COMPONENTS OF GENOTYPE BY ENVIRONMENT INTERACTION AMONG SASRI REGIONAL BREEDING AND SELECTION PROGRAMMES AND THEIR IMPLICATIONS

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1 Zhou et al roc S Afr Sug Technol Ass (2011) 84: REFEREED AER CETS F ETYE BY EVRET TERACT A SASR REA BREED AD SEECT RRAES AD TER CATS ZU, S S, ARTZ T AD BERSTE South African Sugar Sugarcane Research nstitute, rivate Bag X02, ount Edgecombe, 40, South Africa arvellous.zhou@sugar.org.za Abstract enotype by environment interaction (xe) 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 nstitute (SASR) 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 xe 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 idlands 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. enotype 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 xe. rogrammes with crop cycles of more than 12 months produced more complex variance components indicating the difficulty of making genetic gains. eywords: genotype by environment interaction, variance components, rainfed, irrigated ntroduction n sugarcane breeding programmes worldwide, genotype x environment interaction (xe) is known to influence the selection of superior genotypes in trials (ang and iller, 1984; illigan et al., 1990; irzawan et al., 1993, 1994; ackson et al., 1991; imbeng et al., 2002, 2009). The presence of xe complicates selection decisions because the performance of the elite genotypes becomes conditional on the local environment where the variety is planted (Rattey and imbeng, 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 (ang, 2002). These rank changes confound the determination of the overall true genetic value of the prospective varieties (imbeng et al., 2009). 363

2 Zhou et al roc S Afr Sug Technol Ass (2011) 84: When xe 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 (imbeng et al., 2009). Because sugarcane is a perennial crop, these environments are made up of locations and crop stages (plant, first, second ratoon crops). ocations may also be chosen to represent different soil types in addition to agro-climatic conditions (uss, 1998). ost sugarcane breeding programmes plant advanced stage variety trials at several locations and harvest these trials over several years. n 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 (ang et al., 1987). Studies on xe provide guidance in developing strategies for testing and selecting genotypes best adapted to the targeted environments (Rea and de Sousa-Vieira, 2002). revious studies in Queensland, Australia (ackson et al., 1991; ackson and ogarth, 1992; Rattey and imbeng, 2001; imbeng et al., 2002), ouisiana (illigan et al., 1990) and Texas in the USA (imbeng et al., 2009) suggested that results from xe are not universal. The South African Sugarcane Research nstitute (SASR) operates seven regional breeding and selection programmes (Table 1) that were established to develop varieties for their respective agro-ecological regions (uss, 1998; arfitt, 2000, 2005; arfitt 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 (uss, 1998), there has been no study on xe (except for arfitt, 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 xe. 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). rogramme Research station Agro-climatic regions arvest age F ongola orthern rrigated areas 12 months T Empangeni Coastal high otential 12 months U ingindlovu Coastal average potential months ingindlovu Coastal average potential 18 months earsney Coastal hinterland 18 months B Bruyns ill idlands umic soils months S lenside idlands Sandy soils months The objectives of this study were to determine whether xe was present for sugar yield and its components (cane yield and sucrose content) in the secondary variety trials and, where xe was present, to determine the statistical significance, relative importance and implications of the variance components of xe. 364

3 Zhou et al roc S Afr Sug Technol Ass (2011) 84: aterials and ethods 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 ongola research station, respectively (arfitt, 2000). V2 represented a late planted off-station trial in the ongola area. TV2 represented a late planted trial at hlati in the alelane area, while V represented an early planted trial at omatidraai in omatipoort. The alelane and omati areas are in the pumalanga province of South Africa and represent the owveld 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 ingindlovu research station while U1V was planted off-station. V was planted on-station while 1V and 2V were planted off-station for the ingindlovu area. V was planted on-station at earsney research station while 1V was planted off-station for the earsney area. For the idlands programmes, BV was planted on-station at Bruyns ill research station while B1V and B2V were planted off-station. SV was planted on-station at lenside 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: Y ijkl = µ + l + R() k(l) + i + il + R() ik(l) + C j + C jl + CR() jk(l) + C ij + C ijl + E ijkl Equation 1 where, Y ijkl = observation for genotype i, in crop-year j, in replication k nested within location l; µ = overall mean; l = the random effect of the l th location; R() k(l) = the random effect of the k th replication nested within the l th location and was the error term for the location effects; i = the random effect of the i th genotype; il = the random interaction effect between the ith genotype and the lth location; R() ik(l) = the random interaction effect between the i th genotype and the k th replication nested with the l th location and was the error term for the genotype and genotype by location interaction effect; C j = the random effect of the j th crop-year; C jl = the random interaction effect between the l th location and the j th crop-year; CR() jk(l) = random interaction effect between the j th crop-year and the k th replication nested within the l th location and was the error term for the crop-year and location by crop-year interaction effect; C ij = random interaction effect between the i th genotype and the j th crop-year; C ijl = random interaction effect between the i th genotype, l th location and j th crop year; and E ijkl = residual error. The genotypes, although selected, were taken in this study to represent a random sample of several possible advanced stage genotypes in the SASR sugarcane breeding programme. Similarly, although the location were chosen to represent sugarcane production environments differing in soil 365

4 Zhou et al roc S Afr Sug Technol Ass (2011) 84: 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 enotypes Crops Trial sites ocation rrigated F Q , 3R, 3R, 3R, 3R, 3R, 3R, 3R, 3R, 3R, 3R, 1R FV, FV2, V2 FV, FV2, V2 FV, FV2, V2, TV2 FV, FV2, V2, TV2 FV, FV2, V2, V, TV2 FV, FV2, V2, V, TV2 FV, FV2, V2, V, TV2 FV, FV2, V2, V, TV2 FV, FV2, V2, V, TV2 FV, FV2, V2, V, TV2 FV, FV2, V2, V, TV2 FV, FV2, V2, V, TV2 ongola ongola ongola, alelane ongola, alelane ongola, omati, alelane ongola, omati, alelane ongola, omati, alelane ongola, omati, alelane ongola, omati, alelane ongola, omati, alelane ongola, omati, alelane ongola, omati, alelane Q , 1R, 1R, 1R, 1R, 1R Coastal short cycle TV, UV TV, UV TV, T1V, UV Coastal long cycle and hinterland V, V V, 1V, 2V, V, 1V V, 1V, 2V, V, 1V V, 1V, 2V, V, 1V V, 1V, 2V, V, 1V V, 1V, 2V, V, 1V V, 1V, 2V, V, 1V V, 1V, 2V, V, 1V V, 1V, 2V, V, 1V V, 1V, 2V, V, 1V idlands BV, SV BV, B1V, SV, S1V, S2V BV, B1V, SV, S1V, S2V BV, B1V, SV, S1V, S2V BV, B1V, SV, S1V, S2V BV, B1V, B2V, SV, S1V, S2V BV, B1V, B2V, SV, S1V, S2V BV, B1V, B2V, SV, S1V, S2V BV, B1V, B2V, SV, S1V, S2V Empangeni, ingindlovu Empangeni, ingindlovu Empangeni, ingindlovu Empangeni, ingindlovu Empangeni, ingindlovu Empangeni, ingindlovu Empangeni, ingindlovu Empangeni, ingindlovu Empangeni, ingindlovu Empangeni, ingindlovu Empangeni, ingindlovu ingindlovu, earsney ingindlovu, earsney ingindlovu, earsney ingindlovu, earsney ingindlovu, earsney ingindlovu, earsney ingindlovu, earsney ingindlovu, earsney ingindlovu, earsney ingindlovu, earsney Bruyns ill, lenside Bruyns ill, lenside Bruyns ill, lenside Bruyns ill, lenside Bruyns ill, lenside Bruyns ill, lenside Bruyns ill, lenside Bruyns ill, lenside Bruyns ill, lenside The data were analysed using the mixed procedure of SAS (SAS nstitute, 2009). The estimates of variance components, their standard errors and their significant tests were done using the CVTEST option in the model statement (ittell et al., 2008). 366

5 Zhou et al roc S Afr Sug Technol Ass (2011) 84: Results Variance components The irrigated region genotype effect variance component () was significant (<0.05) for sucrose content, cane and sugar yield (Table 3). The for sucrose content produced larger values for,,,,, Q series than for F,,,,,. The genotype x location interaction effect variance components (x) were significant (<0.05) in 10 out of 12 series for cane yield, 11 out of 12 for sucrose content and 8 out of 12 for sugar yield. The x for sucrose content showed higher values in,,, Q. Five out of 12 series for cane yield and 7 out of 12 Table 3. Variance components (± standard errors) for cane yield, sucrose content and sugar yield for the irrigated region. Series x xc xxc Error Cane yield (t/ha) F Q 53.45±21.07** 45.93±15.80** 57.60±15.42** 37.53±15.29** 83.06±23.80** 37.16±12.01** 44.22±13.66** 54.93±16.14** 49.26±15.17** 45.74±14.28** 15.42±8.76* 82.08±23.36** 19.79±8.83* 4.22± ±7.23*.95±10.26** 20.59±6.91** 22.85±5.96** 13.19±5.67** 10.80±5.23* 17.06±8.52* 22.39±8.75** 21.96±10.53* 14.73± ±8.27** 5.± ± ±5.10** 13.36±3.63** 11.61±3.08** 9.39±2.80** 3.99±2.24* 1.41± ± ± ± ±8.12* 25.64±7.07** 5.19± ±4.40** 8.04±3.68* 15.58±3.70** 22.56±3.89** 17.02±4.10** 7.55±4.00* 17.32±7.77* 5.18± ±8.43** ±9.39** ±6.52** ±7.56** ±5.67** ±4.97** 97.59±4.33** 86.27±3.82** 93.65±4.64** ±5.54** ±9.60** 95.83±12.20** F Q F Q 0.58±0.18** 0.46±0.13** 0.44±0.10** 0.25±0.07** 0.49±0.14** 0.51±0.14** 0.85±0.22** 0.68±0.18** 0.68±0.18** 0.41±0.12** 0.51±0.14** 0.99±0.** 0.53±0.29* 0.65±0.27** 0.83±0.26** 0.60±0.26** 1.±0.40** 0.56±0.20** 1.29±0.37** 0.83±0.25** 0.87±0.26** 0.72±0.24** 0.58±0.23** 0.84±0.43* Sucrose content (% cane) 0.02± ± ± ± ± ± ± ± ± ±0.06** 0.09±0.04* 0.04± ±0.02** 0.14±0.04** 0.14±0.04** 0.12±0.03** 0.05±0.03* 0.24±0.05** 0.26±0.06** 0.18±0.06** 0.24±0.14* 0.60±0.22** 0.24± ± ±0.20** 0.70±0.18** 0.48±0.13** 0.37±0.11** 0.28±0.11** 0.46±0.15** 0.65±0.17** 0.± ±0.47 Sugar yield (t/ha) 0.44±0.20* 0.11± ± ±0.11** 0.28±0.09** 0.18±0.07** 0.13±0.06* 0.08±0.05* 0.12±0.05** 0.12± ± ±0.05** 0.08±0.03** 0.09±0.04** 0.05± ±0.04** 0.09±0.03** 0.13±0.03** 0.05±0.03* 0.13±0.05** 0.02± ±0.18* 0.34±0.18* 0.77±0.20** 0.14± ±0.11* 0.36±0.10** 0.41±0.10** 0.32±0.08** 0.34±0.08** 0.04± ±0.22** 0.60±0.50 = genotype; x = genotype x location interaction; xc = genotype x crop-year interaction; xxc = genotype x location x crop-year interaction. * = significant at <0.05, ** = significant at < ±0.05** 0.88±0.05** 0.85±0.05** 0.90±0.04** 1.08±0.05** 0.95±0.04** 1.02±0.04** 0.93±0.04** 1.07±0.05** 1.06±0.05** 1.12±0.08** 1.03±0.12** 3.15±0.19** 3.73±0.22** 3.38±0.18** 3.46±0.20** 3.33±0.15** 2.65±0.12** 2.53±0.11** 2.15±0.10** 2.23±0.11** 2.49±0.13** 3.44±0.25** 2.44±0.32** 367

6 Zhou et al roc S Afr Sug Technol Ass (2011) 84: for sugar yield produced significant (<0.05) genotype x crop-year interaction effect variance components (xc). The xc for,,,, Q were lower than those for F,,,,, for cane yield and,,, for sugar yield were lower than those for F,,,,,. All series produced non-significant (>0.05) xc for sucrose content. ine out of 12 for cane yield, 8 out of 12 for sucrose content and 9 out of 12 for sugar yield produced significant (<0.05) genotype x location x crop-year interaction effect variance components (xxc). Based on number of series that produced significant effects, >x>xxc>xc was the order of importance for cane yield, sucrose content and sugar yield. The error term variance component for cane and sugar yield was larger in F,,,, series than,,,,,, Q. The opposite trend was apparent for sucrose content. The error term variance components were generally the largest. The coastal region short cycle was significant (<0.05) for all series for cane yield, sucrose content and sugar yield (Table 4). Five out of 10 for cane yield, 6 out of 10 for sucrose content and 3 out of 10 for sugar yield produced significant (<0.05) x. The significant x for yield were largely from,,, and were generally larger than those for,,,,. Six out of 10 series for cane and sugar yield, and 7 out of 10 for sucrose content produced significant xc. The significant xc for yield were from,,,,, and were larger than for,,,. Furthermore, xc for yield increased from to. Eight out of 10 for cane yield, 4 out of 10 for sucrose content and 6 out of 10 for sugar yield produced significant (<0.05) xxc. Based on number of series producing significant values, cane yield > sugar yield > sucrose content was the order of xxc. The order of importance across all series was >xxc>xc>x for cane and sugar yield, and >xc>x>xxc for sucrose content. The variance component for the error term for cane and sugar yield for,, was larger than that for,,,,,,. The error term variance component was generally the largest. The coastal long cycle and hinterland regions produced significant (<0.05) across all series for cane yield and sucrose content while sugar yield was significant in 6 out of 9 (Table 5). Eight out of 9 series produced significant (<0.05) x for sucrose content and sugar yield while cane yield produced significant effects in 7 out of 9. Seven out of 9 for cane yield, 6 out of 9 for sugar yield and 5 out of 9 for sucrose content produced significant (<0.05) xc. Seven out of 9 for cane yield and sucrose content, and 8 out of 9 for sugar yield produced significant (<0.05) xxc. For cane yield, produced the largest number of significant series while x, xc and xxc were similar. The order of importance was >x>xxc>xc for sucrose content and x=xxc>=xc for sugar yield. The from to were all significant (<0.05). The error produced the largest variance components. 368

7 Zhou et al roc S Afr Sug Technol Ass (2011) 84: Table 4. Variance components (± standard errors) for cane yield, sucrose content and sugar yield for the coastal region short cycle (12 months crop age). Series x xc xxc Error Cane yield (t/ha) 75.08±24.42** 38.27±14.82** 32.65±10.72** 29.53±10.45** 68.46±19.75** 36.61±11.** 21.52±7.88** 34.61±10.09** 32.78±12.69**.52±13.41** 4.17± ± ± ±4.51* 5.05± ±4.21** 22.02±5.92** 23.47±7.73** 14.87±7.48* 5.47± ± ± ± ±2.24** 5.99±2.21** 6.01±2.23** 15.67±3.80** 8.92±2.70** 14.15±5.92** 24.07±8.89** 6.48±2.82** 3.64± ±2.45** 6.03±2.40** 6.90±2.56** 8.90±2.06** 6.93±2.50** 14.67±5.11** 93.56±7.47** 68.69±6.32** 66.53±3.78** 48.59±3.40** 32.91±2.09** 42.68±2.80** 47.13±3.00** 28.29±1.86** 43.45±2.82** 53.97±4.75** 0.56±0.21** 0.72±0.24** 0.39±0.12** 0.45±0.17** 0.41±0.14** 0.47±0.14** 0.44±0.14** 0.49±0.15** 0.53±0.16** 0.36±0.11** 0.75±0.32** 0.80±0.34** 0.50±0.18** 0.42±0.17** 0.65±0.21** 0.44±0.15** 0.39±0.14** 0.23±0.13* 0.42±0.18** 0.37±0.19* 0.±0.11** 0.19±0.10* 0.03± ± ±0.05** 0.07±0.04* 0.12±0.04** 0.04± ±0.03** 0.12± ± ± ± ± ± ±0.05* 0.33±0.10** 0.36±0.12** 0.22±0.15 Sucrose content (t/ha) 0.06± ±0.04** 0.15±0.06** 0.10±0.04** 0.10±0.04** 0.14±0.04** 0.09±0.04* 0.13±0.04** 0.01±0.03 Sugar yield (t/ha) 0.13± ± ±0.06* 0.07± ±0.05* 0.09±0.04* 0.15±0.05** 0.±0.08** 0.19±0.06** 0.17±0.10* 0.07± ±0.06* 0.07± ± ±0.06* 0.03± ± ±0.06** 0.13±0.05** 0.04± ±0.18** 0.14±0.06* 0.10± ±0.07** 0.08± ±0.05* 0.21±0.05** 0.15±0.05** 0.37±0.13** = genotype; x = genotype x location interaction; xc = genotype x crop-year interaction; xxc = genotype x location x crop-year interaction. * = significant at <0.05, ** = significant at < ±0.06** 0.90±0.08** 1.05±0.05** 1.11±0.08** 1.13±0.07** 1.12±0.07** 0.87±0.05** 0.96±0.06** 0.86±0.05** 0.82±0.07** 1.76±0.15** 1.55±0.14** 1.42±0.08** 1.15±0.08** 0.84±0.05** 1.12±0.07** 0.96±0.06* 0.73±0.05** 0.94±0.06** 1.±0.11** 369

8 Zhou et al roc S Afr Sug Technol Ass (2011) 84: Table 5. Variance components (± standard errors) for cane yield, sucrose content and sugar yield for the coastal long cycle and hinterland (18 months crop age). Series x xc xxc Error Cane yield (t/ha) 46.42±17.36** 1.67± ±11.27** 1.97±10.82** 24.57±13.08* 61.03±21.98** 37.23±20.18* 46.32±18.90** 35.27±13.68** 18.36±9.39* 51.60±18.18** 48.04±17.89** 38.40±13.10** 60.49±12.96** ±23.83** 62.23±14.59** 26.69±9.10** 14.97± ±8.54* 33.58±11.10** 15.95±6.09** 11.94±4.90** 10.±4.** 0.04± ±4.49** 8.61±3.46** 18.63±6.34** 10.±4.96* 7.± ±6.77** 33.05±6.00** 34.82±8.34** 25.63±6.22** 6.52± ±5.60** 10.90±6.26* ±9.44** ±6.76** 85.90±5.00** ±7.76** ±6.38** ±5.87** 87.73±5.78** 99.54±8.01** 0.29±0.16* 0.35±0.13** 0.33±0.11** 0.90±0.28** 0.41±0.14** 0.41±0.13** 0.69±0.20** 0.24±0.09** 0.41±0.14** 0.29± ±0.24* 0.37±0.22* 0.17± ± ±0.15* 0.45±0.18** 0.82±0.** 0.64±0.28** 0.19±0.10* 0.14±0.06** 0.13±0.05** 0.±0.10** 0.25±0.07** 0.14±0.05** 0.12±0.04** 0.12±0.05** 0.03± ± ±0.21** 1.±0.26** 2.20±0.38** 1.43±0.** 0.46±0.14** 0.28±0.15* 0.40±0.17** 0.49±0.19** Sucrose content (% cane) 0.±0.10** 0.13±0.06** 0.03± ±0.05* 0.09±0.04* 0.06±0.04* 0.05±0.03* 0.12±0.04** 0.12±0.06* Sugar yield (t/ha) 0.14± ±0.13** 0.15±0.08* 0.04± ±0.09* 0.12±0.06* 0.06± ±0.11** 0.29±0.14* 0.10± ±0.07** 0.21±0.07** 0.39±0.09** 0.16±0.06** 0.18±0.06** 0.11±0.04** 0.13±0.05** 0.12±0.07* 0.67±0.28** 0.23±0.13* 0.61±0.13** 0.27±0.09** 0.38±0.13** 0.29±0.11** 0.41±0.11** 0.56±0.13** 0.18±0.17 = genotype; x = genotype x location interaction; xc = genotype x crop-year interaction; xxc = genotype x location x crop-year interaction. * = significant at <0.05, ** = significant at < ±0.09** 1.37±0.07** 1.42±0.08** 1.41±0.08** 1.00±0.07** 1.26±0.07** 0.95±0.05** 0.87±0.06** 1.04±0.08** 2.28±0.19** 2.80±0.16** 2.17±0.13** 1.79±0.10** 2.±0.15** 2.28± ±0.10** 1.81±0.12** 2.95±0.24** The was significant (<0.05) across all series for cane yield, sucrose content and sugar yield (Table 6) for the idlands. Five out of 8 series for cane yield, 2 out of 8 for sucrose content and 4 out of 8 for sugar yield produced significant (<0.05) x. x was the most important for cane yield followed by sugar yield and sucrose content. All series produced significant (<0.05) xc for cane yield while 6 out of 8 for sucrose content and 7 out of eight for sugar yield. The xc was most important for cane yield followed by sugar yield and sucrose content. Six out of 8 series for cane yield and sucrose content, and 7 out of 8 for sugar yield produced significant xxc. The order of importance of xxc was sugar yield followed equally by cane yield and sucrose content. enerally, the order of importance of the variance components was >xc=xxc>x for yield and sucrose content. enerally, the error produced the largest variance components. 370

9 Zhou et al roc S Afr Sug Technol Ass (2011) 84: Table 6. Variance components (± standard errors) for cane yield, sucrose content and sugar yield for the idlands region. Series x xc xxc Error Cane yield (t/ha) ±40.60** 90.15±34.39** 72.09±23.68** 97.74±29.38** ±44.18** 90.±27.13** 73.14±25.01** 72.99±25.09** 24.78± ±19.83** 12.± ±9.19** 39.46±11.50** 20.89±8.43** 46.79±12.48** 3.28± ±7.37* 12.67±5.09** 13.66±5.91** 11.09±4.13** 34.85±9.76** 14.17±4.55** 12.41±6.27* 20.38±8.25** 15.84±7.73* 5.24± ±6.49** 6.18± ±7.51** 24.12±5.10** 37.41±8.57** 19.87±8.32** 79.94±6.84** ±10.07** 92.43±5.93** ±8.03** ±6.21** 97.35±5.23** ±7.71** ±10.16** 0.42±0.14** 0.21±0.08** 0.19±0.07** 0.34±0.10** 0.44±0.13** 0.25±0.08** 0.40±0.12** 0.29±0.08** 1.28±0.50** 1.±0.55** 1.04±0.38** 1.07±0.38** 1.78±0.58** 0.94±0.32** 1.28±0.45** 0.57±0.28* Sucrose content (% cane) 0.13±0.07* 0.10±0.03** 0.02± ±0.02* 0.08±0.03** 0.10±0.03** 0.06±0.03* 0.02± ±0.03* 0.02± ± ±0.03* 0.02± ± ± ±0.36** 0.19± ±0.19** 0.68±0.22** 0.23± ±0.24** 0.12±0.22 Sugar yield (t/ha) 0.28± ±0.14** 0.32±0.14* 0.36±0.11** 0.54±0.18** 0.40±0.11** 0.27±0.14* 0.39±0.17** 0.16±0.06** 0.11±0.03** 0.11±0.05** 0.02± ±0.03** 0.11±0.05* 0.06±0.03* 0.54±0.19** 0.39±0.16** 0.63±0.16** 0.12± ±0.18** 0.46±0.11** 0.84±0.21** 0.51±0.17** = genotype; x = genotype x location interaction; xc = genotype x crop-year interaction; xxc = genotype x location x crop-year interaction. * = significant at <0.05, ** = significant at < ±0.06** 0.69±0.04** 1.00±0.07** 0.74±0.04** 1.18±0.06** 0.74±0.04** 1.04±0.06** 0.52±0.04** 1.45±0.12** 3.51±0.20** 2.39±0.15** 3.02±0.17** 2.55±0.15** 2.09±0.11** 3.15±0.20** 2.68±0.20** Discussion Sugarcane breeding exploits the variability among breeding populations to identify high yielding genotypes. The xe as represented by x, xc and xxc determine genotypes yield as influenced by locations and ratooning ability (ang and iller, 1984; illigan et al., 1990; irzawan et al., 1993, 1994; ackson et al., 1991; imbeng et al., 2002, 2009). enerally high genotype main effect variance components provide ideal opportunity to identify genotypes that excel in traits of interest. Furthermore, the ability to evaluate the changes in the magnitudes of the variance component parameters over time and selection series can pinpoint the strength and weaknesses of breeding programmes. The irrigated programme showed that effect was dominant, indicating high levels of stability among our genotype populations. This is ideal for making progress during selection of superior genotypes. >x>xxc>xc was the order of importance where xc was non-significant for sucrose content. The non-significant xc indicated that sucrose content of genotypes was not significantly influenced by ratooning ability. The x and xxc were more important than xc, further indicating that testing across locations was more important than testing for ratooning ability. Rankings of the components of xe confirmed that x followed by xxc were the most important, further highlighting that ratooning 371

10 Zhou et al roc S Afr Sug Technol Ass (2011) 84: ability was generally less important. The results indicated the importance of testing genotypes in more locations rather than for more ratoons as is currently practiced in order to maintain the high levels of genotype stability and wide adaptability. The coastal short cycle programme showed that >xxc>xc>x was the order of importance of variance components for cane and sugar yield and >xc>x>xxc for sucrose content. For yield, testing and selecting across locations appeared less important for the coastal short cycle than ratooning ability (xc). The high xxc indicated the complex nature of xe. t 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 xc 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 >x>xxc>xc for sucrose content and x=xxc>=xc for cane and sugar yield. Testing across locations was most important in the coastal long cycle, a reflection of the complexity of these programmes. ately, 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 (epidoptera: yralidae) (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 idlands was >xc=xxc>x, indicating the importance for testing and selecting for ratooning ability. The important xxc indicated that the location effects appeared to influence ratooning ability of the genotypes. Additionally, the less important x highlights the need to evaluate the representativeness of current trial sites. enerally, 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. enerally, yield produced larger and significant xxc, 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 xe effects. reater precision in testing and data analysis, including statistical methods suggested by Zhou and imbeng (2010), would improve precision and selection for yield. The coastal programmes generally produced much larger and more complex xe 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 idlands. 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. n addition, eldana is endemic in the coast, an additional factor that would impact on yield and 372

11 Zhou et al roc S Afr Sug Technol Ass (2011) 84: 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. enerally, 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 imbeng et al. (2009) would also help reduce the error variances and thus increase the precision of the trials. Conclusions enotype by location was more important for the irrigated and coastal long cycle than coastal short cycle and idlands, indicating the importance of identifying and characterising sites used for testing varieties and validating their representativeness. enotype 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. enotype 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 xe. enerally, 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. REFERECES Anon (2003). lant Breeding Crossing and Selection rogrammes. South African Sugar Association Experiment Station. ount Edgecombe, wazulu-atal, South Africa. 12 pages. ackson A and ogarth D (1992). enotype x environment interactions in sugarcane.. atterns of response across sites and crop-years in orth Queensland. Aust Agric Res 43: ackson A, orsley D, Foreman, ogarth D and Wood AW (1991). enotype x environment (E) interactions in sugarcane variety trials in the erbert. roc Aust Soc Sug Cane Technol 13: ang S (2002). enotype-environment interaction: rogress and prospects. n: S ang (Ed) Quantitative enetics, enomics and lant Breeding. CAB nternational, ew York, USA. ang S and iller D (1984). enotype x environment interactions for cane and sugar yield and their implications in sugarcane breeding. Crop Science 24: ang S, iller D, Tai Y, Dean D and laz B (1987). mplications of confounding of genotype x year and genotype x crop effects in sugarcane. Field Crops Res 15: imbeng CA, Rattey AR and etherington (2002). nterpretation and implications of genotype by environment interactions in advanced stage sugarcane selection trials in central Queensland. Aust Agric Res 53(1): imbeng CA, Zhou and da Silva A (2009). enotype x environment interactions and resource allocation in sugarcane yield trials in the Rio rande valley region of Texas. Am Soc Sug Cane Technol 29:

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