Exotic plants can selectively recruit beneficial microorganisms, such as arbuscular mycorrhizal fungi (AMFs) and Bacillus spp., during their invasion process to enhance growth and competitiveness by improving nutrient absorption and strengthening defense capabilities against herbivores. However, research in the context of invasive plants remains limited. In this study, a greenhouse pot experiment was conducted to examine the effects of different treatments on the growth and defense of Ageratina adenophora. The treatments included no inoculation, inoculation with Bacillus thuringiensis (BT), inoculation with arbuscular mycorrhizal fungus (Claroideoglomus etunicatum, CE), dual inoculation with BT and CE (BT + CE), and the presence or absence of Procecidochares utilis. The results showed that both CE and BT + CE significantly enhanced nutrient concentration and promoted the growth of A. adenophora. The aboveground biomass increased by 35.48 and 53.38% under non-parasitism and by 68.03% and 103.72% under the parasitism of P. utilis for these two treatments, respectively. In comparison to the control P. utilis-parasitized A. adenophora, the BT, CE, and BT + CE treatments significantly increased protective enzyme activity, jasmonic acid concentration, and secondary metabolites. Our study indicates that the recruitment of B. thuringiensis in the rhizosphere of A. adenophora can enhance its defense ability, while C. etunicatum improved both growth and defense ability. The interaction effects of these two microorganisms enhances the regulation of growth and defense ability of A. adenophora against P. utilis parasitism, providing insights into the feedback effects of beneficial microorganisms on the interactions between invasive plants and biological control.

Rice planthoppers (RPH) are important pests that cause severe yield losses in rice production in China. The widespread and intensive use of insecticides to prevent RPH causes serious issues. Forecasting the population dynamics of RPH is an important part of integrated pest management, which helps to prevent impending RPH outbreaks and reduce the use of insecticide. However, forecasting RPH population dynamics is very challenging as it results from complex spatial and temporal dispersion processes impacted by many factors, such as source populations, meteorological conditions, and host plant characteristics. Therefore, a novel deep learning-based model using graph convolutional network (GCN) and long short-term memory network (LSTM) is proposed in this study to capture RPH population dynamics. This model includes one dynamic GCN (DGCN) for modeling source populations and two attention-based LSTM encoder-decoder network (ALSTM) for modeling meteoro-logical and host plants, respectively. The proposed model can dynamically aggregate the outputs of one DGCN and two ALSTM based on data to produce the final output for each county. The proposed model is evaluated on the dataset collected in South and Southwest China during 2000-2019, which includes populations, meteoro-logical and host plant factors. The results demonstrate that DGCN-ALSTM outperforms other state-of-art deep learning methods and traditional time series forecasting approaches. The improvement brought by DGCN-ALSTM are about 1.95 %, 1.32 %, and 1.60 % for brown planthopper population, and 1.79 %, 0.71 %, and 0.32 % for white back planthopper population according to the R, RMAE, and RRMSE take DGCN as the baseline, respec-tively. The proposed DGCN-ALSTM provides a new better tool to forecast RPH population dynamics and the accurate forecasting results can guide local plant protection organizations to develop detailed sustainable pest control strategies and methods to control RPH in time.

Early forecasting of rice panicle blast is critical to the management of rice blast. To develop early forecasting models for rice panicle blast, the relationship between the seasonal maximum incidence of rice panicle blast (PBx) and the PBx in the preceding crop, weather conditions, location, and acreage of susceptible varieties was analyzed. Results revealed that PBx in the preceding crop, acreage of the susceptible varieties in class (SVC), altitude, weather conditions 120 to 180 days before the PBx date (dbPBx) and 30 to 90 dbPBx were significantly correlated with the PBx. Subsequently, a logistic model and a two-step hurdle model were developed to predict rice panicle blast. The logistic model was developed to predict whether the PBx was 0 or not based on the preceding PBx, altitude, acreage of susceptible varieties, the longest stretch of days with soil temperatures between 16 and 24°C for the period 120 to 150 dbPBx, and the longest stretch of rainy days in the period 120 to 180 dbPBx. The hurdle model predicted if the PBx was greater than 0 at the first step, and if the prediction was greater than 0, then a regression model was developed for predicting PBx based on the preceding PBx, SVC, altitude, and weather data 180 to 30 dbPBx. Validation with the test datasets showed that the logistic model could correctly predict whether PBx was 0 at a mean test accuracy of 78.39% and that the absolute prediction error of PBx by the two-step hurdle model was smaller than 6.16% for 90% of the records. The model developed in this study will be helpful in management decisions for rice growers and policy makers and offer a useful basis for further studies on the epidemiology and forecasting of rice panicle blast.

红火蚁是全球公认的百种最具危险性入侵物种之一.本文分析了云南省红火蚁发生分布传播特点,从压实各方防控责任、建立科学防控机制、加强调运检疫监管、强化疫情监测防控、广泛开展宣传培训等方面,总结了云南省阻截防控红火蚁的主要做法;分析了疫情防控工作存在的防控经费投入不足、防控组织化程度低、防治规范化落实难、防控技术储备欠缺等主要问题,提出加大资金投入、完善防控策略、开展技术研究、研究根除案例、加强宣传培训等科学防控红火蚁的对策建议.

探究使用植物生长调节剂0.136％赤·吲乙·芸薹可湿性粉剂对水稻全生育期常规施药的增效及促进水稻生长作用.结果表明,在常规药剂处理加入0.136％赤·吲乙·芸薹可湿性粉剂3 g/667m2,对于水稻秧苗期株高、根长、鲜重的促生率分别为7.2％、17.8％、19.5％,除草剂对杂草的30 d株防效、50 d鲜重防效分别为89.7％、87.8％,对于水稻蜡熟期株高、穗长的促生率分别为4.1％、11.6％,理论产量增加9.6％,均显著高于不加赤·吲乙·芸薹的常规药剂处理.另外,加入助剂安融乐3 mL/667m2并减药20％的处理对杂草的50 d鲜重防效为87.4％,对于水稻蜡熟期株高、穗长的促生率分别为4.0％、11.4％,理论产量增加5.0％,也均显著优于常规药剂处理.

Rice planthoppers (RPH) are important pest that cause severe yield losses of rice production in China. South and Southwest China are the main infestation areas on the annual spread of RPH. Identifying the spatial and temporal patterns in migration and subsequent development can provide insight into underlying mechanisms driving the spread of RPH and assist governments with prioritizing areas to achieve proactive management and prevention. Across rice production regions in South and Southwest China, RPH population were recorded by light traps and field surveys at 195 counties from March to October in 2000 to 2019. We first measured the spatial patterns in RPH immigrant populations and field populations with spatial autocorrelation analysis. Then, spatial hotspot analysis was undertaken to highlight dense RPH regions, and significant hot spots were further extracted for comparing the spatial and temporal patterns according to RPH species. The results revealed that RPH population were highly aggregated in space over large geographical distances up to 700 km approximately. Geographic patterns and hot spots of populations varied substantially with time and species, and various spatial patterns might be determined by inherent properties of RPH and rice eco-systematic. Overall, these results provided essential information to improving and optimizing the monitoring network for RPH. The findings from this research may provide helpful information to enhance proactive management against RPH and offer sustainable management practitioners new opportunities to design, develop, and implement optimal pest control strategies to protect rice production in China.

分析了广东省2010-2019年各地稻瘟病的发生情况,结合地域方位、稻瘟病发生面积、水稻种植面积等,对广东水稻主栽县(市、区)进行了稻瘟病级别划定.结果显示,广东稻瘟病重发区主要集中在粤北.另外,粤西湛江、茂名、阳江和云浮等部分地区呈点状重发分布;零星发生区主要集中在粤东、珠江三角洲和粤西部分地区.

Northern China is a main winter wheat production area and plays an important role in ensuring food security. Wheat aphids, as one kind of main agricultural pest, threaten wheat production. Based on wheat aphids disaster and prevention data, planting area and yield loss of wheat, growth period of winter wheat, and daily meteorological data at 561 observation stations from 1958 to 2018 in 8 main wheat production provinces of northern China, relationships between surface meteorological factors and the occurrence area of wheat aphids for every province in North China and Huanghuai area are fully analyzed using methods of correlation analysis, principal component analysis and stepwise regression analysis in various time-periods from last December to 10 June. Eight key meteorological factors which affect the occurrence area of wheat aphids in North China and 6 key meteorological factors for Huanghuai area are determined. The climate disaster indices of wheat aphids are established based on the normalized key meteorological factors and validated in 8 provinces. Furthermore, climatic risk aspects are assessed to explore the occurrence tendency of winter wheat aphids in northern China. The frequencies of different-level decadal climate disasters are taken as hazard index, the ratio of occurrence area of wheat aphids to the wheat-sown area is defined as vulnerability index, and the ratio of controlled area to occurrence area is calculated to measure the disaster prevention and mitigation capability.Comprehensive risk index is built by integrating hazard, vulnerability, disaster prevention and mitigation capability indexes to assess risk development trend in decades. The results show that the climatic hazard for wheat aphids tends to increase gradually and there are significant differences in different decades. The vulnerability for wheat aphids tends to be severe over time.The disaster prevention and mitigation capability for wheat aphids tends to improve gradually especially in the 1990s, and the trend slows down since 2001. The comprehensive climatic risk has been more severe and their scopes of the highest risk have been larger since the 1990s. The climatic risk is highest in Beijing, Tianjin, central-south Hebei, part of north Shandong. And it's the second highest in most of Shandong, north Henan, eastern and southern Shanxi, and north Jiangsu area, where effective measures should be taken to reduce the detriment of wheat aphids.

2020年7月-10月在我国南方3大稻区的6省(区)12个点开展了稻纵卷叶螟成虫种群动态的食诱监测试验,并与性诱、灯诱和田间赶蛾等传统监测技术进行了对比.结果表明,食诱监测可准确反映田间稻纵卷叶螟种群数量动态,其峰次、峰期与性诱、灯诱和田间赶蛾基本一致.4种监测方法在长江中下游单季晚稻区、单季中稻区和华南双季晚稻区同期监测到3个、2个和1～2个峰次,峰期依次为7月中下旬、8月上中旬和9月中旬.从日均诱蛾量和峰日蛾量看,食诱监测总体优于性诱,但差于灯诱和田间赶蛾;食诱监测的专一性较高,靶标数量占总诱虫量的比率一般高于85％,但不及性诱(95％以上);从雌、雄蛾的总量对比及逐日对比看,食诱监测性比均大于1.综合分析表明,食诱监测具有使用方便、反应灵敏、专一性较强、雌雄同诱的优点,为精准监测稻纵卷叶螟成虫种群动态提供了新手段.

简要介绍了韩国亲环境农业在提升农业生产能力、提高农民生产积极性、提升农产品质量等方面的发展经验,以及在智能农场循环农业研究、热带作物种植技术研究、未来农业专业人才培养等领域的先进理念和技术.结合国内发展现状,提出了3项发展建议:加强农业科技人才培养,推进植保体系建设;完善农业补贴制度,提高农民种粮积极性;加强宣传引导监管,促进绿色农业发展.

The forecasting and early warning technology of meteorological suitability of wheat aphids in the main growing areas can provide a scientific basis for disaster prevention and high yield. Based on data of the occurrence area of wheat aphids, winter wheat growth period and daily meteorological data at 601 observation stations from 1958 to 2015 in 8 main wheat production provinces of the northern China, relationships between surface meteorological factors and the occurrence area of wheat aphids for every province in North China and Huanghuai Area are fully analyzed using methods of correlation analysis, principal component analysis and stepwise regression analysis in various time-periods from last December to 10 June. Results indicate that the key meteorological factors which affect the occurrence area of wheat aphids in North China are average air temperature of last winter and in the first ten days of April, temperature-precipitation coefficients and the number of days with maximum air temperature(no less than 25℃) in March, sunshine hours in the third ten days in March, the number of days with daily maximum air temperature(no less than 28℃) in the third ten days of April, the number of heavy rain days(no less than 25 mm) in April, the number of days with relative air humidity between 40% and 80% in the first ten days in May. The key meteorological factors which affect the occurrence area of wheat aphids in Huanghuai Area are average air temperature of last winter and in March, precipitation in the third ten days of January, the number of days with relative air humidity (more than 80%) in the first ten days in March, temperature-precipitation coefficients in April, the number of rainless days in the third ten days in April. The meteorological suitability forecasting models of wheat aphids are established based on the normalized key meteorological factors in North China and Huanghuai Area. Hindcast validation results show that the forecasting accuracy for meteorological suitability models is 91.2%, 93.1% in North China and Huanghuai Area. The accuracy of extrapolation forecasting in 2016-2018 is higher than 75% in the former two areas respectively. The average accuracy of extrapolation forecasting from 2016 to 2018 is 100% in Anhui and Jiangsu using the meteorological suitability forecasting model in Huanghuai Area. Models can be put into operational application in Huang-Huai-Hai region of China.

稻飞虱、稻纵卷叶螟等迁飞性害虫及其携传的水稻病毒病是中国和越南、泰国等中南半岛国家重要的跨境跨区域迁飞发生和流行性病虫害.在建立中国水稻重大病虫害跨区域监测预警体系的基础上,建立中国和周边国家间水稻重大病虫害跨境跨区域监测预警体系,对于提高我国水稻迁飞性害虫及其传播的病毒病发生的早期预见性、增强防控主动性具有重要意义.自20世纪50年代起,中国通过构建体系架构,合理布局站点,明确站点任务,实施信息共享和开展联合预警,逐步构建了相对完善的全国水稻重大病虫害监测预警网络体系,在提高国内水稻重大病虫害预警防控能力,保障全国粮食生产“十二连增”“十四连丰”方面发挥了重要作用.为进一步提高重大迁飞性害虫的监测预警能力,自2010年开始,在中国农业部的支持下,中国、越南两国实施了“中越水稻迁飞性害虫监测防治项目”,双方通过互设联合监测站点,开展水稻迁飞性害虫等重大病虫害系统监测和发生信息交流、数据交换,以及实地调查和技术交流,进一步增强了中国水稻重大病虫害发生的早期预见性和可持续治理能力,使全国水稻病虫害出现了近10年的连续下降趋势,为保障国家粮食安全做出了积极贡献.当前,我们农作物重大病虫害监测预警在取得重大进展的同时,也面临不少困难,尤其是全国普遍存在人员减少、青黄不接和保障不力的问题.为此提出如下建议,一是健全保障机制,保证工作条件,保持稳定运行;二是增加监测站点,统一监测标准,开展联合监测;三是构建信息网络,实时上报信息,实施信息共享;四是推广智能设备,提升装备水平,推动信息直采;五是强化技术研究,研究明确规律,提升支撑能力.

为给我国稻纵卷叶螟Cnaphalocrocis medinalis防治提供前期预警,使用R语言软件对我国15个省市区稻纵卷叶螟发生等级与全球海温场资料进行遥相关分析,绘制相关系数的时空间分布图,筛选出显著相关海温区作为预测因子,根据各省市区虫情数据组建回归模型+判别模型、BP神经网络模型和支持向量机(SVM)模型,比较3种模型的历史回检率和预测完全准确率.结果 显示,3种模型对稻纵卷叶螟发生等级均有一定的预测能力,其中判别模型+回归模型效果最好,预检完全准确率可达到75.0％,BP神经网络模型次之,预检完全准确率为68.2％,SVM模型预测效果最差,预检完全准确率为54.5％.进一步分析建模所使用的50个预测因子的空间位置,在南印度洋和北大西洋确定3个预测指标,预检准确率为94.4％.通过海温场数据建立的我国15个省市区稻纵卷叶螟发生等级预测模型,适用于长期预测预报.判别模型+回归模型更适合在样本量少、预测因子相关性强的地区建模,而根据预测因子空间分布选择的预测指标进行定性预测准确率更高.

The rice striped stem borer (SSB), Chilo suppressalis Walker, and the rice yellow stem borer (YSB), Scirpophaga incertulas Walker, are two of the most damaging pests of rice plant, whose relative crop damage has changed in recent years. Here, we carried out experiments and surveys to understand the potential impact of field flooding on populations of these species. YSB had a consistently higher mortality rate than SSB in overwintering populations. We show that SSB survived under submersion better than YSB, crawled more strongly than YSB, and escaped more effectively from a waterlogged environment than YSB. These differences may relate partly to the longer abdominal prolegs and thoracic legs of SSB than YSB. These factors likely explain why YSB has a higher mortality rate than SSB in overwintering populations and why the relative importance of YSB is declining in some areas. In addition, the flooding method provides an effective cultural practice for some crops. Our results uncover the reasons why flooding practice is more effective for YSB than SSB. The results also point to an effective control measure for YSB.

【目的】利用性诱防控和监测水稻二化螟Chilo suppressalis的有效性在田间已经得到认可。但雄蛾具有多次交配能力,致使性诱防治二化螟的应用策略一直存在争论。本研究的目的是探索二化螟雄蛾的多次交配及其对雌蛾繁殖力的影响,认识性诱防控害虫的机理。【方法】利用行为学方法调查了雌雄蛾以不同比例配对（1∶1, 4∶1和10∶10）时雄蛾的交配次数和交配持续时间,并结合解剖学方法,观察分析了二化螟雄蛾的交配次数对精巢、交配囊和精包大小以及雌蛾产卵量的影响。【结果】二化螟雌雄蛾按1∶1配对时,交配雄蛾和多次交配雄蛾的比例分别为74.0%和36.0%,平均交配1.7次,首次交配主要发生在0-1日龄,绝大部分具有多次交配能力的雄蛾的首次交配发生在0-1日龄。雌雄蛾按4∶1配对时,交配雄蛾和多次交配雄蛾的比例分别为69.4%和51.3%,平均交配2.1次,显著高于按1∶1配对。雌雄蛾按10∶10配对时,交配雄蛾和多次交配雄蛾的比例分别为65.5%和37.8%,平均交配1.9次。雄蛾第3次交配的持续时间显著长于第1和2次交配,但是交配1-3次雄蛾的精巢体积无显著差异。与不同交配次数雄蛾进行交配的雌蛾交配囊和精包体积无显著差异,雌蛾产卵量也无显著差异。【结论】二化螟中仅有部分雄蛾能够多次交配,多次交配雄蛾的首次交配主要发生在0-1日龄,部分雄蛾一生都不会交配。研究结果为二化螟的性诱防治提供了理论依据。

简述了越南区域植物保护中心的地位和职能、水稻病虫测报调查技术、近年来水稻病虫防控技术的发展以及越南农业合作社发展现状.结合中越水稻迁飞性害虫监测与防治合作项目研究进展,明确了越南水稻迁飞性害虫发生对我国具有重要的指示意义,提出了进一步扩大合作范围、深化合作内容的建议,以期为两国预测迁飞性害虫发生动态提供更为充分的依据,全面提升两国植保能力,为保障两国农业发展和粮食安全发挥重要作用.

为了揭示大气低温胁迫对中国褐飞虱年内初始迁入的影响,更好地预警来自境外的褐飞虱早期迁入,通过统计2000-2017年中国华南、西南两个稻区褐飞虱年内的始见期和首次迁入峰日,逆推其迁飞轨迹和虫源区,分析大气温度场对迁飞过程产生的作用,比较了褐飞虱在我国不同稻区、不同年内初始迁入期受大气低温胁迫产生的作用差异.结果 表明:(1)近年来褐飞虱初始迁入中国的时间提前,初始迁入华南稻区的时间比西南稻区早,华南稻区始见期提早可能与褐飞虱种群越冬北界北移有关.(2)西南稻区褐飞虱年内初始迁入的境外虫源主要来自缅甸,华南稻区年内初始迁入的境外虫源主要来自越南和老挝的中北部.(3)对盛行迁飞层的温场分布研究表明,在褐飞虱年内初始迁入过程中低温屏障发生的概率约为54.4％,迁入当晚降虫地的平均低温强度为13.45℃,平均降温幅度为1.88℃.其中低温胁迫在华南稻区表现更为显著(发生概率为58.3％,平均强度为13.18℃),在始见期表现得更明显(发生概率为70.6％,平均强度为12.53℃).

AbstractRice planthoppers and related viral diseases have become one of the most important factors affecting rice production in Asian countries, and the resulting abuse of pesticides is laying a hidden danger for future food security. As the most economically devastating species, the brown planthopper, Nilaparvata lugens (Stål), moves from the tropical Indochina Peninsula to temperate regions where it cannot overwinter annually with the feature of long‐range migration, and eventually appears in paddy fields of most East Asian countries. Compared with the overland migration that has been studied in more detail, there is relatively less understanding of N. lugens’ performance in transnational movement, especially in the representative overseas migration between China and Northeast Asia. Based on the light‐trap data from China and Korea and the matched meteorological data of East Asia in 2016, a typical overseas migration event was basically analyzed. The results are as follows. First, the source area of the N. lugens population in this migration was in southeastern Jiangsu and eastern Shanghai. They took off at dusk on 1 August, flying at the altitude of 1700–2200 m, and landed in southwestern Korea at 02:00–11:00 UTC on 3 August. Second, a southerly airflow belt was the main weather factor for population's overseas migration, and the confrontation between the western Pacific subtropical high and a northern high pressure in southern Korea was the main reason for population to land. Besides, we discussed the one‐off feature of overseas migration compared to overland migration. These results show the close relation between weather systems and the migration dynamics of N. lugens and may extend the perception for N. lugens’ behavioral chain in the long‐range migration between China and Northeast Asia, thus allowing a sufficient time for reasonable and effective control measures.

应用大数据、人工智能和深度学习技术,研发了一款基于手机移动端的农作物病虫害移动智能识别系统——随识;该系统可安装到智能手机上,通过手机拍照,即可实现农作物病虫害从鉴定识别到防治技术等方面的服务,针对一个病虫害,提供包括鉴定识别、特征描述、分布范围、危害损失、发生规律、防治技术和防控信息等内容,可为广大植保技术人员和使用者开展病虫害防治提供较为全面的帮助.本文在介绍随识病虫害识别系统架构的基础上,简要介绍了系统的下载安装、注册登录、智能识别、圈记、病虫知识库和用户信息等功能模块的使用方法,以期帮助用户更好地使用该系统.

为提高农作物重大病虫害发生信息自动化、智能化采集能力,全面提升监测预警水平,笔者基于大数据、人工智能和深度学习技术,研发了一款农作物病虫害移动智能采集设备——智宝,主要实现了3个方面的功能:一是病虫害发生信息自动采集上报.通过该产品进行人工拍照,可实现对田间农作物重大病虫害发生图像、发生位置、发生数量、微环境因子等数据的实时采集和上报.二是自动识别计数.基于植保大数据与人工智能技术,通过构建病虫害自动识别系统,可实现重大病虫害精准识别与分析,只要拍摄照片,即可快速、精确地识别病虫害种类,并自动计数、上报到指定的测报系统.三是自动分析判别分级.针对拍摄采集上报的重大病虫害发生信息,系统可在自动识别和计数的基础上,进一步对病虫害发生严重程度进行智能判别分级,甚至根据相关预测模型,对病虫害的发生趋势进行辅助分析预测,提出预测建议.通过2016—2019年组织多地植保机构进行试验改进,该技术产品日趋成熟,有望在未来的农作物病虫害发生信息采集和预测预报工作中推广使用.