De la familia y su representación

The combined analysis showed highly significant variation for genotype, environment and GEI for GY, indicating genotypes displayed differential expression of yield across the environments. The GEI effect was nearly twice as that of genotype effect. The relative contribution of GEI and genotype to the total variation of GY found in the present study was smaller than those previously found using the INGER irrigated nurseries. This is partially due to the extreme soil and/or weather conditions in some of the testing locations of the INGER nurseries. In the present study, no testing environments were under extreme stress (arguably the no nitrogen treatment can be regarded as abiotic stress). INGER nurseries had many more testing locations in many countries but only a small number of highly selected elite released or pre-released lines. Nevertheless, our results were similar to the results of the studies in rainfed lowland rice (Ouk et al., 2007; Tariku et al., 2013; Wade et al., 1999) and other crops (i. e. wheat, Bertero et al., 2004; Cooper et al., 1996; Canola, Zhang et al., 2013). Thus, it would be very difficult to select for improved lines with broad adaption by conducting selection only in one target environment, ignoring the observed GEI. Better breeding and testing strategy to accommodate the effects of large GEI is required. The following are possible options: (1) Adopting multi-environment testing at the early stages of variety development to allow selecting for general adaptation to be conducted earlier. We regard a program adopting early multi-environment testing allowing early selection for general

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adaptation as a potentially efficient way of breeding. An example of this is a collaborative shuttle breeding programme between the Indian Council of Agricultural Research and IRRI. This program has proved to be an effective tool for rice varietal improvement in rainfed lowland ecosystem in eastern India (Mallik et al., 2002). It provided an opportunity for flow of breeding materials of diverse origin among the eastern Indian states to strengthen the breeding programme. The materials developed through this project also served as input to the other breeding programmes for this ecosystem. For those breeders with limited resources, it might be practical to cooperate with peers located different places and thus achieve early multi-environment testing. Across-environment performance must be used as selection criterion when sufficient selection can still be applied. (2) Balancing the number of advanced lines tested in different stages of a multi-stage multi-environment testing scheme. Testing many lines in the first stages and a small number of lines at last stage is not a good option for obtaining reliable GEI information and maximizing genetic gain (Kempton and Fox, 1997). (3) Testing at least 50 lines from a breeding program to include enough genetic variation for the trait of interest. The GEI information obtained from testing a small number of lines from many breeding programs that do not exchange germplasm extensively is less relevant to any breeding program. (4) Subdividing the lowland irrigated ecosystem into more homogeneous TPEs to reduce the effects of GEI if repeatable GEI is identified (Atlin et al., 2000). Critical genotypic characters and soil and weather variables that account for a large proportion of GEI will need to be identified to help defining the TPEs. It should be pointed out that the present study was not aimed at characterizing the TPE of IRRI’s irrigated breeding program or the TPE of irrigated lowland ecosystem for indica rice in Asia. The number of locations used was few and the trials were conducted only in one year. To characterize the TPE for IRRI’s irrigated breeding program a representative sample of IRRI’s breeding lines to be tested in many more testing locations across multiple years is needed.

The GEI for GY was partitioned into principal component axis following the AMMI analysis. The first two principal components i.e. IPCA 1 and IPCA 2, accounted for 68.4% of the total variation, were significant and sufficient to explain the GEI. This is in accordance with Gauch and Zobel’s (1996) recommendation that the first two IPCAs are usually sufficient. Similarly, Yan and Rajcan (2002) also suggested that most of the interaction occurred in the first few axes. The GSI was the largest source of phenotypic variation for GY and accounted for 22.0% of the total variation. This was inconsistent with Samonte and Hernandez’s findings (1990). They found that GSI was significant only in four of their 12 combined analyses for yield and implied that there was no need to conduct stability and

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adaptability analysis. The inconsistence between the two results might partially contribute to the different origins of the testing lines. Samonte and Hernandez’s data was from lines of four maturity subgroups of the NCT. The testing lines were the promising rice lines selected over a certain number of seasons and over several testing locations nationwide in Philippines. They were more adapted to both of the DS and WS of Philippines. The lines in present study were from many programs including IRRI and PhilRice in Philippines, China and programs in other countries, which were not all subjected to selection under DS and WS. Another more probable reason is that the testing locations in the present study have much bigger differences than those used in Samonte and Hernandez (1990). JX and SC are in the subtropical region while IRRI is in the tropical region. Small GEI observed for yield related traits including DTF, GN, PB, PH, PN, SB, SN, SR, TGW and TN indicated that these traits were relatively more stable among the testing environments. Samonte and Hernandez’s findings (1990) also found that GSI and GLI had no significant effect on plant height, tillers and maturity, while the GSLI had significant effect on the three traits in most of the combined analysis.

The DS and WS environments in IRRI were grouped into different groups, indicating that they discriminated the genotypes in different ways. IRRI’s irrigated breeding program aims at developing varieties adapted to both of DS and WS. The results of present study showed that the vectors of DS and WS were not in the opposite direction in the biplots (Figure 3.1b), suggesting that it is possible to select genotypes with stable performance across seasons. However, with distinctive and highly repeatable seasonal pattern and different genotype responses to seasons it makes sense that variety development should explore the repeatable GEI caused by season. Much large genetic progress can be made even with the current breeding gene pools by breeding separately for the two seasons. For instance, the average grain yields of the top two genotypes in the DS, entry 60 and 58, were 1264.6g and 1247.9g. In the WS, they ranked the last sixth and sixtieth with average grain yields being only 764.3g and 695.3 g, respectively. Furthermore, it should be pointed out that the majority of the genotypes used in present study are IRRI lines, which have been derived from parental lines selected with stable performance across seasons as the key criterion and as results the interaction between genotype and season was underestimated. It is expected that GSI will be larger if new breeding populations are to be developed using parental materials that have not been selected for adaption to both of the seasons.

The three N treatments in the DS were grouped together to form one group while the three N treatments in the WS were grouped together in another group, indicating that the different N rates used had only a relatively small effect on the relative performance of

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genotypes, compared with the season. Previous studies indicated that it is difficult to create useful GEI patterns by use of managed environments in a single location (Cooper et al., 1996). Managed environments can only be useful if they are created by manipulating the key biotic and/or abiotic factors underlying the GEI. Therefore, it may be more appropriate to first investigate the GEI pattern and identify the major reasons for the observed GEI using multi-environment trials to then establish a set of managed environments to measure the GEI. The IRRI WS environments located close to SC in the biplot. This was consistent with the results of AMMI analysis of the 1994 and 1995 INGER nurseries (INGER 1994a, 1995a). Thus, it seemed that IRRI breeding lines with stable and good performance in the WS could be used in SC (directly as varieties or as parental lines in breeding). Similarly, JX was relatively closer to the IRRI DS in the biplot, suggesting that selection is better to be done in DS in IRRI for use in JX, China. The top 10 varieties recommended for SC, China were Entries 92, 208, 166, 107, 324, 58, 101, 369, 105, and 316. Entries 276, 285, 366, 316, 381, 280, 380, 272, 352, and 349 were best suited for JX, China based on yield performance.

3.5 Conclusion

Using a large number of indica genotypes from breeding programs for irrigated ecosystem and 8 testing environments the present study showed that GEI was very important for GY and the genotype-by-season interaction was the major source of GEI. We recommended breeding for different seasons separately to exploit the repeatable GEI caused by seasonal changes. The two testing environments in China were chosen to represent two major distinct rice production environments in China. SC was grouped together with the WS environments of IRRI. JX formed a separate group with more similarity to the DS environments of IRRI. Clearly, great attention should be paid to the relevance of performance at IRRI to their target production environments when IRRI breeding lines are introduced. On the other hand, with a global mandate IRRI’s irrigated rice breeding program should expand its testing and selection environments to allow exploiting specific adaption and providing critical and relevant performance information to the developing countries that largely depend on IRRI for new breeding lines.

68 This chapter has been published in the journal of Field Crops Research. Abstract is formatted according to the journal standards

Chapter 4

Usefulness of the cloned and fine-mapped