大豆杂草管理技术的多元判别分析

IF 0.8 Q3 Agricultural and Biological Sciences

Planta Daninha Pub Date : 2020-01-01 DOI:10.1590/s0100-83582020380100077

A. Bianchini, Pedro V.D. Moraes, Solon J. Longhi, Paulo F. Adami, Patricia Rossi, Vanderson V. Batista

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引用次数: 0

摘要

背景:对涉及不同处理的实验产生的信息进行分析，可以通过多元统计分析技术，如判别分析，来分析从预定义组中获得的数据。目的:通过判别分析，验证不同覆盖作物(麦草、藜麦、菊苣和休耕地)处理对主要作物大豆产量的影响。方法:采用刈割、施用草甘膦或施用百草枯等不同的管理技术对覆盖作物进行杂草控制。试验设计采用4 × 3 × 2因子全随机分组设计，设4个重复:因子a:(处理)小麦、藜麦、藜麦覆盖作物和休耕地;因子B:(管理)地块被细分，施用百草枯或草甘膦，或割除覆盖植物;因素C:地块被细分，并通过一到两次出苗后除草剂的施用进行管理。为了评估不同管理技术和处理的正确分类的百分比，制定了一个数据矩阵，用于评估与大豆作物有关的变量，并将数据标准化为log log 10 log (n;10）. 采用Fisher线性判别法进行多变量分析。结果:判别分析选择了4个具有判别力的变量，分别与藜麦、藜麦、藜麦和休耕有关，占解释方差的100%。结论:以燕麦作为覆盖作物可以提高大豆产量，而在管理方面，所有覆盖作物使用草甘膦进行杂草控制效果最好。[3][植物学报]，[A]， [j]。大豆的多因素分析[j] .大豆学报，2020;38:e020210864 https://doi.org/10.1590/S0100-83582020380100077 2/8

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Multivariate analysis using a discriminant method for evaluating the techniques of weed management in soybean crop

Background: The analysis of information generated from experiments involving different treatments, can be done by multivariate statistical analysis techniques, such as discriminant analysis, to analyze data obtained from predefined groups. Objective: Verify, through discriminant analysis, the differences among cover crop (Avena strigosa, Chenopodium quinoa, Cichorium intybus, and fallow land) treatments with respect to main crop soybean yield. Methods: For weed control, these cover crops were subjected to different management techniques, namely mowing, the application of glyphosate or the application of paraquat. The experimental design consisted of completely randomized blocks in a 4 × 3 × 2 factorial scheme, with four replications, consisting of the following factors: Factor A: (treatment) cover crops of A. strigosa, C. quinoa, C. intybus, and fallow land; Factor B: (management) plots were subdivided and treated with the application of paraquat or glyphosate, or the mowing of cover plants; Factor C: the plots were sub-subdivided and managed by one or two applications of a post-emergence herbicide. In order to evaluate the percentage of correct classifications of the different management techniques and treatments, a data matrix was elaborated for evaluation of variables relating to the soybean crop and the data were standardized by log log 10 log (n; 10). Multivariate analysis was performed using Fisher's linear discriminant method. Results: Discriminant analysis selected four variables with discriminatory power relating to the A. strigosa, C. quinoa, C. intybus and fallow, which contributed to 100% of the explained variance. Conclusions: Treatment with oats used as a cover crop provided higher soybean crop yield, whereas in terms of management, weed control using glyphosate provided the best results with all cover crops. SBCPD | Planta Daninha Bianchini A, et al. Multivariate analysis in soybean Planta Daninha 2020;38:e020210864 https://doi.org/10.1590/S0100-83582020380100077 2/8

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来源期刊

Planta Daninha Agricultural and Biological Sciences-Plant Science

自引率

0.00%

发文量

审稿时长

16 weeks

期刊介绍： Planta Daninha is a scientific journal published by the Brazilian Society of Weed Science (SBCPD - Sociedade Brasileira da Ciência das Plantas Daninhas). Papers submitted for publication must be sent through an electronic system, on http://www.scielo.br/pd. Works may be written in Portuguese, English, or Spanish, and will be accepted after being reviewed and approved by the Editorial Board. Only papers that have not been published or submitted for publication in other media will be accepted. Articles in Portuguese will be translated to English after being properly corrected and authorized by the authors. Planta Daninha has with goal to publish genuine technical-scientific papers and literature reviews from a critical perspective on Biology, weed management, and related topics.