农田杂草种子库动态及其生态学意义

研究土壤杂草种子的杂草种类组成、数量、分布及动态,对于研究杂草种群的消长规律、预测杂草群落的演替具有重要意义,可为持久稳定地控制农田杂草发生与危害提供科学依据。详细总结了农田杂草种子库方面的国内外研究进展,并对今后在农田杂草种子库的研究应当关注的问题进行了探讨。     

花生田杂草发生危害状况与防除技术研究进展

对目前中国花生田主要杂草的种类、分布、发生规律以及防除技术的研究进展进行了阐述和分析,旨在为更加安全、高效地防除花生田杂草提供参考和建议。展望了未来各花生生产区杂草的全面普查、主要杂草的发生规律、土壤杂草种子库、杂草可持续治理技术等研究方向。     

农田主要杂草的防除技术方法

冬春作物是西藏农作物种植结构中面积最大,而防除农田主要杂草是作物增产的重要因素,为此作者经过几年的实践与研究,进而提出了适宜防除当地农田主要杂草的技术方法及几项措施.     

江苏省农业科学院农田草害项目组

农田草害项目组主要从事农田杂草的发生规律、杂草群落演替、土壤杂草种子库、除草剂配方筛选及配套应用技术研究;除草剂药效、药害与环境条件的关系、杂草抗性、植物化感等多学科综合性研究;并应用化学、生物、     

不同耕作方式对烟草—水稻轮作农田杂草种子库的影响

为明确不同耕作方式下烟草—水稻轮作农田杂草种子库的物种组成、季节和空间垂直分布规律。采用杂草种子萌发法,调查烟稻轮作田免耕、翻耕2种耕作模式下烟草—水稻农田0～15 cm土层土壤杂草种子库组成及特征。2017—2018年试验统计结果表明,免耕烟稻轮作田共有杂草13科27种,翻耕烟稻轮作田共有杂草13科25种,有22种杂草在翻耕和免耕烟稻轮作田均有分布。有5种杂草只在免耕田出现,有3种杂草只在翻耕田出现。在试验中发现7种主要杂草种子,其中,马唐、无芒稗和狗尾草是不同季节杂草种子库的共同优势物种。免耕处理下杂草总密度大于翻耕处理下杂草总密度,翻耕处理中优势物种在种子库种子中所占比例高于免耕处理。春季和秋季是杂草种子库杂草种子发生的高峰期。免耕对杂草种子库的垂直分布影响明显,免耕杂草种子主要集中于土壤上层(0～5 cm),翻耕处理对杂草种子库的垂直分布影响很小,翻耕处理下杂草种子库密度没有产生显著性差异。研究结果明确了不同耕作模式土壤杂草种子库的物种组成、季节分布和垂直变化规律,为今后杂草的管理和控制提供理论依据。     

农田土壤种子库特征的研究

为进一步了解农田土壤种子库的生态学特征,采用土壤种子萌发法对吕梁市离石区6个农田样本土壤种子库展开研究。结果显示土壤中杂草种子多达40种,种类最多的为禾本科,占杂草种类总数的78.3%;其次有苋科、豆科、十字花科和菊科等。本研究对有效防治农田非目标作物,增加目标作物的产量有一定意义。     

浅析农田杂草调查与防除的研究

通过对农田杂草进行准确的调查研究为除草剂的推广应用、杂草防除措施的制定、杂草的综合防除提供可靠的依据。     

杂草农业防除技术

目前农田杂草的防除越来越依赖化学除草剂,不可避免地出现了环境污染、生态破坏、杂草产生抗药性等问题。采用农业措施防除杂草具有环保、经济、高效的优点,总结了杂草农业防除技术,旨在促进其推广应用。     

农田杂草的防除技术

本文主要介绍了农田杂草防除的综合措施,以及玉米田和水稻田杂草的化学防除技术等。     

应用农业防控技术控制农田杂草

本文详细介绍了应用农业防控技术控制农田杂草,为农田杂草的防除提供参考。     

麦田杂草防治技术

分析麦田杂草的危害及技术,并提出杂草防治关键技术。     

7种除草剂防除带田玉米杂草试验

对7种除草剂防除小麦／玉米带田玉米杂草的试验结果表明，7种药剂在试验剂量下对玉米发芽出苗、生长发育及小麦带小麦安全，可有效地控制玉米杂草；其中以50％玉米膜草净悬浮剂和18．9％玉米田灭草净可湿性粉剂的除草效果较好，药后50d株防效分别为83．30％、72．14％。鲜重防效分别为72．43％、80．56％，有广泛的应用前景。     

杂草鉴别专家系统

杂草鉴别专家系统包括杂草鉴定、信息查询、杂草处方和系统维护等模块．用户可采用多种鉴定方式,系统给出鉴定结果并列出其性状描述以及彩色模式图,以资对照确认．用户还可以查询各种除草信息并输出最佳的除草处方．采用 Ｖｉｓｕａｌ Ｆｏｘｐｒｏ ５．０ 编程,利用可视化与面向对象编程技术得到良好的界面,操作方便,系统易于修改和扩充     

Predicting Weed Species Occurrence Based on Site Properties and Previous Year's Weed Presence

Probabilities of Setaria spp., Solanum ptycanthum, Helianthus annuus and Abutilon theophrasti occurrence were predicted based on two site property factors and weed species presence in a previous year using logistic regression models. Weed seedling surveys were conducted just prior to post-emergence weed management in two grower-managed fields in the central Platte River Valley of Nebraska, USA at Alda in 1995 and 1996 and at Shelton in 1994, 1995 and 1997. Weed species density data were re-classified as present or absent at each pair of points on the sampling grid, representing quadrat locations either in the pre-emergence herbicide band or between the crop rows. Site property data were collected in March 1995 at Alda and March 1994 at Shelton. Using factor analysis, two independent factors were derived from correlated attributes of relative elevation, percent organic carbon, pH, nitrate, phosphate, and soil texture measured at Alda. Logistic regression models were estimated and parameterized for each weed species at Alda in 1996 based on the two factors (topography and soil type and soil fertility status) and weed species presence in 1995. Performance of these models for each weed species was evaluated using the independent data set from Shelton. Between and on crop row Setaria spp. and Solanum ptycanthum models described these populations at Alda. At Shelton, on row Setaria spp. occurrence and between row Solanum ptycanthum occurrence were adequately predicted. Helianthus annuus or Abutilon theophrasti occurrence was not well predicted even with knowledge of their presence in the previous year, probably as a result of low actual occurrence within a given year. Maps of predicted occurrence have value in directing weed scouting to field locations where the species is most likely to occur.     

田间杂草识别与除草技术智能化研究进展

随着计算机与电子技术的发展,田间杂草识别与除草技术逐步走向智能化,自动除草系统成为国内外研究热点。本文对组成田间杂草识别与自动除草系统的4个主要模块(系统导航模块、图像采集与处理模块、杂草识别模块和除草控制模块)分别进行了综述和分析,并指出目前研究中存在的问题及今后努力的方向。     

Two-Year Weed Seedling Population Responses to a Post-Emergent Method of Site-Specific Weed Management

Field experiments were conducted to determine how a site-specific weed management practice in Zea mays L. influenced the numerical and spatial distribution of a naturally occurring weed infestation in Z. mays and the succeeding Beta vulgaris L. crop. Compared to conventional broadcast herbicide applications, site-specific herbicide applications reduced herbicide load by 11.5 and 98.0% in two separate Z. mays fields. The broad range in outcomes was attributed to the spatial aggregation and density of target weed populations. While herbicide use was successfully reduced at field locations with low weed density, most survivors of multiple control tactics were in locations with the highest initial density. A greater understanding of interactions between weed management and weed density would increase the likelihood that site-specific weed management offers long-term improvements over conventional approaches.     

不同除草方式对稻田杂草群落及其多样性的影响

为了解不同除草方式[人工除草(A_(01))和化学除草(B_(01))]稻田杂草群落和生物多样性的现状,2016年对新疆伊犁河谷2种不同除草方式稻田杂草重要值、生态位宽度和生态位重叠值进行研究。结果表明:A_(01)和B_(01)稻田杂草种类分别有10目11科12属14种和6目7科10属11种,2种模式杂草群落结构类似,为阔叶类杂草+莎草科杂草+禾本科杂草;A_(01)重要杂草和主要杂草为B_(01)的1.33倍和1.50倍;A_(01)扁秆藨草生态位宽度(0.838 2)最大,B_(01)长芒稗生态位宽度(0.895 9)最大;B_(01)中长芒稗、空心莲子草、扁秆藨草生态位宽度均高于0.6且长芒稗-空心莲子草(A_(01):0.814 8,B_(01):0.790 9)、长芒稗-扁秆藨草(A_(01):0.838 0,B_(01):0.735 3)和空心莲子草-扁秆藨草(A_(01):0.967 9,B_(01):0.614 5)的生态位重叠值也均高于0.6,说明不同除草方式下,稻田杂草种间竞争格局发生改变,长芒稗、空心莲子草和扁秆藨草逐步演替为调查区主要杂草且其在B_(01)稻田表现出抗药性。     

空心莲子草的可利用价值

介绍了外来入侵物种空心莲子草的可利用价值,空心莲子草具有较广泛的应用前景。     

Weed Detection Using Canopy Reflection

For site-specific application of herbicides, automatic detection and evaluation of weeds is desirable. Since reflectance of crop, weeds and soil differs in the visual and near infrared wavelengths, there is potential for using reflection measurements at different wavelengths to distinguish between them. Reflectance spectra of crop and weed canopies were used to evaluate the possibilities of weed detection with reflection measurements in laboratory circumstances. Sugarbeet and maize and 7 weed species were included in the measurements. Classification into crop and weeds was possible in laboratory tests, using a limited number of wavelength band ratios. Crop and weed spectra could be separated with more than 97% correct classification. Field measurements of crop and weed reflection were conducted for testing spectral weed detection. Canopy reflection was measured with a line spectrograph in the wavelength range from 480 to 820 nm (visual to near infrared) with ambient light. The discriminant model uses a limited number of narrow wavelength bands. Over 90% of crop and weed spectra can be identified correctly, when the discriminant model is specific to the prevailing light conditions.