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A study on the methodolgy of cultivar evaluation based on yield trial data with special reference to winter wheat in Ontario.

机译:基于产量试验数据的品种评估方法研究,特别针对安大略省的冬小麦。

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Improvement in the methodology of yield trial data analysis is an important aspect of improving breeding efficiency. The key to effective cultivar evaluation based on yield trial data is to correctly grasp the pattern of genotype by environment (GE) interaction and to develop a selection strategy in accordance with it.; One outcome of this study is the development of a "GGE biplot", which graphically displays the genotypic main effect (G) and the GE interaction contained in the multi-environment trial (MET) data. The GGE biplot can be used to effectively address the following questions. (1) Are there crossover GE interactions in the data? (2) Which cultivars are best for a given location environment? (3) Which locations are more suitable for a given cultivar? (4) Can the genotypes be meaningfully grouped? (5) Can the environments be meaningfully grouped? If multi-year MET data are available, (6) can the target environment be divided into different mega-environments? If not, (7) what are the more discriminating and representative test environments? (8) What are the higher yielding and more stable genotypes? These questions can be directly answered by examining a GGE biplot. In addition, if external information, i.e., covariables other than yield, about the genotypes and the test environments are available, the following question can also be addressed: (9) what are the traits/characteristics that make-up a superior cultivar? And (10) what are the environmental factors that make up a better test environment?; Another outcome of this study is the proposal of a new measure of cultivar performance---YREM, which is the yield relative to (i.e., divided by) the environmental maximum. It is a simple and intuitive measure of cultivar performance that is relatively independent of cultivar attendance. It provides quantitative criteria for selection/culling based on data from a single or multi-location trial in a single year.; Application of the GGE biplot technique and the concept of YREM to the 1989 to 1999 Ontario winter wheat performance trial data revealed the following insights into winter wheat in Ontario. (1) Crossover genotype by location (GL) interaction occurred every year; the loss of yield due to crossover GE interaction was as high as 26∼40% of the attainable yield. (2) The crossover GL interaction was largely unrepeatable, however. (3) Nevertheless, the yearly GL interaction patterns suggested that the Ontario winter wheat growing region could be divided into two mega-environments: Eastern Ontario as one and Western and Southern Ontario as the other. (4) Plant height and maturity were found to be the major genotypic causes of GE interaction, while temperatures in the winter (December to March) and summer (May to July) months are the major environmental causes of the GL interaction. In Southern and Western Ontario, shorter and earlier cultivars seemed to be more adapted; but in Eastern Ontario taller and later cultivars are more favored. (5) Resistance to various diseases, especially to septoria leaf blotch, was frequently found to contribute to higher average yield, and thus should be an important breeding objective. (6) Analysis using YREM indicated that the multi-year average YREM of adapted cultivars was >0.89. The power of a single year MET is to discard genotypes with average YREM 0.84 and to promote genotypes with average YREM > 0.94; The data of single trial can only be used to discard genotypes with YREM 0.60∼0.74.
机译:产量试验数据分析方法的改进是提高育种效率的重要方面。基于产量试验数据进行有效品种评估的关键是正确把握基因与环境之间相互作用的模式,并据此制定选择策略。这项研究的一项成果是开发了“ GGE双图”,该图以图形方式显示了多环境试验(MET)数据中包含的基因型主效应(G)和GE相互作用。 GGE双线图可用于有效解决以下问题。 (1)数据中是否存在交叉GE交互? (2)哪种品种最适合给定的定位环境? (3)哪个位置更适合给定品种? (4)基因型可以有意义地分组吗? (5)是否可以对环境进行有意义的分组?如果可获得多年的MET数据,(6)可以将目标环境划分为不同的大环境吗?如果不是,(7)更具区别性和代表性的测试环境是什么? (8)高产和稳定的基因型是什么?这些问题可以通过检查GGE双图直接回答。另外,如果可获得关于基因型和测试环境的外部信息,即除产量以外的协变量,那么还可以解决以下问题:(9)构成优良品种的特征/特征是什么? (10)构成更好测试环境的环境因素是什么?这项研究的另一个结果是提出了一种新的品种性能测量方法-YREM的建议,它是相对于(即除以)环境最大值的产量。这是对品种表现的一种简单直观的度量,相对独立于品种出勤率。它提供了基于一年中单项或多项试验的数据进行选择/剔除的定量标准。 GGE双线图技术和YREM概念在1989年至1999年安大略省冬小麦性能试验数据中的应用揭示了对安大略省冬小麦的以下见解。 (1)每年发生基于位置(GL)相互作用的交叉基因型;由于交叉GE相互作用而导致的产量损失高达可达到产量的26%至40%。 (2)但是,交叉的GL交互在很大程度上是不可重复的。 (3)尽管如此,年度GL交互作用模式表明,安大略省冬小麦种植区可分为两个大环境:一个为东部安大略省,另一个为西部和南部安大略省。 (4)发现植物高度和成熟是GE相互作用的主要基因型原因,而冬季(12月至3月)和夏季(5月至7月)的温度是GL相互作用的主要环境原因。在安大略南部和西部,较短和较早的品种似乎适应性更强。但在安大略省东部,较高和较晚的品种受到青睐。 (5)经常发现对各种疾病的抵抗力,特别是对黑斑病的抵抗力有助于提高平均产量,因此应该成为重要的育种目标。 (6)利用YREM的分析表明,适应品种的多年平均YREM> 0.89。一年MET的功能是丢弃平均YREM <0.84的基因型,并促进平均YREM> 0.94的基因型。单次试验的数据只能用于舍弃YREM <0.60〜0.74的基因型。

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