Components of Genotype by Environment interaction ... - CiteSeerX

Zhou M et al

Proc S Afr Sug Technol Ass (2011) 84: 363 - 374

REFEREED PAPER

COMPONENTS OF GENOTYPE BY ENVIRONMENT INTERACTION AMONG SASRI REGIONAL BREEDING AND SELECTION PROGRAMMES AND THEIR IMPLICATIONS ZHOU M, JOSHI S, MARITZ T AND KOBERSTEIN H South African Sugar Sugarcane Research Institute, Private Bag X02, Mount Edgecombe, 4300, South Africa [email protected]

Abstract Genotype by environment interaction (GxE) influences and complicates the selection of superior genotypes in trials by confounding the determination of true genetic values. Trials therefore need to be planted over several locations and seasons. The South African Sugarcane Research Institute (SASRI) operates seven regional breeding programmes representing the major agro-climatic regions of the sugar belt. The objective of this study was to determine the variance components of GxE and evaluate their relative importance and implications in the breeding programmes. Data were analysed using the mixed procedure of the Statistical Analysis System (SAS) to estimate variance components and test their significance. There was significant genotype by location in the irrigated and coastal long cycle than the coastal short cycle and Midlands programmes, indicating the importance of identifying and characterising sites used for variety trials. The genotype by crop-year variance component was more significant for the rainfed than irrigated programmes indicating that breeding and selecting for ratooning ability was critical in rainfed regions. Genotype by location by crop-year interaction was more significant for yield than sucrose content, highlighting the complexity associated with breeding and selecting for yield. The variance components also showed that the coastal long cycle and hinterland programmes were the most complex and were generally characterised by large GxE. Programmes with crop cycles of more than 12 months produced more complex variance components indicating the difficulty of making genetic gains. Keywords: genotype by environment interaction, variance components, rainfed, irrigated

Introduction In sugarcane breeding programmes worldwide, genotype x environment interaction (GxE) is known to influence the selection of superior genotypes in trials (Kang and Miller, 1984; Milligan et al., 1990; Mirzawan et al., 1993, 1994; Jackson et al., 1991; Kimbeng et al., 2002, 2009). The presence of GxE complicates selection decisions because the performance of the elite genotypes becomes conditional on the local environment where the variety is planted (Rattey and Kimbeng, 2001). For quantitatively inherited traits such as cane and sugar yield, the genotype values and their relative rankings can change from one environment to another (Kang, 2002). These rank changes confound the determination of the overall true genetic value of the prospective varieties (Kimbeng et al., 2009).

363

Zhou M et al


When GxE exists, plant breeders need to accurately sample the target environmental conditions where the varieties will be grown after release using trials planted at several sites and locations (Kimbeng et al., 2009). Because sugarcane is a perennial crop, these environments are made up of locations and crop stages (plant, first, second ratoon crops). Locations may also be chosen to represent different soil types in addition to agro-climatic conditions (Nuss, 1998). Most sugarcane breeding programmes plant advanced stage variety trials at several locations and harvest these trials over several years. In sugarcane, yield and quality data in the first and second ratoon crops are measured from the same plots as the plant cane to facilitate the assessment for ratooning ability. As a result, the effects of years are confounded as each crop stage is grown in a different year. Therefore, the effects of years and crop stages are referred to as crop-years (Kang et al., 1987). Studies on GxE provide guidance in developing strategies for testing and selecting genotypes best adapted to the targeted environments (Rea and de Sousa-Vieira, 2002). Previous studies in Queensland, Australia (Jackson et al., 1991; Jackson and Hogarth, 1992; Rattey and Kimbeng, 2001; Kimbeng et al., 2002), Louisiana (Milligan et al., 1990) and Texas in the USA (Kimbeng et al., 2009) suggested that results from GxE are not universal. The South African Sugarcane Research Institute (SASRI) operates seven regional breeding and selection programmes (Table 1) that were established to develop varieties for their respective agro-ecological regions (Nuss, 1998; Parfitt, 2000, 2005; Parfitt and Thomas, 2001). The secondary variety trials (the last testing stage) are established in several trials planted both on and off-station to represent the prevailing soil types and other agro-climatic conditions that exist in a region. These trials are harvested in the plant crop, and two or three ratoons. Since the inception of the new regional breeding programmes in 1998 (Nuss, 1998), there has been no study on GxE (except for Parfitt, 2000) and its potential influence on variety improvement. These programmes have established advanced variety trials since 1998, and the data collected over 12 years provides an opportunity to evaluate the impact of GxE. The results of the study are expected to guide the future of the breeding programme and help in determining the most important variables to focus selection on in order to achieve significant genetic gains. Table 1. Regional programmes, research stations and conditions represented (Anon., 2003). Programme F T U G K B S

Research station Pongola Empangeni Gingindlovu Gingindlovu Kearsney Bruyns Hill Glenside

Agro-climatic regions Northern Irrigated areas Coastal high Potential Coastal average potential Coastal average potential Coastal hinterland Midlands – Humic soils Midlands – Sandy soils

Harvest age 12 months 12 months 12-15 months 18 months 18 months 18-24 months 18-24 months

The objectives of this study were to determine whether GxE was present for sugar yield and its components (cane yield and sucrose content) in the secondary variety trials and, where GxE was present, to determine the statistical significance, relative importance and implications of the variance components of GxE.

364

Zhou M et al


Materials and Methods Trial sites Data were collected from trial series planted from 1997 to 2008 and harvested from 1998 to 2010 (Table 2). For the irrigated sites, FV and FV2 represented early and late planted and harvested trials at the Pongola research station, respectively (Parfitt, 2000). NPV2 represented a late planted off-station trial in the Pongola area. NTV2 represented a late planted trial at Mhlati in the Malelane area, while NNV represented an early planted trial at Komatidraai in Komatipoort. The Malelane and Komati areas are in the Mpumalanga province of South Africa and represent the Lowveld irrigated agro-climatic conditions. For the coastal short cycle programmes, TV and T1V represented on and off-station trials at the Empangeni research station, respectively. UV was planted at Gingindlovu research station while U1V was planted off-station. GV was planted on-station while G1V and G2V were planted off-station for the Gingindlovu area. KV was planted on-station at Kearsney research station while K1V was planted off-station for the Kearsney area. For the Midlands programmes, BV was planted on-station at Bruyns Hill research station while B1V and B2V were planted off-station. SV was planted on-station at Glenside research station while S1V and S2V were planted off-station. Experiment design and data collection All the trials were laid out as randomised block design with three replications. The plot sizes were five rows by eight metres long. The number of genotypes planted in each trial ranged from 24 to 36 (Table 2). Different genotypes were planted to trials each year in each programme. At harvest, all the millable stalks in the plots were cut and weighed. From each plot, 12 stalks were randomly picked to provide a sample for sucrose determination. Data analysis The data were subjected to analysis of variance using the statistical linear mixed model where all variables were considered random: Yijkl = µ + Ll + R(L)k(l) + Gi + GLil + GR(L)ik(l) + Cj + LCjl + CR(L)jk(l) + GCij + GLCijl + Eijkl Equation 1

where, Yijkl = observation for genotype i, in crop-year j, in replication k nested within location l; µ = overall mean; Ll = the random effect of the lth location; R(L)k(l) = the random effect of the kth replication nested within the lth location and was the error term for the location effects; Gi = the random effect of the ith genotype; GLil = the random interaction effect between the ith genotype and the lth location; GR(L)ik(l) = the random interaction effect between the ith genotype and the kth replication nested with the lth location and was the error term for the genotype and genotype by location interaction effect; Cj = the random effect of the jth crop-year; LCjl = the random interaction effect between the lth location and the jth crop-year; CR(L)jk(l) = random interaction effect between the jth crop-year and the kth replication nested within the lth location and was the error term for the crop-year and location by crop-year interaction effect; GCij = random interaction effect between the ith genotype and the jth crop-year; GLCijl = random interaction effect between the ith genotype, lth location and jth crop year; and Eijkl = residual error. The genotypes, although selected, were taken in this study to represent a random sample of several possible advanced stage genotypes in the SASRI sugarcane breeding programme. Similarly, although the location were chosen to represent sugarcane production environments differing in soil

365

Zhou M et al


characteristics, together with crop-years they represent a random sample of possible sugarcane growing environments being targeted by the regional breeding programmes. Table 2. The series, number of genotypes, number of crops, trial sites and locations. Series

Genotypes

F G H I J K L M N O P Q

32 32 32 33 34 35 34 33 36 36 36 35

G H I J K L M N O P Q

31 31 33 31 30 30 30 30 30 30 32

G H I J K L M N O P

29 30 30 30 30 30 28 30 31 32

G H I J K L M N O

31 30 24 31 33 32 30 31 33

Crops

Trial sites Irrigated P, 1R, 2R, 3R FV, FV2, NPV2 P, 1R, 2R, 3R FV, FV2, NPV2 P, 1R, 2R, 3R FV, FV2, NPV2, NTV2 P, 1R, 2R, 3R FV, FV2, NPV2, NTV2 P, 1R, 2R, 3R FV, FV2, NPV2, NNV, NTV2 P, 1R, 2R, 3R FV, FV2, NPV2, NNV, NTV2 P, 1R, 2R, 3R FV, FV2, NPV2, NNV, NTV2 P, 1R, 2R, 3R FV, FV2, NPV2, NNV, NTV2 P, 1R, 2R, 3R FV, FV2, NPV2, NNV, NTV2 P, 1R, 2R, 3R FV, FV2, NPV2, NNV, NTV2 P, 1R, 2R FV, FV2, NPV2, NNV, NTV2 P, 1R FV, FV2, NPV2, NNV, NTV2 Coastal short cycle P, 1R, 2R TV, UV P, 1R, 2R TV, UV P, 1R, 2R TV, T1V, UV P, 1R, 2R TV, T1V, UV, U1V P, 1R, 2R TV, T1V, UV, U1V P, 1R, 2R TV, T1V, UV, U1V P, 1R, 2R TV, T1V, UV, U1V P, 1R, 2R TV, T1V, UV, U1V P, 1R, 2R TV, T1V, UV, U1V P, 1R, 2R TV, T1V, UV, U1V P, 1R TV, T1V, UV, U1V Coastal long cycle and hinterland P, 1R, 2R GV, KV P, 1R, 2R GV, G1V, G2V, KV, K1V P, 1R, 2R GV, G1V, G2V, KV, K1V P, 1R, 2R GV, G1V, G2V, KV, K1V P, 1R, 2R GV, G1V, G2V, KV, K1V P, 1R, 2R GV, G1V, G2V, KV, K1V P, 1R, 2R GV, G1V, G2V, KV, K1V P, 1R, 2R GV, G1V, G2V, KV, K1V P, 1R GV, G1V, G2V, KV, K1V P, 1R GV, G1V, G2V, KV, K1V Midlands P, 1R, 2R BV, SV P, 1R, 2R BV, B1V, SV, S1V, S2V P, 1R, 2R BV, B1V, SV, S1V, S2V P, 1R, 2R BV, B1V, SV, S1V, S2V P, 1R, 2R BV, B1V, SV, S1V, S2V P, 1R, 2R BV, B1V, B2V, SV, S1V, S2V P, 1R, 2R BV, B1V, B2V, SV, S1V, S2V P, 1R BV, B1V, B2V, SV, S1V, S2V P, 1R BV, B1V, B2V, SV, S1V, S2V

Location Pongola Pongola Pongola, Malelane Pongola, Malelane Pongola, Komati, Malelane Pongola, Komati, Malelane Pongola, Komati, Malelane Pongola, Komati, Malelane Pongola, Komati, Malelane Pongola, Komati, Malelane Pongola, Komati, Malelane Pongola, Komati, Malelane Empangeni, Gingindlovu Empangeni, Gingindlovu Empangeni, Gingindlovu Empangeni, Gingindlovu Empangeni, Gingindlovu Empangeni, Gingindlovu Empangeni, Gingindlovu Empangeni, Gingindlovu Empangeni, Gingindlovu Empangeni, Gingindlovu Empangeni, Gingindlovu Gingindlovu, Kearsney Gingindlovu, Kearsney Gingindlovu, Kearsney Gingindlovu, Kearsney Gingindlovu, Kearsney Gingindlovu, Kearsney Gingindlovu, Kearsney Gingindlovu, Kearsney Gingindlovu, Kearsney Gingindlovu, Kearsney Bruyns Hill, Glenside Bruyns Hill, Glenside Bruyns Hill, Glenside Bruyns Hill, Glenside Bruyns Hill, Glenside Bruyns Hill, Glenside Bruyns Hill, Glenside Bruyns Hill, Glenside Bruyns Hill, Glenside

The data were analysed using the mixed procedure of SAS (SAS Institute, 2009). The estimates of variance components, their standard errors and their significant tests were done using the COVTEST option in the model statement (Littell et al., 2008).

366

Zhou M et al


Results Variance components The irrigated region genotype effect variance component (G) was significant (PGxL for cane and sugar yield, and G>GxC>GxL>GxLxC for sucrose content. The variance component for the error term for cane and sugar yield for G, H, I was larger than that for J, K, L, M, N, O, P. The error term variance component was generally the largest. The coastal long cycle and hinterland regions produced significant (PG=GxC for sugar yield. The G from L to O were all significant (PGxL>GxLxC for sucrose content. For yield, testing and selecting across locations appeared less important for the coastal short cycle than ratooning ability (GxC). The high GxLxC indicated the complex nature of GxE. It appears that ratooning ability is location specific. The trends may also indicate that current trial sites may not be adequately representative and further studies are required to verify these trends. The important GxC for sucrose content indicate the effect of seasonal variability on sucrose content. Selecting genotypes that are stable for yield and quality across seasons and ratoons is essential. The coastal long cycle produced G>GxL>GxLxC>GxC for sucrose content and GxL=GxLxC>G=GxC for cane and sugar yield. Testing across locations was most important in the coastal long cycle, a reflection of the complexity of these programmes. Lately, concerns with less ideal harvest crop ages at some sites for these programmes may be producing these trends. Trial observations indicate that some trial sites could be harvested well before the recommended 18 months. Studies to determine the optimum harvest ages at these sites are ongoing. The incidence of the borer Eldana saccharina Walker (Lepidoptera: Pyralidae) (eldana) coupled with harvesting old and lodged crops at some of the sites could also be producing these trends. The trends also indicate the confounding effect of sub-optimum harvest ages and their impact on selecting high yield genotypes. For yield traits, it appears that testing and selecting for ratooning ability was as important as selecting high yield genotypes. The order of importance of variance components for yield and sucrose in the Midlands was G>GxC=GxLxC>GxL, indicating the importance for testing and selecting for ratooning ability. The important GxLxC indicated that the location effects appeared to influence ratooning ability of the genotypes. Additionally, the less important GxL highlights the need to evaluate the representativeness of current trial sites. Generally, selecting for ratooning ability was more important for the rainfed than the irrigated areas. Selecting for locationspecific varieties appeared more important for irrigated than for rainfed areas. Generally, yield produced larger and significant GxLxC, indicating the complexity of selecting for yield traits. Yields traits are known to be controlled by several quantitative genes that have small additive effects. Because of the several small additive genes, the effect of the environment is cumulatively larger on yield traits, resulting in complex GxE effects. Greater precision in testing and data analysis, including statistical methods suggested by Zhou and Kimbeng (2010), would improve precision and selection for yield. The coastal programmes generally produced much larger and more complex GxE components than the irrigated and midlands programmes. Coastal programmes also produced relatively larger error variance compared to other programmes, indicating that there was large variability that was not accounted for by the model. The data were more variable with coefficient of variation ranging from 12 to 24 compared to 6 to 15 irrigated and Midlands. The coastal programmes research stations are located in variable and hilly terrain leading to inherent soil variability. Such variability appears to manifest in the data. In addition, eldana is endemic in the coast, an additional factor that would impact on yield and 372

Zhou M et al


its variability. The effect of harvest age in the coast is further exacerbated by eldana damage, which increases with crop age. Therefore there is a need to properly map the variability that exists in the trial sites and use that knowledge to optimise experimental design for these regions. Generally, the error variance was the largest variance component. This trend indicated that the statistical model was not adequately accounting for the variability. Furthermore, concerns with experimental designs on some sites could also be causing the high error variances. Further studies to improve the designs particularly on research sites located in hilly terrain would reduce the error variance. Resource allocation focused on determining the optimum number of replications as described by Kimbeng et al. (2009) would also help reduce the error variances and thus increase the precision of the trials. Conclusions Genotype by location was more important for the irrigated and coastal long cycle than coastal short cycle and Midlands, indicating the importance of identifying and characterising sites used for testing varieties and validating their representativeness. Genotype by cropyear was more important for rainfed programmes than irrigated, indicating that breeding and selecting for ratooning ability was more essential for the rainfed regions. Genotype by location by crop-year was more important for yield than sucrose content, highlighting the complexity associated with breeding and selecting for yield. The variance components showed that the coastal long cycle and hinterland programmes were the most complex and were generally characterised by larger GxE. Generally, programmes with crop cycles greater than 12 months produced more complex variance components, indicating the difficulty of making significant genetic gains. The possible contribution of eldana and the harvest ages in the coastal programmes may be contributing to these trends. REFERENCES Anon (2003). Plant Breeding Crossing and Selection Programmes. South African Sugar Association Experiment Station. Mount Edgecombe, KwaZulu-Natal, South Africa. 12 pages. Jackson PA and Hogarth DM (1992). Genotype x environment interactions in sugarcane. I. Patterns of response across sites and crop-years in North Queensland. Aust J Agric Res 43: 1447-1459. Jackson PA, Horsley D, Foreman J, Hogarth DM and Wood AW (1991). Genotype x environment (GE) interactions in sugarcane variety trials in the Herbert. Proc Aust Soc Sug Cane Technol 13: 103-109. Kang MS (2002). Genotype-environment interaction: Progress and prospects. In: MS Kang (Ed) Quantitative Genetics, Genomics and Plant Breeding. CAB International, New York, USA. Kang MS and Miller JD (1984). Genotype x environment interactions for cane and sugar yield and their implications in sugarcane breeding. Crop Science 24: 435-440. Kang MS, Miller JD, Tai PYP, Dean JD and Glaz B (1987). Implications of confounding of genotype x year and genotype x crop effects in sugarcane. Field Crops Res 15: 349-355. Kimbeng CA, Rattey AR and Hetherington M (2002). Interpretation and implications of genotype by environment interactions in advanced stage sugarcane selection trials in central Queensland. Aust J Agric Res 53(1): 35-1045. Kimbeng CA, Zhou MM and da Silva JA (2009). Genotype x environment interactions and resource allocation in sugarcane yield trials in the Rio Grande valley region of Texas. J Am Soc Sug Cane Technol 29: 11-24. 373

Zhou M et al


Littell RC, Milliken GA, Stroup WW, Wolfinger RD and Schabenberger O (2008). SAS for Mixed Models. Second Edition. Cary, NC, USA: SAS Institute Inc. Milligan SB, Gravois KA, Bischoff KP and Martin FA (1990). Crop effects on broad sense repeatabilities and genetic variances of sugarcane yield components. Crop Science 30: 344-349. Mirzawan PDN, Cooper M and Hogarth DM (1993). The impact of genotype x environment interactions for sugar yield on the use of indirect selection in southern Queensland. Aust J Exp Agric 33: 629-638. Mirzawan PDN, Cooper M, DeLacy IH and Hogarth DM (1994). Retrospective analysis of the relationship among test environments of the southern Queensland sugarcane breeding programme. Theor Appl Gen 88: 707-716. Nuss KJ (1998). Aspects considered in the search for new farms for the Experiment Station. Proc S Afr Sug Technol Ass 72: 42-45. Parfitt RC (2000). Genotype x Environment interaction among secondary variety trials in the Northern Region of the South African sugar industry. Proc S Afr Sug Technol Ass 74: 245-248. Parfitt RC (2005). Release of sugarcane varieties in South Africa. Proc S Afr Sug Technol Ass 79: 63-71. Parfitt RC and Thomas DW (2001). Final stage transfers in a regional breeding and selection programme for sugarcane. Proc S Afr Sug Technol Ass 75: 151-153. Rattey AR and Kimbeng CA (2001). Genotype by environment interactions and resource allocation in final stage selection trials in the Burdekin district. Pro Aust Soc Sug Cane Technol 23: 136141. Rea R and de Sousa-Vieira O (2002). Genotype x environment interactions in sugarcane yield trials in the Central Western region of Venezuela. Interciencia 27: 620-624. SAS Version 9.2 (2009). SAS for Windows, Version 9.2. Cary, NC, USA. Zhou MM and Kimbeng CA (2010). Multivariate repeated measures: a statistical approach for analysing data derived from sugarcane breeding trials. Proc S Afr Sug Technol Ass 83: 92-105.

374