Crop Science Proofing Instructions

Crop Science Proofing Instructions Following are the galley proofs for your Crop Science manuscript. Please review the galley proofs carefully; they will not be read by editorial staff. Review of the word-processing file at the query letter stage should not be considered a substitute for galley proof review. Note that you are being asked to correct errors, not to revise the paper. Extensive alteration may require Editorial Board approval, possibly delaying publication. Reviewing the proofs. Please follow these guidelines for reviewing the proofs: 

Mark your corrections directly on the galley proofs. Make sure that corrections are noticeable and easy to understand.



Check all type on the galley proofs, including the footers that appear at the bottom of each page, the title and byline, the abbreviations list, and the author–paper documentation paragraph.



If your galley proofs have tables, check the table data against that in your original tables.



If your galley proofs have equations, check them against those in your original manuscript.



If your galley proofs have figures, check to be sure that all type is legible, including any small-print text. If a figure requires alteration, you must provide a PDF, .tiff, or .jpg file of the revised figure via email attachment.

Returning the proofs. If the galley proofs require alterations, you may return the marked galleys using one of the following methods:  Express mail;  Fax. If you choose this option, you must also send an email message that includes a description of each alteration;  Email attachment. If the galley proofs do not require alterations, you may send an email message indicating that no changes are needed.

(more)

COLOR REPRINT COVERS AVAILABLE! For reprint covers, we are now offering high-quality color copies of the journal cover. Your covers will be taken from the issue in which your manuscript is published. Covers include manuscript title, byline, and page numbers. A NOTE REGARDING PDF FILES: If you have a subscription to the online journal, you may download a PDF file of the article free of charge. The free file is intended for personal use only, and should not be distributed as a reprint or posted online. If you want to use a PDF file for reprints, please buy it here. Thank you for your contribution to Crop Science. Sincerely, Elizabeth Gebhardt, Managing Editor Crop Science 5585 Guilford Road Madison, Wisconsin 53711 USA 608-268-4950 (phone), 608-273-2021 (fax), [email protected]

Revised 3/13

Research

Location Effects and Their Implications in Breeding for Sugarcane Yield and Quality in the Midlands Region in South Africa Marvellous M. Zhou* and Eastonce T. Gwata

ABSTRACT The genotype × environment interaction (G×E) influences the values of genetic gains attained by plant breeding programs when breeding materials are evaluated in diverse agro-ecological areas. This study was designed to evaluate a broad spectrum of sugarcane (Saccharum officinarum L.) genotypes for G×E and determine the yield and quality differences in the Midlands sugarcane production region of South Africa. Three of the testing locations consisted of a humic soil type, whereas the remainder was under sandy soils. Data collected from the plant and first and second ratoon crops in randomized complete block trials with three replications were analyzed. The significant (P < 0.001) cane yield and quality [estimable recoverable crystal (ERC) per cane, dry matter (DM) per cane, fiber per cane] differences at locations and the significant (P < 0.01) genotype × location interaction (GL) effects supported the need to continue developing varieties that are specifically adapted for humic and sandy soils. The sandy soil locations were more diverse in their sand, clay, and organic matter content than humic soil locations. The cane yield of genotypes was significantly (P < 0.001) higher and the ERC per cane was significantly lower at locations with humic soils than sandy soils. Therefore, there is merit in breeding for high sucrose content in the humic soil breeding program and focusing on high biomass accumulation in the sandy soils program to increase overall sugar yield.

M. M. Zhou, South African Sugarcane Research Institute, Dep. of Plant Breeding, P. Bag X02, Mount Edgecombe 4300, South Africa and Univ. of the Free State, Dep. of Plant Breeding, P. Bag 339, Bloemfontein 9300, South Africa; and E. T. Gwata, Univ. of the Venda, School of Agriculture, P. Bag X5050, Thohoyandou 0920, South Africa. Received 20 Feb. 2015. Accepted 29 June 2015. *Corresponding author ([email protected]). Abbreviations: BV, Bruyns Hill Research Station; BV1, Conrad Klipp Farm 1; B2V, Conrad Klipp Farm 2; DM, dry matter; ERC, estimable recoverable crystal; G×E, genotype × environment interaction; G×L, genotype × location interaction; G×L×C, genotype × location × cropyear; L×C, location × crop-year interaction; PC1, Principal Component 1; PC2, Principal Component 2; PCA, principal component analysis; SV, Glenside Research Station; S1V, Anton Woerner Farm; S2V, Fred Van Breda Farm.

B

reeding programs conduct multienvironment trials to evaluate the adaptability and performance of genotypes across agro-ecological environments (Yan and Tinker, 2006). Agroecological environments can vary from one region to another as well within regions (Setimela et al., 2005), resulting in G×E. In situations where G×E is large, relatively large genetic gains can be achieved by selecting specific genotypes for each distinct environment. In contrast, where cultivars have broad ecological adaptation, this implies that they are not sensitive to G×E. The influence of G×E on cane yield and sucrose content in sugarcane is large (Kang and Miller, 1984; Kimbeng et al., 2002) and can influence the precision of selection. In addition, the G×E interaction in sugarcane is largely controlled by location, time of planting or harvest, and crop-years (Kimbeng et al., 2009); this is possibly true for South Africa. Locations represent the inherent characteristics of the site such as soil types, as well as prevalent

Published in Crop Sci. 55:1–11 (2015). doi: 10.2135/cropsci2015.02.0101 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved.

crop science, vol. 55, november– december 2015

www.crops.org 1

Reproduced from Crop Science. Published by Crop Science Society of America. All copyrights reserved.

agro-climatic conditions (Ramburan et al., 2012; Zhou et al., 2011). In South Africa, the sugarcane breeding programs for the Midlands region were established primarily to exploit the humic and sandy soils (Nuss, 1998) that are prevalent in the area. Humic soils are deep and well drained, with high clay and organic matter content and therefore rich with nutrients, producing high cane yields. In contrast, the sandy soils have low clay content and organic matter and low water-holding capacity and thus are susceptible to moisture stress resulting in low cane yields (Zhou, 2014). Increasing selection and genetic gains is a key measure of the success of breeding programs. Niche breeding, by definition, allows for the development of varieties with adaptations to specific environments. When significant crossover G×E exists, niche breeding is also recognized as an effective method for increasing selection efficiency and genetic gains, both of which are good indicators of the success of breeding programs (Zhou, 2014). The restructuring of the South Africa Sugar Research Institute breeding programs in the late 1990s was designed to increase both selection efficiency and genetic gains (Nuss, 1998). Urban encroachment resulted in some research stations becoming irrelevant. The restructuring also recognized that some current research stations were not representative of the sugarcane growing conditions (e.g., soil type). Apart from the study by Ramburan et al. (2012), there has been limited analysis of the characteristics and yield potential of the sites used for testing advanced genotypes in the Midlands breeding programs since the restructuring of the breeding programs in 1997 (Nuss, 1998). Therefore, this study was designed to examine the G×E effects and determine the cane and sugar yield, ERC per cane, DM cane, and fiber per cane potential of genotypes at the testing locations that were used for sugarcane breeding in the Midlands region using trials planted and harvested between 2003 and 2010. The implications of the findings are also discussed.

MATERIALS AND METHODS Experimental Materials Data were collected from a series of variety trials planted from 2003 to 2010 and harvested from 2005 to 2014 (Table 1). There were three testing locations in each of the humic [Bruyns Hill Research Station (BV), Conrad Klipp Farm 1 (B1V), Conrad Klipp Farm 2 (B2V)] and sandy [Glenside Research Station (SV), Anton Woerner Farm (S1V), Fred Van Breda Farm (S2V)] soil categories (Table 2). All the locations were situated between 29.3° S, 30.6° S to 29.5°S, 31.8° E (Fig. 1). Data were collected from the plant and first and second ratoon crops. All the trials were harvested at a crop age of 20 to 24 mo.

Experimental Design and Data Collection All the trials were laid out as randomised complete block designs with three replicates. Each plot contained five rows spaced 1.1 m apart, each 8.0 m long. The number of genotypes planted in 2

Table 1. Location, crop-years, year planted, and years harvested for the trials.

†

Locations

Cropyears

Year planted

Years harvested

BV, B1V, B2V, SV, S1V, S2V

P, 1R, 2R

2003

2005, 2007, 2009


P, 1R, 2R

2004

2006, 2008, 2010


P, 1R, 2R

2005

2007, 2009, 2011


P, 1R, 2R

2006

2008, 2010, 2012


P, 1R, 2R

2007

2009, 2011, 2013


P, 1R, 2R

2008

2010, 2012, 2014


P, 1R

2009

2011, 2013


P, 1R

2010

2012, 2014

BV, Bruyns Hill Research Station; B1V, Conrad Klipp Farm 1; B2V, Conrad Klipp Farm 2; SV, Glenside Research Station; S1V, Anton Woerner Farm; S2V, Fred Van Breda Farm; P, plant crop; 1R, first ratoon crop; 2R, second ratoon crop.

Table 2. Description and GPS coordinates of the locations. Location

Description

Altitude

GPS coordinates

BV B1V B2V SV S1V S2V

Bruyns Hill Research Station Conrad Klipp Farm 1 Conrad Klipp Farm 2 Glenside Research Station Anton Woerner Farm Fred Van Breda Farm

1012 m 1051 m 1051 m 997 m 1035 m 1028 m

29.42S, 30.68E 29.35S, 30.65E 29.34S, 30.64E 29.35S, 30.77E 29.38S, 30.63E 29.31S, 30.69E

each trial ranged from 32 to 36 and were planted across all locations in each year. Different genotypes were evaluated in the field trials each year. From each plot, 12 stalks were randomly chosen to provide a sample for estimating sucrose content in the laboratory (Shoonees-Muir et al., 2009). The sucrose content (%) was expressed as ERC using an empirical formula that accounts for losses via bagasse and molasses: ERC = Pol – a – b, where Pol is the total sucrose in a cane stalk, a and b are coefficients for losses via molasses and fiber during processing. Bagasse and molasses are by-products from sugar processing. A sample was dried in an oven (at 60°C) for 24 h to estimate the proportion of DM in the cane as the difference between fresh and dry weight expressed as a percentage of the dry weight. Fiber per cane was estimated from the dried sample by subtracting the total soluble solids. At harvest, all the millable stalks in the plots were hand-harvested and weighed. The cane weight per plot was converted to cane yield (t ha1) by dividing by the plot size. Sugar yield (t ha1) was calculated as the product of cane yield (t ha1) by sucrose content (%).

Data Analysis The data were analyzed using SAS (SAS Institute, 2014) using the following statistical linear mixed model: Yijkl = µ + Ll + R(L)k(l) + Gi + GLil + GR(L)ik(l) + Cj + LCjl + CR(L)jk(l) + GCij + GLCijl + Eijkl,

www.crops.org

[1]


where Yijkl is the observation for genotype i in crop-year j in replicate k nested within location l; µ is the overall mean; Ll is the fixed effect of the lth location; R(L)k(l) is the random effect of the kth replicate nested within the lth location and was the error term for the location effects; Gi is the fixed effect of the ith genotype; GLil is the fixed interaction effect between the ith genotype and the lth location; GR(L)ik(l) is the random interaction effect between the ith genotype and the kth replicate nested with the lth location and was the error term for the genotype and G×L effects; Cj is the fixed effect of the jth crop-year; LCjl is the fixed interaction effect between the lth location and the jth crop-year; GCij is the fixed interaction effect between the ith genotype and the jth crop-year; GLCijl is the fixed interaction effect among the ith genotype, lth location, and jth crop year; and Eijkl is the residual error. The six locations were considered to be fixed because they represent a specific soil type (either sandy or humic) that was targeted for adaptability during variety development. Genotypes were considered fixed because the purpose of selection was to identify specific varieties for commercial release. Cropyear was considered fixed because ratooning ability is important in South Africa, where sugarcane is grown on hilly terrain and crop replanting is only done once in at least 10 yr to minimize soil erosion. Least-square means for locations were estimated for each series and mean separation was performed using the LSD and contrast statements were used to compare group means for the humic and sandy soils. The least-square means were subjected to principal component analysis (PCA) in SAS.


RESULTS Genotype × Environment Interactions for Yield Traits There were highly significant (P < 0.001) location, genotype, crop-year, location × crop-year interaction (L×C) and genotype × crop-year interaction (G×C) F-values for both cane and sugar yield in all trial series (Table 3). Genotype × location effect F-values for both cane and sugar yield were highly significant for all the years (i.e., growing season) except for 2005. Similarly, genotype × location × crop-year (G×L×C) F-values were highly significant in 2003, 2004, 2005, 2007, and 2008 for both cane and sugar yield. Both cane and sugar yield produced nonsignificant (P > 0.05) G×L×C F-values in 2006, whereas 2009 and 2010 were significant (P < 0.05). In terms of the G×E F-values, G×C produced the largest F-values, followed by G×L, whereas G×L×C produced the lowest. Genotype produced larger F-values than G×E. The R 2 values for cane yield ranged from 0.93 to 0.96; those for sugar yield ranged from 0.89 to 0.95. The CV for cane yield ranged from 9.80 to 14.07; those for sugar yield ranged from 11.76 to 17.08.

Genotype × Environment Interactions for Quality Traits The location, genotype and L×C F-values for DM per cane, ERC per cane, and fiber per cane were highly significant across all series of trials (Table 4). Similarly, the

www.crops.org 3


Figure 1. Spatial distribution of the humic and sandy soil locations. BV, Bruyns Hill Research Station; B1V, Conrad Klipp Farm 1; B2V, Conrad Klipp Farm 2; SV, Glenside Research Station; S1V, Anton Woerner Farm; S2V, Fred Van Breda Farm.

Table 3. Location (L), genotype (G), genotype ×location interaction (G×L), crop-year (C), location × crop-year interaction, (L×C), genotype × crop-year (G×C), genotype × location × crop-year interaction (G×L×C) F-values, coefficient of determination (R2), and CV percentage for cane and sugar yield. Effect

2003

2004

2005

2006

2007

2008

2009

2010

76.46*** 10.68*** 2.54*** 358.86*** 100.34*** 5.32*** 1.50*** 0.95 10.61

59.39*** 13.29*** 1.59*** 281.04*** 168.86*** 2.72*** 1.62*** 0.95 9.80

34.13*** 18.23*** 2.06*** 1557.57*** 40.41*** 3.84*** 1.53*** 0.96 10.71

111.29*** 13.22*** 1.68*** 181.77*** 39.79*** 6.08*** 1.35* 0.95 10.96

Cane yield (t ha )


1

L G G×L C L×C G×C G×L×C R2 CV%

84.90*** 16.42*** 1.81*** 494.08*** 195.49*** 3.47*** 1.50*** 0.95 11.72

285.12*** 14.28*** 2.95*** 182.23*** 78.60*** 2.93*** 1.61*** 0.95 13.10

51.51*** 9.02*** 1.16ns 1426.25*** 51.41*** 5.04*** 1.49*** 0.96 11.58

27.75*** 8.59*** 1.58*** 46.24*** 90.94*** 3.08*** 1.15ns 0.93 14.07

Sugar yield (t ha1) L G G×L C L×C G×C G×L×C R2 CV%

65.82*** 10.63*** 1.57*** 382.12*** 136.16*** 4.25*** 1.53***

436.45*** 12.83*** 2.73*** 76.49*** 83.26*** 2.89*** 1.63***

52.41*** 5.70*** 1.25ns 1559.93*** 21.09*** 4.47*** 1.55***

14.22*** 4.54*** 1.68*** 142.06*** 104.10*** 2.76*** 1.03ns

91.04*** 10.60*** 2.35*** 213.20*** 91.50*** 5.81*** 1.62***

39.19*** 12.60*** 1.83*** 861.89*** 183.03*** 2.76*** 1.61***

47.37*** 13.94*** 2.08*** 1591.25*** 29.82*** 3.77*** 1.48**

52.35*** 7.99*** 1.73*** 463.15*** 32.55*** 5.73*** 1.27*

0.93 13.14

0.93 15.90

0.95 13.36

0.89 17.08

0.93 12.52

0.95 11.76

0.94 13.36

0.91 13.00

*** Significant at the 0.1% probability level. ** Significant at the 1.0% probability level. * Significant at the 5.0% probability level. †

ns, not significant at the 5.0% probability level.

C F-values were highly significant for all traits except for fiber (P < 0.05) during 2010. The G×L effects for ERC per cane produced highly significant in four consecutive series from 2003 and in 2009. The G×L F-values for DM per cane were highly significant in the 2003 to 2008 series but nonsignificant (P > 0.05) in 2010. The G×C F-values for DM per cane and ERC per cane was highly significant across all series , whereas fiber per cane was highly significant in 2003, 2005, 2007, 2008, and 2010 (Table 3). In addition, the G×L×C effect values for fiber per cane was significant (P < 0.05) only in 2007 and 2010. The highest magnitude of the F-value for the G×E was achieved by G×C followed by G×L and G×L×C, respectively. In comparison with DM per cane and ERC per cane, fiber per cane showed the widest R 2 range (0.13) and CV range (2.79). In general, the yield traits produced larger R 2 and CV values than quality traits (Table 3).

Location Least-Square Means for Yield Traits Generally, the humic soil trial sites (BV, B1V, and B2V) produced higher cane and sugar yield (Table 5) than the sandy soil trials (SV, S1V, and S2V). Contrast tests between humic and sandy soil locations showed highly significant differences between the trial site groupings for both cane and sugar yield across all the trial series. Among the humic sois sites, site B1V produced the lowest cane yield (P < 0.05) 4

during 2003, 2004, and 2009. For the sandy sites, S1V produced the lowest cane yield, whereas SV (2003, 2004, 2005, 2008, 2009) and S2V (2005, 2007, 2008, 2009, 2010) produced higher cane yields. For humic soils, sites BV and B2V produced higher cane and sugar yields than site B1V.

Location Least-Square Means for Quality Traits Generally, the humic soil sites produced lower ERC per cane than the sandy soil sites (Table 6). Among the sandy soil sites, S1V produced the highest ERC per cane, whereas S2V produced the lowest. The humic soil sites produced lower DM per cane than the sandy soil sites. Among the sandy soil sites, S1V generally produced higher DM per cane than SV and S2V. The testing locations in humic soil areas produced largely similar values of DM per cane. The testing locations in the sandy soil types produced higher values of fiber per cane than those in the humic soils. Among the sandy soil locations, site S1V produced more fiber per cane than SV and S2V sites. The fiber per cane of the humic soil sites was similar.

Principal Component Analysis for Yield Traits The eigenvectors for cane and sugar yield showed that Principal Component 1 (PC1) was an average of yields across the trial series and all the eigenvectors were positive (Table 7). The dominant eigenvectors for both cane and

www.crops.org


Table 4. Location (L), genotype (G), genotype × location interaction (G×L), crop-year (C), location × crop-year interaction, (L×C), genotype × crop-year (G×C), genotype × location × crop-year interaction (G×L×C) F-values, coefficient of determination (R2) and CV percentage for estimable recoverable crystal (ERC) per cane, dry matter (DM) per cane, and fiber per cane. Effect

2003

2004

2005

2006

2007

2008

2009

2010


72.96*** 17.47*** 1.83*** 27.08*** 20.52*** 3.23*** 1.28** 0.80 6.67

30.84*** 16.39*** 1.67*** 210.59*** 38.33*** 2.09*** 1.24* 0.83 7.81

16.44*** 22.34*** 1.75*** 398.43*** 52.61*** 3.67*** 1.50*** 0.84 6.36

135.89*** 14.32*** 1.66*** 161.20*** 39.25*** 2.44*** 1.21* 0.86 8.30


54.55*** 31.28*** 1.60*** 12.57*** 35.69*** 2.95*** 1.20* 0.85 3.66

39.02*** 13.43*** 1.55*** 275.14*** 55.53*** 2.02*** 1.10ns 0.86 3.99

22.58*** 34.43*** 1.52*** 352.05*** 37.88*** 2.95*** 1.34*** 0.86 3.53

182.77*** 22.87*** 1.53*** 179.09*** 34.20*** 2.45*** 1.16ns 0.91 4.16


26.96*** 62.10*** 1.85*** 385.04*** 41.15*** 2.70*** 1.13ns 0.87 6.30

10.34*** 18.32*** 1.36* 302.16*** 49.15*** 1.25ns 1.04ns 0.83 7.98

15.47*** 33.14*** 1.16ns 154.02*** 20.30*** 3.41*** 1.04ns 0.82 6.08

177.50*** 33.52*** 1.59*** 12.23*** 30.31*** 1.58** 1.02ns 0.89 7.57

73.27*** 28.45*** 1.44** 133.01*** 55.78*** 3.33*** 1.33** 0.87 6.71

134.89*** 15.54*** 1.32* 950.63*** 146.96*** 2.69*** 1.41** 0.93 6.10

13.83*** 19.90*** 1.58*** 296.77*** 75.12*** 4.05*** 1.35* 0.88 6.59

426.68*** 26.01*** 1.30* 352.11*** 82.02*** 4.01*** 1.08ns 0.93 5.51

158.43*** 15.86*** 1.54*** 833.09*** 60.45*** 2.30*** 1.24* 0.92 3.25

44.58*** 23.97*** 1.44** 452.12*** 41.79*** 2.44*** 1.16ns 0.90 3.95

149.93*** 22.67*** 1.17ns 31.67*** 56.85*** 2.88*** 0.99ns 0.91 3.54

46.30*** 20.84*** 1.37** 737.33*** 5.26*** 1.84*** 1.07ns 0.92 6.07

69.61*** 30.34*** 1.23ns 175.80*** 16.66*** 1.64* 1.06ns 0.87 7.89

22.40*** 34.27*** 1.37** 5.62* 29.61*** 2.20*** 1.25* 0.86 6.58

DM per cane 57.71*** 23.63*** 1.52*** 86.46*** 37.83*** 2.65*** 1.44*** 0.88 3.56

Fiber per cane 36.28*** 22.46*** 1.26* 545.15*** 16.65*** 1.72*** 1.18* 0.85 6.73

*** Significant at the 0.1% probability level. ** Significant at the 1.0% probability level. * Significant at the 5.0% probability level. † ns, not significant at the 5.0% probability level.

Table 5. Location least-square means for cane and sugar yield. Location

2003†

2004

2005

2006

2007

2008

2009

2010

123.12a 108.48b 116.33ab 60.01d 64.27d 88.61c

101.48c 111.17ab 116.88a 107.12bc 57.47d 101.08c

124.00a 90.33c 117.73a 102.42b 65.02d 99.14b

144.89a 136.61b 133.67b 90.55d 82.13e 109.30c

12.49b 12.77b 13.98a 14.18a 8.12c 11.91b

14.59a 10.25c 13.07b 12.24b 8.06d 11.21c

Cane yield (t ha ) 1

BV B1V B2V SV S1V S2V

113.29ab 102.72c 114.35a 81.83d 57.75e 63.34e

126.18a 83.21c 118.05b 73.91d 51.00e 52.82e

116.37b 112.48b 130.89a 79.74c 55.39d 79.26c

108.98a 99.88a 111.48a 69.01b 64.58b 75.21b

Sugar yield (t ha1) BV B1V B2V SV S1V S2V

13.82a 12.37b 14.07a 10.90c 7.65d 8.45d

16.4153a 10.2075c 14.8355b 9.9961c 6.7216d 6.7128d

14.02b 14.14b 16.45a 10.06c 7.26d 9.54c

13.26a 11.72b 13.37a 9.81c 9.17c 9.89c

15.14a 12.71b 13.17b 8.47d 7.93d 11.14c

18.96a 16.31b 15.81bc 12.77d 12.16d 15.12c

†

Means followed by the same letter in a column are not significantly different (P < 0.05).

‡

BV, Bruyns Hill Research Station; B1V, Conrad Klipp Farm 1; B2V, Conrad Klipp Farm 2; SV, Glenside Research Station; S1V, Anton Woerner Farm; S2V, Fred Van Breda Farm.


www.crops.org 5


ERC per cane

Table 6. Least-square means of estimable recoverable crystal (ERC) per cane, dry matter (DM) per cane, and fiber per cane. Location

2003†

2004

2005

BV

12.25bc

13.06b

12.02c

B1V

12.09c

12.24d

12.59a

B2V

12.38b

12.54c

SV

13.36a

S1V S2V

2006

2007

2008

2009

2010

12.05c

12.26c

12.10c

11.77b

13.06d

11.73d

11.73d

11.61d

11.31c

11.96e

12.57b

12.01cd

11.41d

12.16c

11.12c

11.83e

13.59a

12.63a

14.33a

14.12a

13.04b

11.94b

14.16b

13.34a

13.17b

12.88a

14.11a

12.39bc

14.27a

12.41a

14.81a

13.34a

12.63c

11.92c

13.22b

12.65b

12.00c

11.18c

13.84c

BV

28.00c

28.33b

27.50bc

26.33e

27.36c

27.52d

27.14b

29.14d

B1V

28.09c

27.52c

27.92b

26.95d

27.09c

27.80c

27.06b

28.21e

B2V

28.28c

28.50b

27.74b

26.83d

27.09c

27.59cd

26.42c

27.78f

SV

29.10b

29.94a

28.96a

29.91b

30.44a

28.46b

27.53b

30.39b

S1V

30.31a

29.70a

28.87a

30.33a

28.50b

30.27a

29.00a

31.37a

S2V

29.46b

28.37b

27.21c

28.01c

28.01b

27.54cd

25.58d

29.92c

BV

12.79c

12.67b

12.39b

10.80e

11.66d

12.08b

12.43b

13.17bc

B1V

12.53c

12.24c

12.34b

11.82c

11.93cd

12.14b

12.35b

13.00cd

B2V

12.56c

12.77b

12.43b

11.28d

11.95cd

12.17b

12.06c

12.82d

SV

12.76c

13.11ab

13.11a

12.92b

13.39a

12.20b

12.37b

13.13bc

S1V

13.73a

13.32a

12.92a

13.49a

12.50b

13.52a

13.23a

13.99a

S2V

13.21b

12.58bc

12.29b

11.44d

12.01c

12.04b

11.04d

13.30b


ERC per cane

DM per cane

Fiber per cane

†

Means followed by the same letter in a column are not significantly different (P < 0.05).

‡

BV, Bruyns Hill Research Station; B1V, Conrad Klipp Farm 1; B2V, Conrad Klipp Farm 2; SV, Glenside Research Station; S1V, Anton Woerner Farm; S2V, Fred Van Breda Farm.

sugar yield in PC1 were for the 2004 and 2005 trials. Principal Component 2 (PC2) was negatively influenced by the 2006, 2007, and 2010 set of trials and positively influenced by the 2008 and 2009 trials for cane yield. For sugar yield, PC2 was negatively influenced by the 2007 and 2010 series but positively influenced by the 2008 series. The PCA plots for cane and sugar yield showed clear separation of the sites (Fig. 2). On the PC1 axis (which explained 85.85% of the variation), the separation was largely on the average cane and sugar yield. The humic soil testing locations (BV, B1V, and B2V) were in their own cluster, as were the sandy soil locations. The testing locations in the humic soil areas were tightly clustered together; those for the sandy soils were widely scattered. The wide scatter of the sandy soils was largely on the PC2 axis (which explained 6.37% of the variation). For sugar yield, the humic soils were more tightly clustered together than the sandy soil sites. There was more separation of the humic soil sites on the PC1 axis (which explained 82.71% of variation) and PC2 axis (which explained 7.65% of variation). The locations consisting of the humic soil type were on the positive scale on PC1; those with predominantly sandy soils were on the negative scale of PC1. Three locations, namely S1, S2V, and B1V, were similar on the PC2 axis. The locations in the sandy soil areas showed a wider scatter on the PC2 axis than those in the humic soil areas.

6

Table 7. Principal component analysis eigenvectors for cane and sugar yield. Cane yield (t ha-1) Principal

Principal

Year Component 1 Component 2 2003 2004 2005 2006 2007 2008 2009 2010

0.36 0.45 0.42 0.31 0.37 0.23 0.26 0.37

0.06 0.04 0.06 0.21 0.45 0.66 0.47 0.29

Sugar yield (t ha-1) Principal Principal Component 1 Component 2 0.36 0.53 0.45 0.25 0.35 0.20 0.26 0.29

0.20 0.11 0.09 0.10 0.50 0.61 0.21 0.51

Principal Component Analysis for Quality Traits Generally, the eigenvectors for PC1 were all positive for ERC per cane, DM per cane, and fiber per cane (Table 8). For ERC per cane, the dominant eigenvectors on the PC1 scale were from the 2006, 2007, 2008, and 2010 trials. The dominant eigenvectors for DM per cane on the PC1 scale were from 2006 and 2010. The PC2 eigenvectors for DM per cane were dominated by 2006 (negative), 2010 (positive), 2004 (positive), and 2007 (positive). For fiber per cane, the dominant PC1 eigenvector was from the 2006 series. The dominant PC2 eigenvectors were 2007 (negative, largest), followed by 2008 and 2009 (positive), followed by 2003 (positive) and 2006 (negative).

www.crops.org


Table 8. Principal component analysis eigenvectors for estimable recoverable crystal (ERC) per cane, dry matter (DM) per cane, and fiber percane. ERC per cane

DM per cane

Fiber per cane

Year

Principal Component 1



Principal Component2



2003 2004 2005 2006 2007 2008 2009 2010

0.26 0.19 0.05 0.53 0.35 0.39 0.19 0.55

0.11 0.08 0.29

0.27 0.27 0.23 0.52 0.35 0.35 0.32 0.43

0.18 0.36

0.21 0.22 0.21 0.70 0.31 0.34 0.34 0.21

0.26

0.08 0.66 0.63 0.25 0.02


0.06 0.69 0.40 0.07 0.18 0.40

0.01 0.18 0.24 0.63 0.41 0.45 0.27

www.crops.org 7


Figure 2. Principal component analysis biplots for cane yield (TCH) and sugar yield (TSH). PC1, Principal Component 1; PC2, Principal Component 2.


Figure 3. Principal component analysis biplots for estimable recoverable crystal per cane (ERC), dry matter per cane (DM), and fiber per cane (fiber). PC1, Principal Component 1; PC2, Principal Component 2.

For quality traits, the PCA plots showed that the clusters were largely separated by PC1 (Fig. 3). For ERC per cane, the locations with sandy soil types were on the positive scale of PC1 (which accounted for 79.55%),whereas those with the humic soil types were on the negative scale. The 8

locations S1V and SV were similar on the PC1 scale; SV and S2V were similar on the PC2 scale (which accounted for 11.83%). B1V and B2V were similar on the PC1 scale while BV and B1V were similar on the PC2 scale. For DM per cane, SV and S1V were clustered together on

www.crops.org


DISCUSSION The large genotype effects reported in this study indicated the high potential for selecting superior genotypes of sugarcane. The large G×C F-values suggested that ratooning may be important. However, ratooning effects are confounded with year effects (Kang et al., 1987) and are therefore more complex to manipulate. Moreover, the crop-year effects are also confounded with the variability in weather patterns within years, making it difficult to select accurately for ratooning effects (Ramburan, 2013; Zhou and Shoko, 2012b). Although the crop-year effect confounding is present, genotypes that produce higher yield across crop-years are preferred because they are expected to be more stable across crop-years and would provide stable yields across ratoons. These large and highly significant G×L effects also indicate the importance of evaluating genotypes across locations. Because the soil type remains relatively static over time in the testing locations, the locations can be considered as fixed. Consequently, considerable progress in breeding can be achieved by focusing on adaptability to location effects, particularly when the locations are dominated by soil types. In the Midlands region of South Africa, the locations are dominated by soil effects, with one set of the testing locations consisting of humic soils and the second largely constitute sandy soils (Ramburan et al., 2012). The study also found significant G×L×C interactions, which suggested the presence of location-specific performance of genotypes across crop-years, further highlighting the complexity of breeding for ratooning ability. Previous studies have shown that some genotypes ratoon well in sandy soils, whereas most genotypes ratoon well in humic soils where growing conditions are favorable (Zhou, 2014). Therefore, breeding genotypes with specific adaptation to sandy or humic soils could enhance adaptability and hence optimize genetic gains within distinct soil types. The effect of genotype was larger than the G×E effects for quality traits, a similar trend to that for yield traits, suggesting that selecting high-yield and high-quality genotypes would be effective. Genotype × crop-year interaction effects were larger than G×L, indicating the influence of crop-year confounding on genotype cane quality. Generally, it is recognized that cane quality is lower in the plant crop than in ratoon crops (Zhou, 2004b). Cane quality is significantly influenced by weather conditions during the maturation phase of the sugarcane crop. Warm and wet weather leads to lower quality, whereas cool and dry weather results in higher quality (Gosnell, 1967; Zhou et al., 2012). The significant crossover G×L is important crop science, vol. 55, november– december 2015

because it is the component that can be exploited in plant breeding using niche breeding. As reported previously (Zhou, 2014), higher sucrose content is mostly achieved in sandy soils compared to humic soils. Humic soils have higher water-holding capacity and therefore maintain active growth in the sugarcane crop long after the end of the rainy season and during the harvest period. The moisture will promote more active growth, which in turn leads to diminished sucrose deposition in the stalks, as most of the carbohydrates will be used for sustaining growth (Alexander, 1973). These major differences in soil types and their effects on cane quality can be used strategically to develop cultivars that are capable of accelerating sucrose accumulation under ‘unfavorable’ conditions, as in the humic soils in this case. The DM per cane and fiber per cane produced largely nonsignificant G×L×C, indicating the high heritability of these traits and the minimal influence of G×E. However, sucrose content showed a larger influence of G×E and, in particular, larger G×L×C, indicating the complexity of sucrose accumulation as well as the potential for improving genetic gains by breeding for a specific maturation profile under the different soil types and potentially by site-specific selection. The results also confirmed the higher yield potential in humic soils compared to sandy soils. The higher yield potential in the humic soils is attributable (at least in part) to the high clay and organic matter content and inherently high water and nutrient-holding capacity characteristics of humic soils. The yield differences among the sandy and humic soils were highly significant indicating the differential effects of crop growth environments and the yield potential of these two soil types. The sandy soils produced higher quality, with higher sucrose content, higher DM per cane and higher fiber per cane as a result of better maturity conditions compared to the humic soils. The higher fibre may reduce sucrose recovery at mill processing (Shoonees-Muir et al., 2009). Generally, the sandy soils hold less moisture. During the harvest period (which occurs in dry months), these soils dry faster, resulting in termination of crop growth, which results in higher sucrose accumulation compared to humic soils (Zhou, 2014). Principal component analysis for yield traits grouped the sandy and humic soils in two different clusters based on yield potential, indicating that the yield potential of the soils was the most distinguishing variable between these two soil types. The eigenvectors showed that PC1, which accounted for the largest variability, was largely a factor of yield potential, whereas PC2 showed variability across trials. This phenomenon is partly associated with seasonal variability and it occurs from year to year, thus affecting the performance of the crop each year. The PCA plots showed a closer clustering of the humic soil testing locations, whereas the sandy soil testing locations showed greater spread, indicating narrow variability among humic soil sites and larger variability among sandy soil sites. The

www.crops.org 9


the positive PC1 (67.93%) scale; BV, S2V, and B2V were clustered on the negative PC1 scale. Similarly, for fiber per cane, SV and S1V were clustered on the positive scale of PC1 (70.76%), whereas BV, B1V, B2V, and S2V were tightly clustered on the negative scale of PC1.


wider variability of the plot for cane yield, particularly along the PC2 axis, may suggest that the sandy soil sites are more variable and therefore provide better sampling of the sandy soils environment in the Midlands regions. This variability was also reflected in the large fluctuations in yield associated with the sandy soil environments. Considering the spatial distribution of the testing locations, those in the sandy soil areas were more widely dispersed than those in the humic soil areas (Table 2, Fig. 3). For instance, the testing locations in the humic soils are within 5 to 10 km from each other and two of them (B1V and B2V) are only 1.0 km apart. The close clustering of the latter testing locations strongly suggests that they are quite similar. These locations could probably benefit from a critical review aimed at creating new distinct locations that consist of humic soils but are more distant from the current ones to enhance the evaluation of sugarcane for adaptability to a wider agro-ecological range. These results contradict the findings of Ramburan et al. (2012), which showed that S2V was very similar to humic soil locations. The study of Ramburan et al. (2012) analyzed a subset of these data using genotype and G×E interaction biplot analysis and the interpretation was based on biplots with no statistical comparisons. This study highlights the need for combining biplot analysis with statistical tests to validate the results, a caution that has been added to the GGE biplot software (Yan and Kang, 2003). The eigenvectors of quality traits showed that PC1 was largely defined by the average trait values, whereas PC2 captured the variability across trials, which was reflected in the seasonal variability across years. The PCA plot for sucrose content confirmed the higher sucrose content of sandy soil sites compared to humic soils. Therefore, there is merit in maintaining the two breeding programs, which were intended for developing sugarcane cultivars that are adapted specifically to each of the two soil types. The PCA for DM per cane and fiber per cane showed two clusters, one for both SV and S1V, whereas S2V was clustered with the humic soil sites, suggesting the similarity of these traits in the four sites clustered together. Furthermore, SV and S1V are sandier sites than S2V (Ramburan et al., 2012). Cane yield showed higher R 2 values than sugar yield, indicating more variability in the data was represented in the statistical model. The lower CV value of cane yield compared to the sugar yield indicated the greater variability in the sugar yield data (which is essentially a derivative of cane yield and sucrose content). The R 2 values for quality traits were similar, indicating that the model accounted similarly for the variability in the data. Generally, all the quality traits had lower R 2 values than cane yield, suggesting that a covariate is probably necessary for improving the statistical model for analyzing quality data. Considering that sucrose content and the quality variables are influenced by the weather more than yield, particularly during the maturation period closer 10

to harvesting, a covariate that includes temperature or soil moisture content may improve the R 2 values. The results imply that the humic and sandy soils are completely different and therefore maintaining two breeding programs for the two soil types is valid. The highly significant G×L highlights the importance of optimizing the sites to enhance gains for the soil types. In contrast to the findings reported by Ramburan et al. (2012), the results in this study suggested that the testing locations in the humic soil areas merit a critical review aimed at identifying trial sites that are more spread out than currently. Furthermore, the two significant differences in the yield and quality potential of the two soil types highlight the need for two strategic approaches to breeding genotypes adapted to these two environments. Generally, the humic soils would need an approach that aims to identify high yield and high sucrose content, since low quality is generally a problem because of poor maturity associated with good growing conditions during the harvesting period. Therefore, a high sucrose breeding program involving scrutiny of the parents and intentional selection against low sucrose content is required for the humic soils. For the sandy soils, high biomass accumulation during the short period of optimum growth conditions should be the major focus. Genotypes that can produce high cane yield will be desirable under the optimum maturity conditions in sandy soils. Although GC was highly significant, direct selection for ratooning ability is difficult. Enhancing selection for ratooning ability via yield components could improve gains in the ratooning ability of the crop. Selecting for high stalk population (Zhou, 2004a; Zhou and Shoko, 2012a) also enhances breeding for ratooning ability and should be adopted as a future strategy.

CONCLUSIONS The study showed that the sugarcane breeding programs in the Midlands region of South Africa should maintain breeding programs for both the humic and sandy soil areas. Larger gains for both yield and quality could be achieved when breeding for specific adaptability to humic and sandy soils. Nonetheless, the spatial distribution of the testing locations in the humic soils requires a review to enable better sampling of the diversity in this environment. Breeding for ratooning ability was more complex because of crop-year confounding. The low predictability of the ratooning of genotypes indicated the complexity of achieving gains because of crop-year confounding, which limits the ability to identify the genotype effects from those of year to year variation. In addition, robust biomass accumulation during the short optimum growth conditions for sugarcane in the sandy soils should be considered as one of the major focal points of the breeding program, whereas active breeding and selection for high sucrose accumulation would result in high sugar yield in the sandy and humic soils breeding programs.

www.crops.org


Alexander, A.G. 1973. Sugarcane physiology, a comprehensive study of the Saccharum source to sink system. Elsevier Scientific, Amsterdam. Netherlands. Gosnell, J.P. 1967. The growth of sugarcane. Unpublished PhD thesis, University of Natal, Pietermaritzburg, South Africa. Kang, M.S., and J.D. Miller. 1984. Genotype × environment interactions for cane and sugar yield and their implications in sugarcane breeding. Crop Sci. 24:435–440. doi:10.2135/cropsci1984 .0011183X002400030002x Kang, M.S., J.D. Miller, P.Y.P. Tai, J.D. Dean, and B. Glaz. 1987. Implications of confounding of genotype × year and genotype × crop effects in sugarcane. Field Crops Res. 15:349–355. doi:10.1016/0378-4290(87)90022-0 Kimbeng, C.A., A.R. Rattey, and M. Hetherington. 2002. Interpretation and implications of genotype by environment interactions in advanced stage sugarcane selection trials in central Queensland. Aust. J. Agric. Res. 53:1035–1045. doi:10.1071/ AR01190 Kimbeng, C.A., M.M. Zhou, and J.A. Da Silva. 2009. Genotype × environment interactions and resource allocation in sugarcane yield trials in the Rio Grande valley region of Texas. J. Amer. Soc. Sugar Cane Technol. 29:11–24. Nuss, K.J. 1998. Aspects considered in the search for new farms for the Experiment Station. Proceedings of the South African Sugarcane Technologists Association. 72:42–45. Ramburan, S., M.M. Zhou, and M. Labuschagne. 2012. Investigating test site similarity, trait relations and causes of genotype by environment interactions in the Midlands region of South Africa. Field Crops Res. 129:71–80. doi:10.1016/j. fcr.2012.01.017 Ramburan, S. 2013. A multivariate illustration and interpretation of non-repeatable genotype × environment interactions in sugarcane. Field Crops Res. 157:57–64. doi:10.1016/j.fcr.2013.12.009 SAS Institute. 2014. The SAS system for Windows version 9.2. SAS Institute, Cary, NC. Setimela, P.Z., J. Chitalu, A. Jonazi, D. Mambo, M. Hodson, and M. Banziger. 2005. Environmental classification of maize-testing sites in the SADC region and its implication for collaborative maize breeding strategies in the subcontinent. Euphytica 145:123–132. doi:10.1007/s10681-005-0625-4


Shoonees-Muir, B.M., M.A. Ronaldson, G. Naidoo, and P.M. Schorn. 2009. SASTA laboratory manual including the official methods. Yan, W., and M.S. Kang. 2003. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press, New York, USA. Yan, W., and N.A. Tinker. 2006. Biplot analysis of multi-environment trial data: Principles and applications. Can. J. Plant Sci. 86:623–645. doi:10.4141/P05-169 Zhou, M.M. 2004a. Stalk population control of yield, quality and agronomic traits of sugarcane population in early selection stages. Sugar Cane Int. 22(5):14–20. Zhou, M.M. 2004b. Performance of varieties N14 and NCo376 in the South East Lowveld of Zimbabwe. Proceedings of the South African Sugarcane Technologists Association. 78:153–160. Zhou, M.M., S.V. Joshi, T. Maritz, and H. Koberstein. 2011. Components of genotype by environment interaction among SASRI regional breeding and selection programmes and their implications. Proceedings of the South African Sugarcane Technologists Association. 84:363–374. Zhou, M.M., and M.D. Shoko. 2012a. Simultaneous selection for yield and ratooning ability in sugarcane genotypes using analysis of covariance. South African J. Plant Soil. 29(2):93–100. doi :10.1080/02571862.2012.717639 Zhou, M.M., and M.D. Shoko. 2012b. Simultaneous selection for yield and stability in sugarcane using parametric statistics. J. Agric. Sci. Technol. 2:400–410. Zhou, M.M., S.V. Joshi, and T. Maritz. 2012. Trends and implications of genotype by environment interaction in South African sugarcane breeding. J. Crop Improv. 26:1–14. doi:10.1080/154 27528.2011.622429 Zhou, M.M. 2014. Cultivar genetic gains for sugarcane yield, sucrose content and sugar yield in the Midlands region breeding programs. Proceedings of the South African Sugarcane Technologists Association. 87:419–431.

www.crops.org 11


References

Crop Science Proofing Instructions

Crop Science Proofing Instructions

Suggest Documents