through point Dt2 and the origin of the biplot, is called the axis of this environment [31] and along it is the ranking of genotypes. The genotypes were ranked based on their projections onto the Dt2-axis, with rank increasing in the direction toward the positive end [14]. Accordingly, Gorebella, Gudenie, Jalenie, Guassa, Gera, and Belete had higher marketable yield performance than the average yield in increasing order while the remaining 7 genotypes had lower than the average yield. Hundie genotype had a yield near the average yield while Belete and Ararsa were the highest and the lowest yielding genotypes, respectively.

Ranking environments relative to the highest yielding genotype

A line passes through the biplot origin and the genotype is drawn to determine the rank of testing environments relative to the highest yielding genotype. This line is called the axis for this specific genotype, and along it is the ranking of the environments. The environment’s rank was based on its projection onto the genotype axis and the rank increased toward the direction of the genotype’s marker [31, 14]. The graph (Fig. 7) indicates that genotype Belete had a higher marketable tuber yield than the average marketable yield in all test environments. Genotype Belete performed best at environment Dt2 followed by W1, Dk2, Dk1, Dt1 and lastly W2.

The genotype, environment and their interaction effect significantly affected potato genotypes. 40.2% of the variability was detected from the environment while 33.8% and 24.3% were contributed by the genotype and the GE interaction, respectively. The test environments were grouped to a single mega environment with Dt2, W1 and Dk2 as the most representative and discriminating environments. Among the environments, Dt2 was the ideal test environment to select the widely adapted genotypes. Hence, Genotype Belete was the vertex genotype where all the test environments fell, the most stable and high yielding ideal genotype among the thirteen studied genotypes. Genotypes Guassa, Gera and Jalene were stable and high yielders next to genotype Belete. Moreover, they had located nearby the ideal genotype on the GGE biplot and identified as the leading desirable genotype. Considering simultaneously yield performance and Yield stability, genotype Belete could be considered as the best genotype for the areas and similar agro-ecologies while genotype Guassa, Gera, and Jalene might be considered as alternative genotypes for both study areas and Similar agro-ecologies.

Ethics approval and consent to participate

It is not applicable to this study.

Consent for publication

Not applicable.

Availability of data and materials

All data generated from the study and reported in the manuscript are included in the article. Further data sets are available from the corresponding author upon request.

Competing interests

The authors declare that they have no competing interests.

Funding

Funding is not applicable to this research.

Authors' Contributions

SF, AA and TT: contributed to proposal writing, ccollection of Genotypes, data collection, supervision and assisting data Analysis. DA, HT and EM: assisted field management and supervision as well as data collection. AT, TA and DT: assist in data analysis and interpretation. SF: contributed to draft of the manuscript. DT and AA: contributed to critical revision of the manuscript. All authors read and approved the final manuscript.

Acknowledgments

The authors acknowledge the University of Gondar for providing resources to conduct this research. The authors also acknowledge Adet, Holeta and Sinana Agricultural Research Centers for being a tuber seed source. We use this opportunity to thank those district Agriculture Offices and their experts for their cooperation and facilitation during the field trials.

Authors' information

Authors and Affiliations

College of Agriculture and Environmental Sciences,

Department of Horticulture, University of Gondar. P.O. Box 196, Gondar, Ethiopia

Solomon Fantaw, Asrat Ayalew, Tiru Tesfa, Alemnew Tagele, Derajew Asres and

Ermias Misganaw

Department of Plant Sciences (breeding stream), University of Gondar. P.O. Box 196, Gondar, Ethiopia.

Tazebachew Asres and Daniel Tadesse

Institute of Biotechnology, Department of Plant Biotechnology, University of Gondar. P.O. Box 196, Gondar, Ethiopia

Henok Tadesse

Corresponding Author: Solomon Fantaw, E-mail: [email protected]

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AMMI and GGE biplot analysis of potato (Solanum tuberosum L.) genotypes for dry season performance of marketable tuber yield

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Abstract

Background

Results

Conclusions

Figures

Introduction

Material And Methods

Experimental material and design

Statistical analysis

Result And Discussion

The AMMI analyses of Variance

GGE Biplot analysis

Ranking of Genotypes Based on Yield and Stability Performance

Ranking the Genotype

Ranking of Environment

Ranking of genotypes relative to the ideal environment

Ranking environments relative to the highest yielding genotype

Conclusion

Declarations

References

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