Methods to conduct and analyse weed surveys in arable farming: a review

被引:27
|
作者
Hanzlik, Kristin [1 ,2 ]
Gerowitt, Baerbel [1 ]
机构
[1] Univ Rostock, Fac Agr & Environm Sci, Grp Crop Hlth, Satower Str 48, D-18051 Rostock, Germany
[2] BASF AG, Carl Bosch Str 64, D-67117 Limburgerhof, Germany
关键词
Arable weed vegetation; Abundance; Cover; Diversity; Sampling; Multivariate statistics; Europe; Monitoring; Mapping; VISUAL COVER ASSESSMENTS; WINTER OILSEED RAPE; ENVIRONMENTAL-FACTORS; MULTIVARIATE-ANALYSIS; COMMUNITY COMPOSITION; SPECIES COMPOSITION; LANDSCAPE STRUCTURE; PLANT-COMMUNITIES; VEGETATION; FLORA;
D O I
10.1007/s13593-015-0345-7
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Arable weeds always accompany arable farming; however, species and abundances vary in space and change in time. Surveys assess weeds on a large spatial scale. The regional and national importance, the abundances in which weeds occur, is derived from surveys. Environmental and management background data assist to explain the weed vegetation. Arable weed surveys can have an agronomic, a botanical or an ecological purpose-we approach the agronomic viewpoint. Scientific value results from spatial data about the arable weed vegetation for modelling the dependencies and predicting future changes on a large scale. Researchers, botanists, farmers, advisors, herbicide manufacturers and other industry players engaged in weed control can get rationale spatial impressions of trends in the weed vegetation. This review focuses on methodical aspects of weed surveys. Forty-three surveys on farmed fields, mainly but not exclusively undertaken in Europe, done in up to nine crops per survey covering periods of 1 to 67 years and including 59 to 4423 fields were excerpted for suitable methods to conduct and analyse surveys. The methods reflect agronomic and other targets, e.g. from vegetation science. When accessing existing data, the interdependencies of the layout and all targets should be handled with care. When setting up surveys for agronomic purpose, it is worthwhile to include 3 years and apply stratified sampling. We recommend assessing abundances rather than presence only. Weed survey data is most suitable for applying multivariate statistics. We recommend using proven methods, without discouraging to explore others. To collect sound explanatory data demands for robust contacts with land managers. Explanatory variables are important in survey analyses to extract knowledge about the drivers of the weed vegetation. We conclude that extra value can be obtained from existing weed survey data. We propose to setup public-private partnerships as a way to carry on with arable weed surveys.
引用
收藏
页码:1 / 18
页数:18
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