四川省玉米品种区域试验（平丘组）试点评价研究
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摘要
本研究用9个统计数和联合方差分析对2006年-2008年四川省玉米品种区域试验(平丘组)资料进行分析并对各统计方法间进行比较研究,利用试点对品种的判别能力、试点的代表性、试点的精度三个方面,分年度对四川省玉米品种区域试点进行综合评判,结果表明:
1.遗传变异系数、回归系数、环境区分指数、AMMI模型D值和SREG模型的D值这5个反映试点对品种判别能力的统计数中,遗传变异系数只用一个试点内的品种产量作为品种在试点上的表现来进行计算,没有把一年的资料作为一个整体来考虑,对资料信息利用较差;回归系数是建立互作效应与加性环境指数之间呈线性关系的基础之上,且误差分布不独立,在品种与试点交互作用很大时,回归系数对试点判别能力的评估不理想;环境区分指数受试验品种比较精确度影响较大,且不能解释品种与试点的交互作用;AMMI模型D值仅仅考虑试点与品种的互作效应来评价试点对品种的判别能力,不能反映品种效应的在试点中的变异,因此以AMMI模型D值来作为试点对品种的判别能力的统计数值得商榷;SREG模型的D值同时考虑品种选育中品种和品种与试点的互作这两个重要因素,并将所有试点资料作为一个整体研究试点的判别能力,能准确反映试点对品种的判别能力。
2.相关系数和SREG模型下CCL叠图中试点向量与各试点一、二主成分的平均轴夹角在反映试点代表性方面对各试点代表性的秩次表现极显著相关,本文用相关系数作为一个定量指标来研究试点的代表性。
3.对单年某个试点的精度进行分析时,试验精度和品种比较精度是一致的,对试点进行综合评判时,可以任选其中一个统计数来计算试点的综合评判指数,本文选用反映试点试验精度的变异系数。
4.通过方法的比较用SREG模型下的D值作为试点对品种的判别能力统计数、试点的试验精度作为试点的精确度的统计数,品种在某试点产量的均值与该品种在所有试点的产量均值间的相关系数作为试点代表性的统计数,并通过三个方面对试点进行综合评价,研究发现,2006年内江、雅安列为5五级试点;2007年,成都列为5级试点;2008年南江、成都、武胜列为5级试点;两年中都表现4级或4级以下的试点有,峨眉山、南江、成都;3年均表现为4级或4级以下试点为雅安试点。
In this study, 9 statistics and combined anovas analysis were used to analyse the material of locations (even hill group) of maize regional trial in Sichuan province and also compared each statistical method in detail. Discrimination ability of locations, representation of locations and precision of locations are used to carry out quality synthetic evaluation of the locations of maize regional trial in Sichuan province. The results showed that:
1. genetic change coefficient(GCV), regression coefficient(b), discrimination index (Y_m) and distance between location and zero point (D) based on additive main effects and multiplicative interactions (AMMI) model and sites regression (SREG) model, these five statistics are reflect the location distinguishes the cultivars ability. GCV with only a single location's yield to estimate the discrimination ability of the location, had not considered yearly material of maize regional trial as a whole, this made GCV lack of adequate information to estimate the discrimination ability of the location; Regression coefficient is to establish interaction with the additive effect of environmental index showed a linear relationship between the foundation, moreover the error variance component distribution is not independent, so the regression analysis is not a ideal model to estimate the discrimination ability of locations when the interactions between locations and cultivars are rich; Y_m is highly effected by cultivars comparison precision and can't explain more information about the interaction between locations and cultivars; the D value based on AMMI model merely considered the interaction between locations and cultivars which can not reflect the variation of the cultivars under the locations, this made the D value based on AMMI model to estimate the discrimination ability of the location is worth discussing; the D value based on SREG model simultaneously considers in the variation of the cultivars and the interaction between locations and cultivars, In addition, D value based on SREG considered yearly material of maize regional trial as a whole to study the discrimination ability of locations, so that can reflect the discrimination ability of locations accurately.
2. Correlation coefficient(R), the angle (A_n) between the locations' first two component value and the average first two component value for all locations under the CCL bipplot based on SREG, Both R A_n can reflect the representation of locations and it was shown that coefficient of rank correlation between R and A_n were significant at the 0.01 level. In this study, R was used as a quantification index to evaluate the representation of locations.
3. The experimental precision(CCV) and the cultivar comparison precision(RLSD) were consistent when used to analysis the precision of single location by yearly, each of them could be used to calculate the synthetic evaluation index.
4. Select D value based on SREG model as the discrimination ability of locations, correlation coefficient(R) as the representation of locations and the experimental precision as the precision of locations to calculate the location quality synthetic evaluation index. The results showed that Neijiang and Yaan were listed as fifth level location in 2006, Chengdu were listed as fifth level location in 2007, Bazhong, Chengdu and Wusheng were listed as fifth level location in 2008, two years were listed as below fourth location were Emeishan, Bazhong and Chengdu, three years were listed as below fourth location was Yaan.
1.遗传变异系数、回归系数、环境区分指数、AMMI模型D值和SREG模型的D值这5个反映试点对品种判别能力的统计数中,遗传变异系数只用一个试点内的品种产量作为品种在试点上的表现来进行计算,没有把一年的资料作为一个整体来考虑,对资料信息利用较差;回归系数是建立互作效应与加性环境指数之间呈线性关系的基础之上,且误差分布不独立,在品种与试点交互作用很大时,回归系数对试点判别能力的评估不理想;环境区分指数受试验品种比较精确度影响较大,且不能解释品种与试点的交互作用;AMMI模型D值仅仅考虑试点与品种的互作效应来评价试点对品种的判别能力,不能反映品种效应的在试点中的变异,因此以AMMI模型D值来作为试点对品种的判别能力的统计数值得商榷;SREG模型的D值同时考虑品种选育中品种和品种与试点的互作这两个重要因素,并将所有试点资料作为一个整体研究试点的判别能力,能准确反映试点对品种的判别能力。
2.相关系数和SREG模型下CCL叠图中试点向量与各试点一、二主成分的平均轴夹角在反映试点代表性方面对各试点代表性的秩次表现极显著相关,本文用相关系数作为一个定量指标来研究试点的代表性。
3.对单年某个试点的精度进行分析时,试验精度和品种比较精度是一致的,对试点进行综合评判时,可以任选其中一个统计数来计算试点的综合评判指数,本文选用反映试点试验精度的变异系数。
4.通过方法的比较用SREG模型下的D值作为试点对品种的判别能力统计数、试点的试验精度作为试点的精确度的统计数,品种在某试点产量的均值与该品种在所有试点的产量均值间的相关系数作为试点代表性的统计数,并通过三个方面对试点进行综合评价,研究发现,2006年内江、雅安列为5五级试点;2007年,成都列为5级试点;2008年南江、成都、武胜列为5级试点;两年中都表现4级或4级以下的试点有,峨眉山、南江、成都;3年均表现为4级或4级以下试点为雅安试点。
In this study, 9 statistics and combined anovas analysis were used to analyse the material of locations (even hill group) of maize regional trial in Sichuan province and also compared each statistical method in detail. Discrimination ability of locations, representation of locations and precision of locations are used to carry out quality synthetic evaluation of the locations of maize regional trial in Sichuan province. The results showed that:
1. genetic change coefficient(GCV), regression coefficient(b), discrimination index (Y_m) and distance between location and zero point (D) based on additive main effects and multiplicative interactions (AMMI) model and sites regression (SREG) model, these five statistics are reflect the location distinguishes the cultivars ability. GCV with only a single location's yield to estimate the discrimination ability of the location, had not considered yearly material of maize regional trial as a whole, this made GCV lack of adequate information to estimate the discrimination ability of the location; Regression coefficient is to establish interaction with the additive effect of environmental index showed a linear relationship between the foundation, moreover the error variance component distribution is not independent, so the regression analysis is not a ideal model to estimate the discrimination ability of locations when the interactions between locations and cultivars are rich; Y_m is highly effected by cultivars comparison precision and can't explain more information about the interaction between locations and cultivars; the D value based on AMMI model merely considered the interaction between locations and cultivars which can not reflect the variation of the cultivars under the locations, this made the D value based on AMMI model to estimate the discrimination ability of the location is worth discussing; the D value based on SREG model simultaneously considers in the variation of the cultivars and the interaction between locations and cultivars, In addition, D value based on SREG considered yearly material of maize regional trial as a whole to study the discrimination ability of locations, so that can reflect the discrimination ability of locations accurately.
2. Correlation coefficient(R), the angle (A_n) between the locations' first two component value and the average first two component value for all locations under the CCL bipplot based on SREG, Both R A_n can reflect the representation of locations and it was shown that coefficient of rank correlation between R and A_n were significant at the 0.01 level. In this study, R was used as a quantification index to evaluate the representation of locations.
3. The experimental precision(CCV) and the cultivar comparison precision(RLSD) were consistent when used to analysis the precision of single location by yearly, each of them could be used to calculate the synthetic evaluation index.
4. Select D value based on SREG model as the discrimination ability of locations, correlation coefficient(R) as the representation of locations and the experimental precision as the precision of locations to calculate the location quality synthetic evaluation index. The results showed that Neijiang and Yaan were listed as fifth level location in 2006, Chengdu were listed as fifth level location in 2007, Bazhong, Chengdu and Wusheng were listed as fifth level location in 2008, two years were listed as below fourth location were Emeishan, Bazhong and Chengdu, three years were listed as below fourth location was Yaan.
引文
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