EL ÉXITO CLINICO DE LOS IMPLANTES INSERTADOS CON CIRUGÍA GUIADA

The biplot analysis, as viewed by the environment vector of genotypes for disease severity to LLS has been presented in Fig 4.12, 4.14 and 4.16. The results of PCA of genotype × environment interaction (GEI) showed that the first two PCs in the biplot explained 87.51% and 89.94% of the total variation due to GEI for LLS and rust at 90 DAS, respectively.

(a) Polygon view of GGE biplot analysis for LLS scores at 90 DAS

The polygon view of a biplot is the best way to visualize the interaction patterns between genotypes and environments to show the presence or absence of crossover GE interaction which is helpful in estimating the possible existence of different mega-environments. Visualization of the "which won where" pattern of MET data is necessary for studying the possible existence of different mega-environments in the target environment. Fig. 4.12 represents a polygon view of MET data of genotypes for LLS scores at 90 DAS. In this biplot, a polygon was formed by connecting the vertex genotypes with straight lines and the rest of the genotypes placed within the polygon. The partitioning of GE

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interaction through GGE biplot analysis showed that PC1 and PC2 together accounted for 87.51 of GGE sums of squares for LLS at 90 DAS. The vertex genotypes in the biplot were 262, 238, 3, 73, 186, 269, 82, 321, 256 and 268. These genotypes were the best or the poorest genotypes for disease resistance/susceptibility in some or all of the environments because they were farthest from the origin of the biplot. From the polygon view of biplot analysis of MET data of three environments, the genotypes fell in four sections and the test environments fell in two sections. The first section contains the test environments Aliyarnagar and Jalgaon which had the genotype 73 (TMV 2) as the genotype recorded higher disease score at 90 DAS considered most susceptible to LLS across the environments whereas genotypes 262 (ICGV 86699) plotted left side of biplot indicates that this genotypes had lowest disease severity score to LLS. The second section contains the environments ICRISAT_R15 with the genotype 321 (ICG 13895) as the best plotted farthest side biplot.

(b) Mean and stability performance of genotypes for LLS score at 90 DAS The ranking of 109 genotypes of GSP based on their disease severity score and stability performance are shown in Fig. 4.14. The line passing through the biplot origin is called the average environment axis (AEA), which is defined by the average PC1 and PC2 scores of all environments. A concentric circle drawn on AEA is called Average Environment Coordinate (AEC). The genotypes closer to concentric circle indicates higher mean performance. The line which passes through the biplot origin and is perpendicular to the AEA represents the stability of genotypes. Distance in either direction away from the biplot origin on this axis indicates greater GE interaction and reduced stability. The genotypes on the right side of this perpendicular line performed greater than mean disease severity score across the environments and the genotypes on the left side of this line had lesser score than mean across the environments. For selection, the stable resistant genotypes are those with lowest disease severity and least vector length from AEA. In the biplot, the genotypes plotted left side of biplot and have the shortest vector from the AEA are the superior and stable for

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disease resistance. The genotype 71 (GPBD 4), 238 (ICGV 00248), 84 (ICGV 06142), 152 (ICGV 02411), 237 (ICGV 00246), 246 (ICGV 00068), 293 (SPS 11), and 301 (ICG 11426) can be considered as stable genotypes with lower disease score and shortest vector length from AEA. The genotype 262 (ICGV 86699) had lowest disease score compared to other with greater vector length from AEA.

(c) Relationship among test environments

The summary of the interrelationships among the test environments has been presented in Fig 4.16. The lines that connect the biplot origin and the markers for the environments are called environment vectors. The angle between the vectors of two environments is related to the correlation coefficient between them. The cosine of the angle between the vectors of two environments approximates the correlation coefficient between them. Acute angles indicate a positive correlation, obtuse angles a negative correlation and right angles indicate no correlation. A short vector may indicate that the test environment is not related to other environments. Based on the angles between environment vectors, all the three environments (Aliyarnagar, Jalgaon, and ICRISAT_R15) were positively correlated with each other because of acute angles (<900_{) formed between them. The position of the environment on biplot}

revealed that ICRISAT_R15 was the best environments where genotypes got higher diseases scores followed by Aliyarnagar and Jalgaon. Jalgaon was the poorest environment plotted nearer to biplot origin indicates that genotypes recorded lower disease scores at Jalgaon. The ranking of environments in with respect to ideal test environments (Fig 4.16) revealed that the ICRISAT_R15 and Aliyarnagar are plotted on border of the inner circle in the biplot indicates that both had similar disease pressure and also revealed that both the environments are ideal for cultivar evaluation against LLS disease.

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Figure 4.12 Polygon view of scattered biplot showing ranking of genotypes based on which won where pattern for disease severity against late leaf spot at 90 DAS across three locations

Figure 4.13 Polygon view of scattered biplot showing ranking of genotypes based on which won where pattern for disease severity against rust at 90 DAS across three locations

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Figure 4.14 GGE biplot showing ranking of genotypes for mean performance and stability for disease severity score to LLS at 90 days after sowing across the three locations

Figure 4.15 GGE biplot showing ranking of genotypes for mean performance and stability for disease severity score to rust at 90 days after sowing across the three locations

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Figure 4.16 Comparison of environments with respect to ideal test environment for LLS severity. Area of inner circle of in biplot represents ideal test environment and the environment plotted within this circle are the best environment for cultivar evaluation

Figure 4.17 Comparison of environments with respect to ideal test environment for rust severity. Area of inner circle of in biplot represents ideal test environment and the environment plotted within this circle are the best environment for cultivar evaluation

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