防除多花黑麦草等4种禾本科杂草的药剂活性测定

为了系统研究多花黑麦草、早熟禾、碱茅和棒头草等4种小麦田禾本科杂草的防除药剂活性,在温室内采用盆栽法研究了8种除草剂对小麦田多花黑麦草等4种禾本科杂草的生物活性。结果表明,乙酰乳酸合成酶(ALS)抑制剂啶磺草胺对早熟禾有很好的防除效果,田间推荐剂量下防效达到93.06%(防效均为田间推荐剂量下,下同),对碱茅、多花黑麦草和棒头草的效果也较好,防效在82.82%~86.89%之间,另2种ALS抑制剂氟唑磺隆和甲基二磺隆对早熟禾的防效较好,在61.27%~86.71%之间,但对其他3种杂草碱茅、棒头草和多花黑麦草的防效很差,防效仅在10.47%~29.43%之间。乙酰辅酶A羧化酶(ACCase)抑制剂唑啉草酯、肟草酮和炔草酯对碱茅、棒头草和多花黑麦草的防效均较好,在85.41%~100.00%之间,但对早熟禾的防效均较差,在19.08%~60.69%之间;另一种ACC抑制剂精噁唑禾草灵对棒头草防效为99.60%,对碱茅和多花黑麦草防效分别为72.00%和55.00%,对早熟禾仅为16.18%;植物光合系统Ⅱ抑制剂异丙隆对早熟禾、棒头草和碱茅的防效均较好,田间推荐剂量下防效在88.15%~96.53%之间,对多花黑麦草的效果略差,为67.43%。     

Modeling Invasive Annual Grass Abundance in the Cold Desert Ecoregions of the Interior Western United States

Invasive annual grasses, primarily Bromus tectorum, are a severe risk to native vegetation of the intermountain West. Once established, annual grasses alter natural fire regimes and outcompete natives until, in some places, they become the overwhelming dominant. We developed a regional spatial model encompassing eight ecoregions to indicate the relative abundance of invasive annual grass at five levels of canopy cover. We used field sample data representing invasive annual grass abundance to build and calibrate the model. Explanatory variables, represented as map inputs, included image indices, climate, landform, soil, and human-induced surface disturbance. As a novel modeling approach, we built multiple models based on classes of invasive annual grass cover abundance were developed individually and then combined into a final 90-m pixel resolution model that indicates locations relative to invasive annual grass abundance into classes of < 5%, 5−15%, 16−25%, 26−45%, and > 45% cover. Each component model was validated using held-out sample data, and relative accuracy was 86%, 74%, 62%, 62%, and 60%, respectively, with an overall kappa of 0.773. The Columbia Plateau, Northern Basin and Range, and Snake River Plain ecoregions appear to have the greatest overall proportions (48−62%) mapped within at least one of the invasive cover categories. Overlay of the resulting model with major vegetation types indicated > 50 major vegetation types that are affected by current distribution of annual grasses and are at risk of expansion. Among these, Intermountain Basins, Big Sagebrush Steppe, and Columbia Plateau Steppe and Grassland each consistently scored high for invasive risk where they occur. Spatial models of this type should assist with rangeland restoration and for decisions involving placement of infrastructure, vegetation treatments where further surface disturbance could trigger additional cheatgrass expansion. Options exist for extending this model, using climate projections over upcoming decades, to indicate areas of increasing risk for invasion.     

福州登云高尔夫球场草坪杂草种类及分布

在建造登云高尔夫球场期间，调查了球场造型现场、草坪建植期间和造成坪后不同功能区、不同要草发生种类及其群落组成类型。结果表明，造型现场以一年生禾本科杂草为主，草坪建植以一年生禾本科和菊科菊草为主；草坪成坪后，果岭和发玲台以一年生杂草为主，但密度不大，危害不严重；球道以香附子密度最大，出现频最高，分别为２３．１株／ｍ＾２和５５％，危害严重；长草区杂草种类多，密度大。     

青海格尔木地区草地有毒植物危害及防治

在进行调查的基础上 ,对格尔木地区草地危害较大的主要有毒植物小花棘豆 ,醉马草 ,达乌里龙胆的分布状况 ,引起牲畜中毒的症状 ,中毒处理方法等进行综合整理 ,提出防治对策。     

Modellversuch zur Beschreibung von Konkurrenzvorgängen zwischen Sommerweizen und einer aus Capsella bursa-pastoris L., Stellaria media L. und Alopecurus myosuroides Huds. bestehenden Mischverunkrautung

Investigation to Describe the Competition between Spring Wheat and a Weed Population Consisting of Capsella bursa-pastoris L., Stellaria media L. and Alopecurus myosuroides Huds.
When controlling weeds in cereals using economic threshold as criteria, weed populations must be evaluated for their potential to cause damage at a very early stage. As well as information of actual weed density or weed ground cover, further knowledge about the species specific growth and competition between weed populations is important.
For this purpose the growth and competition between the weeds Capsella bursa-pastoris L., Stellaria media L., Alopecurus myosuroides Huds. and spring wheat ( Triticum aestivum L.) as a model of a typical weed mixture in cereals were studied in a container experiment. The growth with time of individual species was documented using true colour image processing. In that these measurements are non-invasive, the system lends itself specially to time-dependent analyses. The increasing amount of ground covered by leaves between two observation times served as a parameter to evaluate the increase in biomass over this time period and could be used as a evaluation of the competitiveness of the weed species studied.
The results can be summarized as follows: 1) Stellaria media L. proved to be the most competitive of the studied species. 2) The competition to spring wheat from each weed species was similar. 3) A multi species weed population had a tendenci to a lower competitiveness to spring wheat, than a weed population of only-one species.
The importance of this and other similar studies for the improvement for the prediction of economic weed thresholds to estimate weed populations in early stages will be indicated.     

7种土壤处理除草剂对冬小麦田杂草的防治效果

为了探明7种除草剂土壤喷雾施药对冬小麦田一年生杂草的防治效果与对冬小麦的安全性,参照《农药田间药效试验准则》相应方法于田间比较了施药前浇水与施药后浇水两种模式下防效与安全性的差异。结果表明,药剂在施药后浇水处理模式下对杂草总体的防效优于施药前浇水处理模式;试验剂量下,施药后浇水模式中氟噻草胺药后150天对猪殃殃、播娘蒿、看麦娘3种杂草的总体株数防效和鲜重防效均高于95%,噁草酮有效量720 g/hm²处理对播娘蒿、看麦娘的鲜重防效高于95%,对猪殃殃的鲜重防效则高于85%。从安全性、防效及除草剂减量角度综合分析,推荐在小麦田使用氟噻草胺和噁草酮进行土壤封闭处理,一次施药即有理想的防除效果。     

基于Mask R-CNN的玉米田间杂草检测方法

针对田间复杂环境下杂草分割精度低的问题,提出了基于Mask R-CNN的杂草检测方法。该方法采用残差神经网络Res Net-101提取涵盖杂草语义、空间信息的特征图;采用区域建议网络对特征图进行杂草与背景的初步二分类、预选框回归训练,利用非极大值抑制算法筛选出感兴趣区域;采用区域特征聚集方法(Ro IAlign),取消量化操作带来的边框位置偏差,并将感兴趣区域(Ro I)特征图转换为固定尺寸的特征图;输出模块针对每个Ro I计算分类、回归、分割损失,通过训练预测候选区域的类别、位置、轮廓,实现杂草检测及轮廓分割。在玉米、杂草数据集上进行测试,当交并比(Io U)为0. 5时,本文方法均值平均精度(m AP)为0. 853,优于Sharp Mask、Deep Mask的0. 816、0. 795,本文方法的单样本耗时为280 ms,说明本文方法可快速、准确检测分割出杂草类别、位置和轮廓,优于Sharp Mask、Deep Mask实例分割算法。在复杂背景下对玉米、杂草图像进行测试,在Io U为0. 5时,本文方法 m AP为0. 785,单样本耗时为285 ms,说明本文方法可实现复杂背景下的农田作物杂草分割。在田间变量喷洒试验中,杂草识别准确率为91%,识别出杂草并准确喷雾的准确率为85%,准确喷药的杂草雾滴覆盖密度为55个/cm2,装置对每幅图像的平均处理时间为0. 98 s,满足农药变量喷洒的控制要求。     

Quantifying rice yield gaps and their causes in Eastern and Southern Africa

The demand for rice in Eastern and Southern Africa is rapidly increasing because of changes in consumer preferences and urbanization. However, local rice production lags behind consumption, mainly due to low yield levels. In order to set priorities for research and development aimed at improving rice productivity, there is a need to characterize the rice production environments, to quantify rice yield gaps—that is, the difference between average on-farm yield and the best farmers’ yield—and to identify causes of yield gaps. Such information will help identifying and targeting technologies to alleviate the main constraints, and consequently to reduce existing yield gaps. Yield gap surveys were conducted on 357 rice farms at eight sites (19–50 farmers per site) across five rice-producing countries in Eastern and Southern Africa—that is Ethiopia, Madagascar, Rwanda, Tanzania and Uganda—for one or two years (2012–13) to collect both quantitative and qualitative data at field and farm level. Average farm yields measured at the eight sites ranged from 1.8 to 4.3 t/ha and the average yield gap ranged from 0.8 to 3.4 t/ha. Across rice-growing environments, major causes for yield variability were straw management, weeding frequency, growth duration of the variety, weed cover, fertilizer (mineral and organic) application frequency, levelling and iron toxicity. Land levelling increased the yield by 0.74 t/ha, bird control increased the yield by 1.44 t/ha, and sub-optimal management of weeds reduced the yield by 3.6 to 4.4 t/ha. There is great potential to reduce the current rice yield gap in ESA, by focusing on improvements of those crop management practices that address the main site-specific causes for sub-optimal yields.     

病虫草危害对稻米品质的影响

大力发展优质稻米产业已成为农业科研和生产的热点。影响稻米品质的因素除了品种因素、环境因素、栽培措施等外,病虫草危害也是一个重要方面。概述了病虫草危害对稻米外观品质、碾磨品质、食味品质、营养品质和卫生品质的影响。建议采用以“种苗处理+生态调控+应急防控”为核心的病虫草害绿色防控技术,能有效控制病虫草危害、保障粮食产量安全、提高稻米品质。