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Repeated grading of weed abundance and multivariate methods to improve the efficacy of on-farm weed control trials

机译:杂草丰度的重复分级和多变量方法可提高农场杂草防治试验的功效

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摘要

We evaluated whether new information could be drawn from additional data collection and unconventional statistical analyses of an on-farm trial. First, we compared a conventional sampling method using a biomass estimate of weed abundance to repeated visual assessment of the percentage ground cover of weeds. The biomass was sampled once after the treatment, whereas the ground cover was repeatedly sampled once before weed control plus several occasions after weed control. Second, we contrasted the outcomes from analysis of variance (ANOVA), taking samples from a single point in time with repeated measures (rm)ANOVA and a multivariate method. As the outcomes and conclusions drawn were relatively similar, we conclude that the ground cover estimate of weed abundance was as reliable as the biomass estimate. The rmANOVA enabled us to follow the temporal trend in response to treatments in the most abundant species, including possible initial differences. Multivariate analysis went even further, by clearly displaying species-wise responses and treatment selectivity.
机译:我们评估了是否可以从农场试验的其他数据收集和非常规统计分析中获得新信息。首先,我们比较了使用杂草丰度的生物量估计值和重复观察杂草地面覆盖百分率的常规采样方法。处理后对生物量进行一次采样,而除草之前以及除草后多次重复对地被植物进行一次采样。其次,我们对方差分析(ANOVA)的结果进行了对比,采用重复测量(rm)ANOVA和多元方法从单个时间点取样。由于得出的结果和结论相对相似,因此我们得出结论,杂草丰度的地面覆盖估计与生物量估计一样可靠。 rmANOVA使我们能够顺应时间趋势,响应最丰富物种的处理,包括可能的初始差异。通过清楚地显示物种反应和治疗选择性,多变量分析甚至走得更远。

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