Pod yield per hectare

The polygon view and ranking biplot for yield performance of selected genotypes of GSP have been presented in Fig. 4.24 and 4.25, respectively. The partitioning of GE interaction through GGE biplot analysis showed that PC1 and PC2 together accounted for 81.20% of GGE mean sums of squares for pod yield per hectare. The vertex genotypes in the biplot are 79, 24, 293, 3, 267, 165, 328, 334, 321, 34 and 335 indicating that these genotypes were the best or the poorest genotypes for pod yield per hectare in some or all the environments because they are farthest either of direction from the origin of biplot. The polygon view of MET data of four environments in the biplot showed that genotypes fell in four sections whereas the test environments fell in two sections. The first section contains the test environments Aliyarnagar and Jalgaon whereas the second section consists ICRISAT_R15 and ICRISAT_PR15. Among these four environments, ICRISAT post-rainy

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(ICRISAT_PR15) discriminating itself from other environments indicates varying performance of genotypes during post-rainy season compared to all rainy seasons across the environments while among rainy seasons Jalgaon is furthest from origin of biplot compared to Aliyarnagar and ICRISAT_R15 which had similar position on biplot indicates that genotypes had higher yield performance at Jalgaon than Aliyarnagar and ICRISAT_R15 (Fig 4.24).

The ranking of genotypes for mean pod yield and stability are presented in Fig. 4.25. The ranking biplot of genotypes based on higher mean and stability revealed that genotype 154 (ICGV 06100) followed by 26 (ICGV 05163), 153 (ICGV 05155), 30 (ICGV 07223), 32 (ICGV 07235), 253 (ICGV 02323), 266 (ICGV 06099), 37 (ICGV 07120), 152 (ICGV 02411), 25 (ICGV 05161), 45 (ICGV 03043), 1 (ICGV 06423), 42 (ICGV 01273) and 27 (ICGV 06422) were the genotypes plotted near to AEC with shortest vector length from AEA indicating their superior performance and stability for pod yield per hectare. The genotype 3 (ICGV 07247), followed by 24 (ICGV 03064), 293 (SPS 11), 180 (ICGV 01276), 247 (ICGV 01495), 84 (ICGV 06142), 43 (ICGV 01274), 76 (ICGV 03042), 109 (49 M-16), 268 (ICGV 05032) were also high yielding genotypes but greater vector length from AEA indicates their unstable performance for pod yield per hectare. Among these, 3 (ICGV 07247), 180 (ICGV 01276), 247 (ICGV 01495) and 109 (49 M-16) are plotted nearer to environment Aliyarnagar and Jalgaon indicated that these genotypes have location specific adaptability under these environments whereas 24 (ICGV 03064), 293 (SPS 11), 84 (ICGV 06142), 43 (ICGV 01274) and 76 (ICGV 03042) plotted towards ICRISAT_PR15 indicates these were superior at ICRISAT during post-rainy season compared to other genotypes (Fig 4.25).

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Figure 4.22 Polygon view of scattered biplot showing ranking of genotypes based on which won where pattern for hundred seed weight across three environments

Figure 4.23 GGE biplot showing ranking of genotypes for mean performance and stability for hundred seed weight across the three environments

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Figure 4.24 Polygon view of scattered biplot showing ranking of genotypes based on which won where pattern for pod yield hectare across three environments

Figure 4.25 GGE biplot showing ranking of genotypes for mean performance and stability for pod yield per hectare across the three environments

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4.5.8 Nutritional quality traits

The polygon view and ranking biplot for oil protein, oleic and linoleic acid have been presented in Fig. 4.26 to 4.33. The partitioning of GE interaction through GGE biplot analysis showed that PC1 and PC2 together accounted for 82.47, 71.73, 92.02 and 89.77% of GGE mean sums of squares for oil, protein, oleic and linoleic acid. The vertex genotypes in the biplot were 335, 73, 203, 167, 30, 164 and 335 for oil content (Fig 4.26) whereas 203, 180, 109, 84, 137, 82, 26, 153, 296 and 180 for protein content (Fig 4.28), 254, 296, 301, 38, 280, 262, 335, 76 and 24 for oleic acid (Fig 4.30) and 33, 301, 73, 135, 190, 251, 76, 79, 64, 288 and 262 for linoleic acid (Fig 4.32). These vertex genotypes were the best or the poorest genotypes for hundred seed weight in some or all the environments because they are farthest from the origin of the biplot. From the polygon view of biplot of MET data of four environments, the genotypes fell in four sections whereas the test environments fell in two sections for all nutritional quality traits. The first section contains the test environments Jalgaon, ICRISAT_R15 and ICRISAT_PR15 with a narrow-angle between them indicates that genotypes had similar performance for oil content in these three environment’s whereas the second section consists Aliyarnagar discriminating itself from other environments (Fig 4.26). For protein content, the first section consisted environments Jalgaon and Aliyarnagar and second consisted ICRISAT_R15 and ICRISAT_PR15. Among these Aliyarnagar plotted farthest from biplot origin indicating that most of the genotypes had higher protein content at Aliyarnagar compared to other environments. Jalgaon, Aliyarnagar, and ICRISAT_R15 were in the first section whereas ICRISAT_PR15 discriminated itself from other environments in the biplot for oleic acid (Fig 4.28).

The ranking of selected genotypes of GSP for oil, protein, oleic and linoleic acid content based on mean and stability performance is presented in Fig. 4.27, 4.29, 4.31 and 4.33, respectively. In the biplot, the genotypes plotted right sides of biplots with shortest vector length from AEA are better genotypes. The genotype 153 (ICGV 05155) followed by 71 (GPBD 4), 244 (ICGV 97128),

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296 (SPS 21), 238 (ICGV 00248), 84 (ICGV 06142), 76 (ICGV 03042), 293 (SPS 11), 147 (SPS 9), 109 (49 M-16), 29 (ICGV 07220), 152 (ICGV 02411), 80 (ICGV 06424), 154 (ICGV 06100), 26 (ICGV 05163) and 265 (ICGV 06040) are near to AEC with shortest vector length from AEA indicates their superior and stability performance for oil content (Fig 4.27). Genotypes 335 (ICG 2381), 174 (ICG 12625), 164 (ICG 6022), 64 (ICGV 99085) and 79 (ICGV 06420) were also higher oil containing genotypes but with greater vector length from AEA indicates their instability for oil content. The genotypes 180 (ICGV 01276) followed by 229 (ICGV 00362), 170 (ICG 10053), 109 (49 M-16), 293 (SPS 11), 118 (24 M-86), 231 (ICGV 02287), 253 (ICGV 02323), 186 (ICGV 02266), 44 (ICGV 02321) and 43 (ICGV 01274) were plotted right side of the biplot with shorter vector length from AEA indicating their superiority and stability for protein content across the environments compared to other genotypes (Fig 4.29). However genotypes, 39 (ICGV 97120) followed by 301 (ICG 11426), 295 (SPS 20), 280 (ICG 11337), 164 (ICG 6022), 174 (ICG 12625), 294 (SPS 15), 64 (ICGV 99085), 288 (SPS 2), 108 (49 × 39-8), 29 (ICGV 07220) and 71 (GPBD 4) were the high performing genotypes across the environments with least vector length from AEA for oleic acid (Fig 4.31). Low linoleic acid is desired traits in groundnut for higher nutritional quality with longer self-life therefore, genotypes plotted left side of the biplot with least vector length are the superior compared to others. In the biplot, genotype 39 (ICGV 97120), 301 (ICG 11426), 73 (TMV 2), 295 (SPS 20), 108 (49 × 39-8), 71 (GPBD 4), 29 (ICGV 07220), 288 (SPS 2), 262 (ICGV 86699) and 294 (SPS 15) had lower linoleic content with shorter vector length from AEA indicating their superiority and stability for low linoleic acid (Fig 4.33).

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Figure 4.26 Polygon view of scattered biplot showing ranking of genotypes based on which won where pattern for oil content across four environments

Figure 4.27 GGE biplot showing ranking of genotypes for mean performance and stability for oil content across four environments

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Figure 4.28 Polygon view of scattered biplot showing ranking of genotypes based on which won where pattern for protein content across four environments

Figure 4.29 GGE biplot showing ranking of genotypes for mean performance and stability for protein content across four environments

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Figure 4.30 Polygon view of scattered biplot showing ranking of genotypes based on which won where pattern for oleic acid content across four environments

Figure 4.31 GGE biplot showing ranking of genotypes for mean performance and stability for oleic acid content across four environments

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Figure 4.32 Polygon view of scattered biplot showing ranking of genotypes based on which won where pattern for linoleic acid content across four environments

Figure 4.33 GGE biplot showing ranking of genotypes for mean performance and stability for linoleic acid content across four environments

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