{'fraction_non_alphanumeric': 0.0443257206598955, 'fraction_numerical': 0.02677156108730112, 'mean_word_length': 4.829568788501026, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 28, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Girgel-Cokkizgin: GGE biplot analysis in wild (Pisum sativum L. subsp. elatius and subsp. sativum) and cultivated pea (Pisum sativum L.) genotypes in northern and southern turkey

This research was carried out in the multiple-environment trials (MET) which one is in Bayburt (40°24'05.7"N 40°08'31.3"E) where the Black Sea geographical region located in the north of Turkey and in the Kahramanmaras (37°35'24.8"N 36°48'49.4"E) where the Mediterranean geographical region located in the south of Turkey in 2016 and 2017 years. In this investigation, 6 registered pea cultivars (Bolero, Jof, Karina, Nihal, Reyna and Utrillo) and 2 wild pea genotypes (Pisum sativum L. subsp. elatius and P. sativum L. subsp. sativum) were used. The variance analysis showed a significant difference among Location (L), genotypes (G), genotypes×locations interaction (GL), genotypes×year interaction (GY), year×location interaction (YL) (all significant at P<0.01 level) and genotype×location×year interaction (GLY) (significant at P<0.05 level) interaction in terms of seed yield. Main effect genotypes (G) is explained 24.42% of total variation. This is followed by 18.78% (L), 17.87% (YL) 7.04% (GL), 4.91% (GY), 2.71% (GLY) and 1.12% (Y), respectively. Graphs were drawn according to the results of GGE biplot analysis and yield data of genotypes were evaluated according to these graphs. In accordance with GGE biplot analysis the seed yield first principal component explained 82.81% of the total variation and second principal component explained 17.19% of the total variation.


Introduction
Dry pea cultivation was carried out on 8.141.031 hectare area in the world and it has 16.205.448 tons production and 1990,6 kg/ha yield value. On the other hand green pea cultivation has 2.669.305 hectare area value, 20.699.736 tons production and 7754,7 kg/ha yield value worldwide (FAO, 2018).
Peas are the second most produced legumes in worldwide (FAO, 2018). The number of diploid chromosomes in pea is 14 (Murtaza et al., 2005) and it is a legume crops which origin is near east (Abbo et al., 2017). This region is also the source of wild pea subspecies (Symkal et al., 2015). On the other hand for further pea crop improvement the wild peas are important sources of genetic diversity in climate change context (Symkal et al., 2018).
It is known that Biplot concept started with Descartes. Also Ptolemy used to map cities in ancient times (Gower et al., 2011). Also "Biplot" is a term used by Gabriel (1971), but the first using of biplots in agricultural data was Bradu and Gabriel (1978), as popular versions of variables which were represented by directed vectors and Gower and Hand (1996), emphasized its advantages of biplots with calibrated axes. Genotype×Environment interaction is complicate source of variation and also makes the selection of genotype(s) difficult (Magari and Kang, 1993). Genotype by environment (GE) interaction may vary according to crops and years (Milligan, 1994). When G×E interaction is statistically significant, its nature, reason and results must be carefully examined (Magari and Kang, 1993). On the other genotype environment interaction is very effective on seed yield to reveal the effectiveness of interaction; trials should have multiple years and multiple locations (Rao et al., 2011;Luo et al., 2015).
The objectives of the study were to (1) reveal genotype, location, year values, (2) determine genotype by location interaction, (3) determine genotype by year interaction, (4) determine year×location interaction, (5) determine genotype×location×year interaction, (6) reveal the stability of wild and cultivated pea genotypes in Bayburt and Kahramanmaras locations (in northern and southern part of Turkey) via GGE biplot analysis. As a result, more compatible genotypes will be obtained in future breeding studies.

Research areas
This research was carried out in the multiple-environment trials (MET). The locations were Bayburt/Turkey (Bayburt University, Food and Agriculture and Livestock Application and Research Center area) (40°24'05.7"N 40°08'31.3"E) and Kahramanmaras/Turkey (Kahramanmaras Sutcu Imam University, Field Crops Department Treatment area) (37°35'24.8"N 36°48'49.4"E). The study was conducted at the locations in 2016 and 2017 years. These locations are located in the northern and the southern of Turkey (Table 1). Climate data of the research areas are given in Table 2. In the research, each plot consists of 5 rows, the distance between rows was 50 cm and the plot length was 5 m, total plot area is determined as 12.5 m 2 and plant density was 40 plant m -2 .

Seed materials
Two wild pea genotypes (Pisum sativum L. subsp. elatius and P. sativum L. subsp. sativum) in which they were gained from natural vegetation in Turkey and six registered pea cultivars (Bolero, Jof, Karina, Nihal, Reyna and Utrillo) were used and the study was used in the study. The selection criterion was considered to be the genotype that can adapt to both regions.
Before the seed yield was obtained, one row on each side of the plot and 0.5 m from both ends of the plot which was evaluated as side effect were excluded from the evaluation and the central rows harvested by hand, according to Girgel (2013).

Statistical analysis
In the research, for the phenotypic variation, P = E + G + GE formula was used. In the formula, the phenotype P represents the environment, the G genotype, and GE represents the genotype×environment interaction (Yan and Kang, 2003). Also, year and location factors are within environment (E).
In addition to being a multiple-environment trials (MET), the factors could be divided because it was conducted for two years (2016 and 2017). For a more detailed variance analysis (E) is divided year (Y) and location (L) factors also all interactions between G, L and Y factors were evaluated according to Comstock and Moll (1963).
The experiment was established with 3 replications in according to randomized complete block design (RCBD). Statistical analysis was performed with GenStat Release 12.1 (VSN International Ltd.) according to Gomez and Gomez (1984) and Rao (2007). And GGE biplot graphs and analysis was performed according to Gabriel (1971)

Analysis of variance
In the study, genotype, locaiton, year and their interactions were examined. The variance analysis showed a significant difference among genotypes (G), locations (L), genotypes×location interaction (GL), genotypes×year interaction (GY), year×location interaction (YL) (all significant at P<0.01 level) and genotype×location×year interaction (GLY) (significant at P<0.05 level) in terms of seed yield ( Table 3). Luo et al. (2015) reports that the factors of genotype, location, year, and all interactions between them were found to be statistically significant. And similar statistical results reported by Kan et al. (2010). In the investigation, variance analysis showed that the genotypes had greater effect on seed yield than the factors location or year. Genotype, environment and year factors and their interactions explain 76.85% of the total variance ( Table 3). In here the greatest variance value is due to main effect genotypes (G) and it is explained 24.42% of total variation. This is followed by 18.78% (L), 17.87% (YL) 7.04% (GL), 4.91% (GY), 2.71% (GLY) and 1.12% (Y), respectively. On the other hand, least significant difference statistical groups (LSD) for all measurement interactions and factors were determined with the help of MSTAT-C software package program (v2.10) (MSTAT-C, 1994) ( Table 4).
Environmental factors and primarily genetic structure are the main factors in determining the phenotype. As a result of the interactions of these factors reveals the amount of seed yield. Yan and Kang (2003) reported that phenotypic variation consists of genotype and environment and their interaction. Sabaghnia et al. (2011) reported that when there are no different locations, the year factor explained the large proportion of the total variation. On the other hand, similar results gained by Noerwijati et al. (2014).
According to GGE biplot analysis results for the seed yield first principal component explained 82.81% of the total variation and second principal component explained 17.19% of the total variation. The PCA (Principle Component Analysis) model based on the GGE biplot explains further variation (Yan and Kang, 2003).

Scatter plot results
According to the scatter plot graph for ranking of locations and genotypes, it can be said that there is almost none correlation the between Bayburt and Kahramanmaraş locations due to the acute angle value is very close to the right angle (Fig. 1). In the analysis, the cosine of the angle between vectors shows that correlation between vectors i.e. obtuse angles shows that negative association and acute angle indicate positive association and at right angles there is no relationship between vectors (Yan, 2014; Dallo et al., 2019).   Narrow angle, indicates a correlation between genotypes. Similar opinions have been reported by Yan (2002) and Kang et al. (2006). Utrillo cv. was found to be most yielding genotype but Pisum sativum subsp. elatius was determined as the lowest yielding but the most stable genotype.
In terms of the years the first principal components (PC1) and second principal components (PC2) of the seed yield accounted as 87.65 and 12.35, respectively (Fig. 2). The years was similar to each other due to the narrow angle and there is a very small amount positive correlation. In other words, the conditions of the years are similar. It is reported that in such graphs, there is a correlation between the parameters examined when there is a narrow angle (Yan, 2002). Pea plants showed better adaptation under the first year environmental conditions and the first year point was close to the stability line than second year. On the other hand, the Nihal variety was determined as the genotype closest to the stability line (Fig. 2). Snoad and Arthur (1974), point out that repeated trials for many years reveal genotype-environment interaction in a more understandable way.
According to polygonal Genotype×Environment interaction scatter biplot graph, under the conditions of Kahramanmaras, Utrillo genotype is environmentally compatible and under the Bayburt conditions, Reyna and bolero genotypes are located in the same sectors and emerged as high performance varieties that are compatible with this environment (Fig. 3). Genotype×Environment interaction is very important for genotype selection and therefore, repeating the experiments at different years and locations provides accurate genotypic results. Similar opinions reported by Weber et al. (1996). On the other hand, Yan and Kang (2003) recommended a biplot polygon graph to better understanding the genotype environment interaction.
According to polygonal Genotype×Year interaction scatter biplot graph, in the first year Reyna and Nihal, in the second year, Utrillo and Nihal varieties appeared as the highest performance genotypes (Fig. 4). In the graph, the polygon was divided into 5 parts and the years had taken place in different sections. As a rule, polygon edges and axes were at right angles to each other. If the locations are not taken into consideration, in terms of years, the cv. Bolero was located at the center of the axis in the graph and had been the least affected by the environmental conditions of that year. However, this genotype had less seed yield. On the other hand, cv. Nihal was determined to be stable regardless of locations and it had higher seed yield from cv. Bolero.

Ranking biplot results
GGE ranking biplot graph shows the status of genotypes by Bayburt location (Fig. 5). In the graph a line representing the province of Bayburt was drawn through the biplot origin and another a line perpendicular to the Bayburt axis was drawn from each genotype. The Bayburt line represented the axis for the Bayburt province. Yan and Tinker (2006) reported that the seed yield in the direction of the arrow increases and genotypes closer to the arrow line are more stable. When the GGE ranking biplot graph was examined, Reyna variety showed the highest performance in Bayburt province, followed by Utrillo variety, but it was found that Reyna, which is close to the axis in terms of stability, was more convenient (Fig. 5). In the GGE ranking biplot graph the Kahramanmaras line represented the axis for the Kahramanmaras province. Yan and Tinker (2006) pointed that the seed yield in the direction of the arrow increases and genotypes closer to the arrow line are more stable. When the GGE ranking biplot graph was examined, under the Kahramanmaraş conditions, the Utrillo variety was determined as highest seed yield and the most stable genotype in terms of seed yield (Fig. 6).

Comparison biplot results
By using the ideal environment as the center in order to visualize the distance between Bayburt and Kahramanmaras locations concentric circles were drawn (Yan et al., 2000;Yan and Rajcan, 2002).
The comparison biplot graph assumes that the province of Bayburt has ideal environmental conditions and draws from the center towards the outer ring suitability for genotypes. Concentric circles were drawn so that the ideal environment is in the center (Yan et al., 2000;Yan and Rajcan, 2002). Bayburt ecological conditions were considered as an ideal environment; Reyna variety was found to be most suitable. In   (Fig. 7). Our results are in agreement with other observational study of Yihunie and Gesesse (2018). Figure 6. GGE ranking biplot graph shows according to Kahramanmaras  Kahramanmaras as the ideal environment according to the comparison biplot graph, Utrillo and Nihal varieties are found to be appropriate (Fig. 8). The rings moving away

Joint biplot results
When the provinces were evaluated together according to the joint biplot graph, it was determined that Utrillo was a more stable variety on the other hand Reyna showed higher yield differences (Fig. 9).  Two genotype or two locations can be visually compared by connecting them with a linear line in a GGE biplot (Yan and Tinker, 2006). When the two provinces are evaluated together, the yield ranking of the varieties is as follows; Utrillo > Reyna > Nihal > Bolero > Pisum sativum subsp. sativum > Karina > Jof > Pisum sativum subsp. elatius. However, Reyna Jof and Nihal varieties were located further away from the stability line.
Considering the biplot graph based on an average of two years, genotypes showed as follows ranking; Utrillo > Reyna > Nihal > Bolero > Pisum sativum subsp. sativum > Karina > Jof > Pisum sativum subs. elatius (Fig. 10). Subsp. sativum was the closest wild species to the cultivars, so yield levels were higher than some cultivars. Nihal, Pisum sativum subsp elatius ve Bolero genotypes were determined to be highly stable, regardless of locations, according to two year averages. Repeated trials for many years reveal GEI in a more understandable way (Snoad and Arthur, 1974).

Environment-centered data results
According to environment-centered data graphs, when two locations were taken into consideration, Utrillo, Jof, Nihal performed better in Kahramanmaras, while Reyna, Bolero, Pisum sativum L. subsp. sativum, Karina and Pisum sativum L. subsp. elatius had better performance in Bayburt province (Fig. 11). Subsp.sativum was the closest wild species to the cultivars, so yield levels were higher than some cultivars. To select superior genotypes and increase efficiency in selection, the biplot analysis is a good method (Dallo et al., 2019).  Figure 11. Environment-centered data graph shows that sorting genotypes by environment (x: shows wild and cultivated genotype scores) (

Conclusion
Biplot analysis is an important technique in crop improvement and agricultural research. GGE biplot is an appropriate method for genotype selection and data analysis for agronomists, geneticists and breeders of the plant.
According to GGE biplot analysis results for the seed yield first principal component explained 82.81% of the total variation and second principal component explained 17.19% of the total variation.
In Bayburt province Reyna cultivar was highest yielding and most stable genotype, in Kahramanmaras province Utrillo cv. highest yielding and most stable genotype. When evaluated together for two years, ranking of the wild and cultivated pea genotypes such as Utrillo > Reyna > Nihal > Bolero > Pisum sativum subsp. sativum > Carina > Jof > subsp. elatius. Considering the two-year results obtained from Bayburt and Kahramanmaras, ranking of the genotypes such as Utrillo > Reyna > Nihal > Bolero > Pisum sativum subsp. sativum > Carina > Jof > Pisum sativum subsp. elatius.
According to all these results, it would be more appropriate to use Utrillo variety. On the other hand, wild genotype Pisum sativum subsp. sativum has higher seed yield from some standard varieties (Karina and Jof) and as an important result it can be used to provide gene in breeding studies.