基于GGE双标图的高粱品种农艺性状和稳产性分析

doi:10.16035/j.issn.1001-7283.2023.02.006

摘要/Abstract

摘要：

为了筛选丰产稳产、适应性强的高粱新品种，促进辽西地区高粱产业健康可持续发展。2019-2020年采用随机不完全区组设计（alpha-格子设计），通过R语言GGE双标图分析了30个高粱品种的产量和相关农艺性状。方差分析表明，年份、基因型、基因型与年份互作对高粱产量和相关农艺性状均有显著影响（P<0.05），其产量变异的平方和占总平方和的比例分别为32.1%、41.3%和11.3%，基因型效应对产量和相关农艺性状的变异贡献率最大，株高和穗长指标遗传力较大。GGE双标图分析表明，辽杂19号、平试13和济粱2丰产稳产性较好；辽杂19号和平试13在株高、产量、穗重和穗粒重方面综合性状表现较好；辽杂19号距离“理想品种”最近，其次为平试13。皮尔逊相关分析表明，高粱籽粒产量与株高、穗重、穗粒重和穗粒数均呈极显著正相关（P<0.01）。从参试品种看，辽西半干旱地区高秆品种比矮秆品种更具产量优势。在朝阳地区，辽杂19号、平试13和济粱2具有较高的产量和稳定性。在特定生态区域，基因型是产量和相关农艺性状差异的主要因素。

关键词: GGE双标图, 高粱, 产量, 农艺性状, 稳定性

Abstract:

In order to screen new sorghum varieties with high and stable yield and high adaptability, and promote the sustainable development of sorghum industry in the western Liaoning, the yield and agronomic traits of 30 sorghum cultivars were analyzed by GGE biplot in R language using a random incomplete block design (alpha- lattice design) with three replicates from 2019 to 2020. The analysis of variance showed that year, genotype, and interaction between genotype and year had significant effects on sorghum yield and related agronomic traits (P<0.05), and the proportion of the sum of squares of yield variation to the total sum of squares was 32.1%, 41.3% and 11.3%, respectively. Genotype had the greatest contribution to yield and related agronomic characteristics. The genotypic effects of plant height and spike length were higher. GGE biplot showed that Liaoza 19, Pingshi 13 and Jiliang 2 had better high and stable yield. Liaoza 19 and Pingshi 13 had better comprehensive traits in plant height, yield, spike weight and grain weight per spike. Liaoza 19 was the closest to the “ideal variety”, followed by Pingshi 13. Pearson correlation analysis showed that the grain yield of sorghum was positively correlated with plant height, spike weight, grain weight per spike and kernels number per spike (P<0.01). Based on the tested varieties, the high-stalk varieties had more yield advantages than the low-stalk varieties. Liaoza 19, Pingshi 13 and Jiliang 2 had better performance both in yield and its stability in Chaoyang area. In a specific ecological region, genotype was the main factor affecting the difference of yield and related agronomic traits.

Key words: GGE biplot, Sorghum, Yield, Agronomic trait, Stability

肖继兵, 刘志, 孔凡信, 辛宗绪, 吴宏生. 基于GGE双标图的高粱品种农艺性状和稳产性分析[J]. 作物杂志, 2023 (2): 36–45

Xiao Jibing, Liu Zhi, Kong Fanxin, Xin Zongxu, Wu Hongsheng. Analysis of Agronomic Traits and Yield Stability of Sorghum Varieties Based on GGE Biplot[J]. Crops, 2023(2): 36–45

图/表 9

表1

表2

表3

表4

供试高粱品种农艺性状

编号Code	PD (plants/hm²)	PH (cm)	SL (cm)	SWP (g)	GWP (g)	KPS	TGW (g)	GP (d)
S1	90 000	221.7±9.1	28.1±2.4	139.4±26.3	108.2±20.3	3857.0±58.2	28.0±3.0	105
S2	150 000	154.0±5.2	28.0±2.1	85.9±20.2	69.6±15.6	3077.4±634.4	22.6±2.5	101
S3	105 000	159.7±4.8	25.5±1.8	78.4±10.5	63.4±14.5	1927.1±146.9	32.9±2.7	112
S4	105 000	153.2±5.6	27.8±0.9	90.5±25.4	69.8±20.9	3415.9±162.0	20.4±4.1	98
S5	105 000	171.9±5.9	33.5±1.1	98.6±6.4	81.1±12.5	3487.8±137.8	23.2±1.9	101
S6	120 000	124.1±7.3	25.5±1.6	73.0±8.4	60.1±3.0	2607.0±124.8	23.0±2.5	107
S7	105 000	140.9±11.3	30.6±1.5	91.1±23.6	75.6±9.9	3566.3±105.3	21.2±2.2	92
S8	180 000	150.7±5.7	31.5±1.1	90.4±7.0	75.4±13.4	4901.1±219.7	15.4±0.8	104
S9	120 000	160.7±3.0	29.2±0.9	119.1±22.3	96.6±18.2	3820.2±246.5	25.3±0.9	99
S10	120 000	160.2±3.3	27.5±1.0	115.3±5.4	93.4±8.0	3639.5±140.3	25.7±4.7	101
S11	120 000	150.9±10.4	22.8±1.4	97.2±5.5	81.5±7.8	4217.9±19.2	19.3±1.9	98
S12	120 000	160.6±11.6	27.0±0.9	118.3±9.7	95.9±8.0	3683.9±31.8	26.0±1.4	101
S13	120 000	160.8±13.1	28.6±1.3	110.5±23.1	88.8±19.1	3851.4±222.6	23.1±1.2	97
S14	120 000	93.6±5.8	24.7±1.1	55.1±8.8	44.2±6.6	2290.3±131.4	19.3±2.9	89
S15	120 000	164.6±8.6	31.6±2.1	111.2±15.5	93.8±5.4	3948.6±117.1	23.8±2.2	104
S16	120 000	169.3±11.2	31.6±1.2	115.2±10.3	97.6±7.6	4209.7±34.0	23.2±2.4	104
S17	120 000	131.8±10.1	30.8±0.9	68.9±29.4	75.5±7.2	3530.1±295.8	21.4±0.8	95
S18	135 000	129.5±8.2	34.7±3.7	77.1±10.8	59.6±10.4	2194.5±94.4	27.2±1.9	108
S19	135 000	166.1±8.1	32.8±1.2	97.2±9.5	80.5±3.9	2984.1±189.8	27.0±0.7	101
S20	150 000	183.0±5.9	34.2±0.3	105.5±32.9	91.1±22.2	3197.6±102.0	28.5±4.5	103
S21	120 000	138.6±4.4	28.1±2.9	99.4±9.4	83.9±13.1	3578.9±41.2	23.4±2.8	100
S22	120 000	136.1±5.8	28.9±0.7	96.5±9.5	80.6±7.4	3286.2±49.0	24.5±1.5	98
S23	97 500	169.3±6.4	28.8±1.3	85.8±11.4	63.4±9.2	2538.7±65.7	25.0±2.7	103
S24	97 500	159.5±5.5	26.1±1.6	100.8±12.6	77.9±8.9	3111.8±106.0	25.0±1.1	97
S25	97 500	99.0±5.8	25.4±2.6	66.6±14.1	51.5±6.8	2524.0±165.7	20.4±0.9	101
S26	180 000	126.7±3.2	29.3±0.9	59.4±8.6	43.9±6.7	2582.4±75.7	17.0±1.2	112
S27	150 000	142.5±4.1	30.7±1.1	70.4±9.8	53.7±5.9	2509.3±542.3	21.4±1.3	108
S28	150 000	132.8±14.2	31.1±0.6	80.1±5.7	58.5±8.7	2142.1±80.1	27.3±2.6	93
S29	150 000	165.8±4.1	30.7±1.2	80.3±14.7	56.9±16.4	2671.2±318.8	21.3±0.7	101
S30	225 000	126.6±5.1	32.6±1.6	59.7±10.9	45.9±10.0	2038.0±225.7	22.5±1.2	93
平均值Average		150.1	29.2	91.2	73.9	3179.7	23.5	100.6
变异系数CV (%)		16.54	10.11	22.40	23.85	23.41	15.36	5.56

表4

表5

图1

图2

图3

表6

参考文献 44

[1]	卢庆善. 高粱学. 北京: 中国农业出版社,1999.
[2]	Yan W. GGE Biplot vs. AMMI graphs for genotype-by- environment data analysis. Journal of the Indian Society of Agricultural Statistics, 2011, 65(2):1-10.
[3]	常磊, 柴守玺. GGE双标图在我国旱地春小麦稳产性分析中的应用. 中国生态农业学报, 2010, 18(5):988-994.
[4]	许乃银, 张国伟, 李健, 等. 基于GGE双标图的棉花品种生态区划分. 应用生态学报, 2013, 24(3):771-776.
[5]	严威凯, 盛庆来, 胡跃高, 等. GGE叠图法―分析品种×环境互作模式的理想方法. 作物学报, 2001, 27(1):21-28.
[6]	Yang R C, Blade S, Crossa J. Identifying isoyield environments for field pea production. Crop Science, 2005, 45(1):106-113. doi: 10.1002/csc2.v45.1
[7]	Crossa J. Statistical analyses of multilocation trials. Advances in Gronomy, 1990, 44(1):55-85.
[8]	Yang R C, Crossa J, Cornelius P L, et al. Biplot analysis of genotype×environment interaction:proceed with caution. Crop Science, 2009, 49(5):1564-1576. doi: 10.2135/cropsci2008.11.0665
[9]	石强, 李亚杰, 范士杰, 等. 贵州省马铃薯区试品种产量与农艺性状的GGE双标图分析. 干旱地区农业研究, 2015, 33(2):5-15.
[10]	李吉睿, 孟亚雄, 司二静, 等. 中国西北水地春小麦基因型与环境互作及其产量稳定性分析. 甘肃农业大学学报, 2016, 51(6):44-52.
[11]	Yan W, Pageau D, Frégeau-Reid J, et al. Oat mega-environments and test-locations in Quebec. Canadian Journal of Plant Science, 2011, 91(4):643-649. doi: 10.4141/cjps10139
[12]	常磊, 韩凡香, 柴守玺, 等. 我国旱地小麦区域试验精确度及其环境综合评价. 应用生态学报, 2013, 24(10):2814-2820.
[13]	Freeman G H, Perkins J M. Environmental and genotype- environmental components of variability Ⅷ-Relations between genotypes grown in different environments and measures of these environments. Heredity, 1971, 27:15-23. doi: 10.1038/hdy.1971.67
[14]	Yan W K, Kang M S. GGE biplot analysis:A Graphical Tool for Breeders,Geneticists,and Agronomists. Boca Raton, Florida: CRC Press, 2003.
[15]	Yan W, Hunt L A. Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Science, 2000, 40(3):597-605. doi: 10.2135/cropsci2000.403597x
[16]	Yan W, Holland J B. A heritability-adjusted GGE biplot for test environment evaluation. Euphytica, 2010, 171(3):355-369. doi: 10.1007/s10681-009-0030-5
[17]	Dehghani H, Ebadi A, Yousefi A. Biplot analysis of genotype by environment interaction for barley yield in Iran. Agronomy Journal, 2006, 98:388-393. doi: 10.2134/agronj2004.0310
[18]	昝凯, 陈亚光, 徐淑霞, 等. 基于R语言的GGE双标图在大豆区试中的应用. 大豆科技, 2019(4):16-20.
[19]	陈四龙, 李玉荣, 程增书, 等. 用GGE双标图分析种植密度对高油花生生长和产量的影响. 作物学报, 2009, 35(7):1328-1335.
[20]	Yan W. GGE biplot—A Windows application for graphical analysis of multi environment trial data and other types of two‐ way data. Agronomy Journal, 2001, 93(5):1111-1118. doi: 10.2134/agronj2001.9351111x
[21]	李亚杰, 罗磊, 白江平, 等. GGE双标图在西北旱区马铃薯新品种选育中的应用. 干旱地区农业研究, 2018, 36(2):192-198.
[22]	严威凯. 双标图分析在农作物品种多点试验中的应用. 作物学报, 2010, 36(11):1805-1819. doi: 10.3724/SP.J.1006.2010.01805
[23]	Luo L, Zhang F, Hong M, et al. Evaluation of yield,stability and adaptability of national winter rapeseed regional trials in the upper Yangtze River region in 2017-2018. Oil Crop Science, 2020, 5(3):121-128. doi: 10.1016/j.ocsci.2020.07.001
[24]	Flores F, Hybl M, Knudsen J C, et al. Adaptation of spring faba bean types across European climates. Field Crops Research, 2013, 145:1-9. doi: 10.1016/j.fcr.2013.01.022
[25]	Yan W. LG biplot:a graphical method for mega-environment investigation using existing crop variety trial data. Scientific Reports, 2019, 9(1):1-8. doi: 10.1038/s41598-018-37186-2
[26]	Choudhary M, Kumar B, Kumar P, et al. GGE biplot analysis of genotype × environment interaction and identification of mega- environment for baby corn hybrids evaluation in India. Indian Journal of Genetics and Plant Breeding, 2019, 79(4):658-669.
[27]	de Oliveira T R A, de Carvalho H W L, Oliveira G H F, et al. Hybrid maize selection through GGE biplot analysis. Bragantia, 2019, 78(2):166-174. doi: 10.1590/1678-4499.20170438
[28]	Yan W, Pageau D, Frégeau-reid J, et al. Assessing the representativeness and repeatability of test locations for genotype evaluation. Crop Science, 2011, 51(4):1603-1610. doi: 10.2135/cropsci2011.01.0016
[29]	Badu-apraku B, Akinwale R O. Biplot analysis of line× tester data of maize (Zea mays L.) inbred lines under stress and nonstress environments. Cereal Research Communications, 2019, 47(3):518-530. doi: 10.1556/0806.47.2019.25
[30]	Xi K, Turkington T K, Juskiw P, et al. Field screening is effective for identifying genetic resistance to scald of barley. Crop Science, 2019, 59(4):1479-1493. doi: 10.2135/cropsci2018.09.0536
[31]	Shojaei S H, Mostafavi K, Khosroshahli M, et al. Assessment of genotype-trait interaction in maize (Zea mays L.) hybrids using GGT biplot analysis. Food Science Nutrition, 2020, 8(10):5340-5351. doi: 10.1002/fsn3.v8.10
[32]	Shiri M, Kamrani M, Ebadi A. Evaluation of integrated nitrogen and phosphorous management using the TT biplot method in soybean. Journal of Agricultural Sciences, 2020, 65(1):19-35.
[33]	Baduapraku B, Akinwale R O. Cultivar evaluation and trait analysis of tropical early maturing maize under Striga-infested and Striga-free environments. Field Crops Research, 2011, 121(1):186-194. doi: 10.1016/j.fcr.2010.12.011
[34]	周宇飞, 依兵, 吴奇, 等. 基于GGE双标图的高粱品种产量及其稳定性分析. 沈阳农业大学学报, 2018, 49(6):649-654.
[35]	彭远英, 颜红海, 郭来春, 等. 燕麦属不同倍性种质资源抗旱性状评价及筛选. 生态学报, 2011, 31(9):2478-2491.
[36]	陆平. 高粱种质资源描述规范和数据标准. 北京: 中国农业出版社, 2006.
[37]	Yan W, Hunt L A, Sheng Q, et al. Cultivar evaluation and mega- environment investigation based on the GGE biplot. Crop Science, 2000, 40(3):597-605. doi: 10.2135/cropsci2000.403597x
[38]	Belay F, Meresa H, Gebreslasie A. Evaluation of the performance of some white seeded sesame (Sesamum Indicum L.) genotypes using GGE biplot analysis in Northern Ethiopia. Journal of Cereals and Oilseeds, 2018, 9(1):1-9. doi: 10.5897/JCO
[39]	赵祖亮, 曲艺伟, 刘哲, 等. 玉米产量的品种与环境互作效应空间分布规律. 农业工程学报, 2015, 31(24):232-238.
[40]	陈彩锦, 张尚沛, 师尚礼, 等. 基于GGE双标图对苜蓿品种丰产性和稳定性综合评价. 草地学报, 2021, 29(5):912-918. doi: 10.11733/j.issn.1007-0435.2021.05.007
[41]	柳娜, 曹东, 王世红, 等. 基于GGE双标图的甘肃春小麦区试品系稳产性和试点代表性分析. 西北农林科技大学学报(自然科学版), 2018, 46(4):39-48.
[42]	Kabak D, Akçura M. Bingöl ilinden toplanan yerel çavdarlarda tane verimi ve bazı özellikler arasındaki ilişkilerin biplot analizi ile incelenmesi. Türk Tarım ve Doğa Bilimleri Dergisi, 2017, 4(2):227-235.
[43]	Sabaghnia N. Investigation of some morphological traits in studied lentil (Lens culinaris Medik.) genotypes grown with foliar application of nanosized ferric oxide//Annales Universitatis Mariae Curie-Sklodowska,sectio C-Biologia. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej, 2014, 69(2):29-38.
[44]	肖继兵, 刘志, 孔凡信, 等. 种植方式和密度对高粱群体结构和产量的影响. 中国农业科学, 2018, 51(22):4264-4276. doi: 10.3864/j.issn.0578-1752.2018.22.005

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气象因子 Meteorological factor	2019						2020
气象因子 Meteorological factor	5月 May	6月 June	7月 July	8月 August	9月 September	总和 Sum	5月 May	6月 June	7月 July	8月 August	9月 September	总和 Sum
降雨量Rainfall (mm)	24.6	53.8	85.8	59.4	17.4	241.0	56.2	35.2	67.0	154.5	69.1	382.0
≥10℃积温≥10℃ accumulated temperature (℃)	675.8	728.5	821.5	740.9	620.0	3586.7	570.4	753.3	802.9	775.0	564.2	3465.8
日照时数Sunshine hour (h)	319.3	351.4	359.3	321.2	275.8	1627.0	329.3	363.3	360.6	324.9	277.4	1655.5

编号Code	名称Name	来源Origin	编号Code	名称Name	来源Origin
S1	辽杂19号	辽宁省农业科学院	S16	吉杂145	吉林省农业科学院
S2	辽杂37号	辽宁省农业科学院	S17	吉杂159	吉林省农业科学院
S3	辽粘3号	辽宁省农业科学院	S18	济粱1	山东省农业科学院
S4	白杂11号	吉林省白城市农业科学院	S19	济粱2	山东省农业科学院
S5	白杂12号	吉林省白城市农业科学院	S20	平试13	甘肃省平凉市农业科学研究所
S6	白杂13号	吉林省白城市农业科学院	S21	通杂108	通辽市农业科学院
S7	白杂14号	吉林省白城市农业科学院	S22	通杂127	通辽市农业科学院
S8	机糯粱1号	四川省农业科学院	S23	锦杂110	锦州市农业科学院
S9	吉杂124	吉林省农业科学院	S24	赤杂101	赤峰市农牧科学研究院
S10	吉杂127	吉林省农业科学院	S25	赤杂119	赤峰市农牧科学研究院
S11	吉杂136	吉林省农业科学院	S26	晋杂34号	山西省农业科学院
S12	吉杂137	吉林省农业科学院	S27	晋中0742	山西省农业科学院
S13	吉杂138	吉林省农业科学院	S28	辽夏粱2	辽宁省农业科学院
S14	吉杂140	吉林省农业科学院	S29	龙米粱1号	黑龙江省农业科学院
S15	吉杂142	吉林省农业科学院	S30	龙杂13	黑龙江省农业科学院

编号 Code	2019	2020	平均产量 Average yield
S1	8577.5±317.1	10 816.0±250.4	9696.8±216.7
S2	6398.0±320.5	6405.0±625.4	6401.5±472.5
S3	7375.0±329.4	7851.0±337.5	7613.0±323.7
S4	8540.7±343.2	9532.6±642.9	9036.6±215.5
S5	8576.6±524.7	10 238.5±750.9	9407.5±203.6
S6	4396.7±978.9	7203.6±449.6	5800.1±654.1
S7	6981.3±678.2	9465.9±680.5	8223.6±307.8
S8	6975.8±717.1	10116.2±397.8	8546.0±176.5
S9	6742.3±1331.4	10 271.9±1225.7	8507.1±1154.1
S10	7148.0±950.6	10 077.3±642.5	8612.6±629.0
S11	6797.8±215.0	9921.7±682.4	8359.7±233.8
S12	6948.0±888.2	10 866.6±744.7	8907.3±251.0
S13	6859.0±1774.3	10 333.0±1562.7	8596.0±1593.4
S14	5208.2±287.0	6008.6±610.9	5608.4±421.5
S15	8220.8±721.6	9871.6±437.4	9046.2±565.3
S16	8209.7±873.5	9449.2±1175.6	8829.4±837.9
S17	6853.5±508.8	7920.7±581.4	7387.1±369.4
S18	5408.2±1635.6	10 255.2±981.2	7831.7±1261.6
S19	8354.2±1595.7	11 172.3±356.5	9763.2±670.9
S20	8454.3±656.3	11 372.4±303.4	9913.3±478.4
S21	7787.2±350.2	10 510.8±855.9	9149.0±566.0
S22	7192.5±1017.3	8960.1±756.2	8076.3±574.6
S23	6080.8±915.2	6547.7±167.0	6314.3±532.3
S24	5686.2±953.7	7715.0±619.7	6700.6±375.1
S25	7112.9±414.8	8320.9±986.4	7716.9±460.3
S26	5801.0±960.5	7945.0±1374.8	6873.0±224.5
S27	7707.5±215.1	8069.5±120.4	7888.5±147.6
S28	4885.5±1622.2	10 266.2±1154.6	7575.9±1372.5
S29	4418.9±1632.9	7409.3±271.6	5914.1±710.6
S30	3924.0±1232.3	6464.3±520.5	5194.1±415.2
平均值Average	6787.4	9045.2	7916.3
变异系数CV (%)	19.70	17.49	16.56

农艺性状 Agronomic trait	参数 Parameter	变异来源Source of variation
农艺性状 Agronomic trait	参数 Parameter	年份 Year	重复 Repetition	区组 Block	品种 Variety	年份×品种 Year×variety	误差 Error	总变异 Total variation
PH	平方和	161.9	30.0	203.3	101 404.1	3160.9	4575.2	123 266.5
	F值	4.0	0.4	1.0	86.4	2.7
	P值	0.0479	0.6915	0.4186	0.0000	0.0001
	占总变异比例 (%)	0.1	0.0	0.2	82.3	2.6
SL	平方和	47.4	9.7	22.3	2059.6	94.7	230.8	2658.1
	F值	23.2	2.4	2.2	34.8	1.6
	P值	0.0000	0.0984	0.0605	0.0000	0.0431
	占总变异比例 (%)	1.8	0.4	0.8	77.5	3.6
SWP	平方和	11 137.6	6297.2	162.8	54 357.0	30 230.2	12 744.4	121 272.4
	F值	98.8	27.9	0.3	16.6	9.2
	P值	0.0000	0.0000	0.9184	0.0000	0.0000
	占总变异比例 (%)	9.2	5.2	0.1	44.8	24.9
农艺性状 Agronomic trait	参数 Parameter	变异来源Source of variation
农艺性状 Agronomic trait	参数 Parameter	年份 Year	重复 Repetition	区组 Block	品种 Variety	年份×品种 Year×variety	误差 Error		总变异 Total variation
GWP	平方和	7411.3	4052.8	219.3	43 768.5	22 383.7	9547.4		90 403.7
	F值	87.7	24.0	0.5	17.9	9.1
	P值	0.0000	0.0000	0.7614	0.0000	0.0000
	占总变异比例 (%)	8.2	4.5	0.2	48.4	24.8
KPS	平方和	1 111 592.7	1 171 682.1	286 296.5	85 260 730.0	77 825 588.1	17 035 935.9		193 708 021.4
	F值	7.4	3.9	0.4	19.5	17.8
	P值	0.0077	0.0233	0.8617	0.0000	0.0000
	占总变异比例 (%)	0.6	0.6	0.1	44.0	40.2
TGW	平方和	892.9	205.2	26.1	1334.8	907.2	463.4		3856.0
	F值	217.7	25.0	1.3	11.2	7.6
	P值	0.0000	0.0000	0.2810	0.0000	0.0000
	占总变异比例 (%)	23.2	5.3	0.7	34.6	23.5
Y	平方和	220 023 244.8	2 845 744.5	5 114 880.6	283 334 355.5	77 296 430.0	81 878 279.1		686 295 650.4
	F值	303.7	2.0	1.4	13.5	3.7
	P值	0.0000	0.1451	0.2253	0.0000	0.0000
	占总变异比例 (%)	32.1	0.4	0.7	41.3	11.3

性状Trait	PH	SL	SWP	GWP	PD	KPS	TGW	GP	Y
PH	1.000
SL	0.229	1.000
SWP	0.799^**	0.041	1.000
GWP	0.733^**	0.075	0.959^**	1.000
PD	-0.244	0.430^*	-0.424^*	-0.422^*	1.000
KPS	0.454^*	0.005	0.698^**	0.776^**	-0.198	1.000
TGW	0.454^*	0.125	0.394^*	0.351	-0.312	-0.281	1.000
GP	0.284	0.091	0.059	0.012	0.023	-0.076	0.207	1.000
Y	0.555^**	0.269	0.721^**	0.756^**	-0.331	0.611^**	0.269	0.166	1.000