东北三省春玉米旱灾动态风险评估

干旱演变趋势的不确定性决定了旱灾风险的动态变化特征,如何采用有效的方法探究风险随时间的变化是评估旱灾动态风险的关键。由于干旱发生发展缓慢,长时间尺度数值预报产品精度较低,用于干旱预报有一定的局限性。本研究选用天气发生器随机生成未来逐日气象数据的大量样本预测干旱演变趋势,驱动作物模型评估不同趋势下作物产量因旱损失及动态风险。选取东北三省为研究区,在2012—2017年田间试验数据的基础上,率定及验证APSIM作物模型,利用BCC/RCG-WG天气发生器随机生成未来气象数据,驱动APSIM作物模型模拟春玉米产量因旱损失,计算期望产量因旱损失率,以典型干旱年2000年为例,实时动态评估东北三省春玉米生育期内（5月1日—9月18日）旱灾动态风险。结果表明：（1）APSIM作物模型模拟春玉米播种到开花日数、生育期日数以及产量与试验观测结果决定系数R²均大于0.5,标准均方根误差NRMSE均在10%以下,表明APSIM模型在东北三省模拟春玉米生长效果较好;BCC/RCG-WG天气发生器生成100个气象要素样本与1961—2020年各气象数据年均值R²均在...     

Model of crop:weed competition applied to maize:Abutilon theophrasti interactions. I. Model description and evaluation

A complex set of interactions among crops, weeds and their environment determines the impact of weed interference on crop productivity. These interactions can be simulated with dynamic crop:weed competition models, such as ALMANAC. In this study, ALMANAC was modified to simulate maize: Abutilon theophrasti competition. In the modified ALMANAC model, daily increases in leaf area index (LAI), height and rooting depth are attenuated on the basis of accumulated above-ground biomass and by environmental stress. Also, a simple, flexible method is adopted to partition radiation in a mixed canopy. A maize: A. theophrasti competition study conducted near Aurora, NY, in which a range of weed densities (0–16 plants m⁻²) were established in a maize crop, was used to evaluate the model. The modified ALMANAC proved to be a useful tool for segregating the maize response to competition in 1991 (simulated loss of 35% at the highest weed density) from those in 1992–94 (simulated losses not greater than 16%). Based on these findings, the modified ALMANAC model is judged to be capable of distinguishing between environmental conditions that facilitate large yield losses and those that allow maize to outcompete A. theophrasti .     

基于气象关键因子的河南省夏玉米产量预报研究

以河南省13个地市1990-2006年逐旬光温水气象资料为基础,通过相关分析确定了影响河南省夏玉米产量的关键气象因子,建立了7月中旬-9月中旬的夏玉米气象产量预报模型。将全省由南至北划分为三个区域,分区回代1990-2006年资料对模型预报准确率进行回代检验,并利用2007-2010年资料对模型进行试报。检验结果表明,模型回代准确率全省为88．4％,不同区域间回代准确率差异明显,北部最高92．9％,中部次之87．4％,南部最低83．4％,分析原因主要受夏玉米产量年际波动的影响;模型预报准确率全省为94．9％,各区域差别不大,基本在95％左右。     

基于连续小波变换的干旱胁迫下玉米冠层叶绿素密度估测

测定了玉米各生育时期干旱胁迫下冠层叶绿素密度及冠层光谱数据,利用原始光谱反射率与冠层叶绿素密度进行相关性分析,采用常用植被指数、波段自由组合、连续小波变换构建玉米冠层叶绿素密度估测模型,并用决定系数(R~2)、均方根误差(RMSE)进行精度检验。结果表明:冠层叶绿素密度在抽雄期相较于正常对照,轻度、中度、重度干旱胁迫处理分别下降7.8%、29.5%、44.2%;波段自由组合指数RVI(555,538)、NDVI(555,538)和敏感小波系数bior5.5(26,792)、rbio2.6(22,790)、gaus6(21,791)与叶绿素密度的相关性较高,相关系数绝对值均达到0.900以上;基于敏感小波系数构建的冠层叶绿素密度估测模型验证集R~2均在0.850以上,相较于其他植被指数模型R~2平均提升20.6%,RMSE平均降低32.6%;最优模型为以gaus6(21,791)为自变量的一元线性回归模型,R~2为0.864,RMSE为1.532。利用连续小波变换对光谱数据进行预处理,可以有效提升玉米冠层叶绿素密度估测模型的精度。     

基于Sentinel-2影像的河套灌区作物种植结构提取

作物识别是提取作物种植结构的基础,利用遥感技术对作物进行监测识别,对优化生产布局、调整农业生产模式有着重要意义。文中选取河套灌区杭锦后旗为研究区域,基于2019年覆盖生长周期的Sentinel-2号卫星影像数据,构建NDVI时间序列数据集,利用Savitzky-Golay(S-G)滤波对NDVI时间序列数据集进行平滑,分析不同作物不同发育期的光谱曲线特征,计算各主要作物识别关键期的光谱阈值,构建基于决策树分层分类的农作物种植面积提取模型,并用验证样本对分类结果进行精度验证。结果表明:利用整个生育期内的NDVI最大合成影像确定植被地表覆盖,NDVI曲线变化区别林地与耕地,逐层提取地物,简便易行;采用S-G滤波重构高质量的NDVI时间序列曲线,研究证明重构后曲线更加平滑符合作物生长趋势;基于Sentinel-2号遥感数据和整个生育期NDVI时序数据,构建分层分类决策树模型,作物分类总体精度达92.1%,Kapppa系数精度达0.857。本研究采用的方法满足遥感观测应用化需求,也为县级区域农作物分类提供重要参考价值。     

基于卷积神经网络的农作物叶片病害诊断

农作物病害诊断对于及时发现并采取防控措施具有重要意义.本研究针对苹果、玉米、番茄、葡萄等典型农作物的常见叶片病害问题,使用了两种目前使用最广泛的卷积神经网络——VGG 16及Resnet50,对典型农作物叶片病害进行识别.使用AI Challenger比赛的农作物叶片病害数据集图像,并对这些图像进行预处理,构建了47 ...     

作物病害监测预警研究进展

作物侵染性病害是影响作物安全生产的重要生物灾害，具有突发性、暴发性和强流行性等特点，常常造成巨大的损失。近年来，随着病菌孢子捕捉、遥感、地理信息系统、卫星定位系统、大气环流分析、分子生物学、人工智能、大数据和物联网等技术的快速发展与应用，作物病害监测预警技术取得了重要进展，大幅度提高了对病害监测和预测的准确度。该文综述了小麦、水稻、玉米和马铃薯等粮食作物的6种重大病害监测预警工作的研究进展及应用情况，同时，探讨了我国作物病害监测预警工作中存在的主要问题，并提出了未来作物病害监测预警的前景和发展方向。     

Risk assessment models for wheat fusarium head blight epidemics based on within-season weather data

ABSTRACT Logistic regression models for wheat Fusarium head blight were developed using information collected at 50 location-years, including four states, representing three different U.S. wheat-production regions. Non-parametric correlation analysis and stepwise logistic regression analysis identified combinations of temperature, relative humidity, and rainfall or durations of specified weather conditions, for 7 days prior to anthesis, and 10 days beginning at crop anthesis, as potential predictor variables. Prediction accuracy of developed logistic regression models ranged from 62 to 85%. Models suitable for application as a disease warning system were identified based on model prediction accuracy, sensitivity, specificity, and availability of weather variables at crop anthesis. Four of the identified models correctly classified 84% of the 50 location-years. A fifth model that used only pre-anthesis weather conditions correctly classified 70% of the location-years. The most useful predictor variables were the duration (h) of precipitation 7 days prior to anthesis, duration (h) that temperature was between 15 and 30 degrees C 7 days prior to anthesis, and the duration (h) that temperature was between 15 and 30 degrees C and relative humidity was greater than or equal to 90%. When model performance was evaluated with an independent validation set (n = 9), prediction accuracy was only 6% lower than the accuracy for the original data sets. These results indicate that narrow time periods around crop anthesis can be used to predict Fusarium head blight epidemics.     

基于深度学习的实蝇图像智能识别方法的建立

实蝇科昆虫很多种类是世界性的重要检疫性有害生物。实蝇的快速准确识别对于保护国门生物安全、促进我国农产品出口具有重要意义。本研究建立了一种基于深度学习的实蝇图像智能识别方法,针对实蝇科昆虫的翅图像特征,以桔小实蝇、南瓜实蝇、瓜实蝇、具条实蝇4种实蝇（每种250头共1 000头）为例,通过标本制作与图像采集、图像预处理、创建数据集训练物体检测模型等模块不断调整优化实现对实蝇图像的自动识别。结果表明在图像色阶参数90/1/220,通过百度Easy DL平台Paddle Paddle深度学习框架结合Auto Model Search训练图像分类模型,选择超高精度算法、高级训练配置epoch并使用数据增强策略进行模型训练时识别准确率达95%以上。本方法具有操作简单、准确率高、可扩展性强等特性,通过智能手机拍摄待测样本输入系统即可进行准确识别,可应用于果蔬园实蝇监测、出入境口岸实蝇检疫以及昆虫科普教育等场景,并可为其他昆虫自动识别研究提供有益借鉴。     

干旱对太行山山前平原雨养农田产量影响的模拟研究

以多时间尺度标准化降水蒸散指数（SPEI）作为干旱指标,利用APSIM模型（农业生产系统模拟模型）模拟太行山山前平原雨养旱作农田冬小麦-夏玉米近30 a产量变化,分析干旱对作物产量的影响。结果显示：APSIM模型对模拟雨养条件下作物产量具有良好的适用性,玉米产量年际波动较大、变异系数为51.2%,易受降雨因素影响;小麦产量波动相对较为稳定,变异系数为26.4%。干旱指数与作物产量极显著相关（P<0.01）,其中小麦产量与SPEI-3-Apr相关系数达0.79,玉米产量与SPEI-3-Sep相关系数达0.88,适宜干湿状态在0～2之间;所建立的回归方程分别可以解释61.8%的小麦产量变异和87.7%的玉米产量变异。研究表明,SPEI-3-Apr、SPEI-3-Sep可分别作为该地区雨养农田小麦、玉米产量的估计指标。     

基于根点替代的猕猴桃果园行间导航线生成方法研究

针对棚架式猕猴桃果园树干分立、树干与树冠分离的生理特点,提出基于深度学习的目标检测框底边中点替代果树根点的导航特征目标检测方法。使用LabelImg标注预处理图像中的树干信息,生成猕猴桃树干数据集,基于Faster R-CNN构建目标检测模型,实现对猕猴桃果园行间有效范围的树干识别,利用根点替代方法确定树干根点的定位基点坐标,基于三次样条插值法提取树行线,根据最小二乘法拟合生成导航线。试验结果表明,生成的目标检测模型在地膜、杂草和土壤等果园行间环境下,树干正确识别率分别为90.6%、90.1%、89.4%;树干像素高度大于300像素、200～300像素、100～200像素的正确识别率分别为94.6%、89.9%、85.6%,表明选择树干作为果园行间视觉导航标识,有效利用了深度学习的稳定性,提高了导航目标识别的准确率;根点替代简化了导航定位基点生成算法,获取的果树定位基点与实际树干根点的平均横向像素误差占比为1.3%,平均纵向像素误差占比为2.2%,平均实际距离误差为0.102 m,导航定位基点精度较高;生成的导航线平均横向像素偏差为5.1像素,实际平均横向偏差为0.052 m,获取的果园行间导航路径满足行间导航要求。     

Procedures and potentialities in sugar beet production

Cultivation procedures for the sugar beet crop continue to change considerably in England. The average contract per grower has increased from 3.4 in 1936 to 8.2 ha in 1973. Within the last decade the use of genetic monogerm seed has extended to 65% of the area sown, and pelleted seed to 90%. Seed spacing has doubled and the hand work needed to establish the crop drastically decreased. Much of the crop is now sown in March, instead of April or even May. The average yield of sugar has increased since 1936 from less than 3.6 to over 6 t ha^?1. These changes have been associated with routine usage of fungicide and insecticide on seed and chemical weed control on 90% of the crop in recent years; insecticide, mainly organophosphorus, has been used to control aphids and virus on 36 000 to 125 000 ha of crop annually on the basis of spray warnings; sporadic pesticide control has been used against an abundance of seedling, leaf- and root-eating pests and fungi. The changes in cultural practices have been paralleled by changes in the relative importance of the various pests and diseases—for instance, wide seed spacing and the use of herbicide have intensified seedling pest problems. The extent to which crop losses from weeds, pests and diseases have been assessed as a basis for recommendations for control is discussed, as well as the economics of control by pesticides.     

Effects of mulches on water use in a winter wheat/summer maize rotation system in Loess Plateau,China

Limited water resources often result in reduced crop yield and low water productivity(WP). In northwestern China, crop production is generally dependent on precipitation. Therefore, a variety of agricultural rainwater harvesting(ARH) techniques have been used for conserving soil moisture, ameliorating soil environment, increasing crop yield, and improving water use efficiency. A two-year(2013–2015) field experiment was conducted under a typical sub-humid drought-prone climate in Yangling(108°24′E, 34°20′N; 521 m a.s.l.), Shaanxi Province, China, to explore the effects of mulching(same for summer maize and winter wheat) on soil moisture, soil temperature, crop water consumption, and crop yield with a winter wheat/summer maize rotation. Crops were planted in a ridge-furrow pattern and the treatments consisted of a transparent film mulch over the ridges(M1), a crop straw mulch in the furrows(M2), a transparent film mulch over the ridges and a crop straw mulch in the furrows(M3), a black film mulch over the ridges and a crop straw mulch in the furrows(M4), and a control with no mulch(CK). Results showed that M4 was the best treatment for improving soil water storage and content, and decreasing crop water consumption during the summer maize and winter wheat rotation. In both maize and wheat seasons, M1 had a higher soil temperature than M2 and CK, and M3 had a higher soil temperature than M4. In the maize seasons, M4 had the highest yield, WP, and precipitation productivity(PP), with the average values for these parameters increasing by 30.9%, 39.0%, and 31.0%, respectively, compared to those in CK. In the wheat seasons, however, M3 had the highest yield, WP, and PP, with the average values for these parameters being 23.7%, 26.7%, and 23.8% higher, respectively, than those in CK. Annual yield(maize and wheat yields combined) and WP did not differ significantly between M3 and M4. These results suggested that M3 and M4 may thus be the optimal ARH practices for the production of winter wheat and summer maize, respectively, in arid and semi-arid areas.     

Contamination of fresh and ensiled maize by multiple penicillium mycotoxins

Toxins produced by Penicillium species are reported in maize silage and have been associated with health problems in cattle. Our objectives were to evaluate the prevalence and dynamics of patulin (PAT), mycophenolic acid (MPA), cyclopiazonic acid (CPA), and roquefortine C (ROC) in fresh and ensiled maize. To achieve these objectives we developed a high-performance liquid chromatography method coupled with mass spectrometry to detect all four toxins simultaneously in silage. In addition we collected weather data, information on agronomic practices, and silage fermentation characteristics for each study site. Silage was collected at harvest and after ensiling in 2001 and 2002 from 30 Pennsylvania dairies. The average concentration of toxins (range in parentheses) was: PAT 0.08 microg/g (0.01 to 1.21), MPA 0.16 microg/g (0.02 to 1.30), CPA 0.12 microg/g (0.02 to 1.43), and ROC 0.38 microg/g (0.01 to 5.71). ROC was the most frequently detected toxin (60%), followed by MPA (42%), CPA (37%), and PAT (23%). Of 120 samples tested, 15% contained no detectible levels of toxin, 25% were contaminated with one toxin, 32% with two, 18% with three, and 10% with all four toxins. All four mycotoxins were found in freshly harvested material, contradicting the belief that Penicillium toxin formation occurs exclusively during storage. We observed that weather conditions during specific growth stages of the crop affected the final concentration of toxins in freshly harvested maize. In ensiled material, PAT levels were affected by concentrations of propionic and isobutyric acids. Based on our data, Penicillium mycotoxins can form while the crop is in the field and after ensiling, suggesting that preventative measures should begin prior to ensiling.     

Automated tomato leaf disease classification using transfer learning-based deep convolution neural network

Journal of Plant Diseases and Protection - Early and accurate detection of plant diseases is necessary to maximize crop yield. The artificial intelligence based deep learning method plays a vital...     

Over-winter predation of Abutilon theophrasti and Setaria faberi seeds in arable land

To improve understanding of over-winter weed seed predation in arable fields, we used data from winter exclosure trials to determine the amount of predation and the influence of crop habitats on predation of Abutilon theophrasti and Setaria faberi seed in 2-year (maize/soyabean) and 4-year (maize/soyabean/small grain+lucerne/lucerne) crop rotation systems between 2005 and 2008. Crop habitat influenced seed predation, and had similar impacts on the two weed species. Mean A. theophrasti predation ranged from 31% in the 2-year soyabean habitat to 99% in the 4-year lucerne habitat. Mean S. faberi predation ranged from 31% in the 2-year soyabean habitat to 97% in the 4-year lucerne habitat. Results suggest that a combination or interaction of cover and substrate may have affected crop habitat preference by seed predators. Future research should further examine the influence of physical habitat on seed predation to determine characteristics of cropping systems that encourage predation, particularly during over-winter periods, so as to routinely incorporate seed predators into long-term weed management strategies.     

Impact of sand burial on maize(Zea mays L.)productivity and soil quality in Horqin sandy cropland,Inner Mongolia,China

Croplands are often suffering from sand burial in dry regions of northern China.For studying this phenomenon,we carried out a case study of field experiment including four sand burial levels,i.e.shallow(1–3 cm),moderate(8–12 cm)and deep(15–20 cm)sand burials,and no sand burial(control,CK),in a typical agro-pastoral transitional zone in Naiman Banner of eastern Inner Mongolia.The aim of this study was to assess the impacts of sand burial on maize(Zea mays L.)productivity and the soil quality along a gradient of burial depths.Results showed that there was a strong negative effect of sand burial on maize productivity and soil quality,which significantly declined(P0.05)under moderate and deep sand burial treatments.In comparison with the CK,the maize yield and above-ground biomass reduced by 47.41% and 39.47%,respectively.The soil silt and clay,soil water,soil organic carbon and total nitrogen contents under deep sand burial decreased by 67.85%,40.32%,86.52% and 82.11%,respectively,while microbial biomass carbon,microbial abundance and enzyme activity decreased by 89.78%,42.28%–79.66% and 69.51%–97.71%,respectively.There was no significant effect on crop productivity and soil quality with shallow sand burial treatment.The correlations analysis showed that there was significant positive correlations of both maize yield and above-ground biomass with soil silt and clay,soil organic carbon and total nitrogen contents,p H,electrical conductivity,soil water content,microbial abundance and biomass and all tested soil enzyme activities.Stepwise regression analysis indicated that soil water and total nitrogen contents,urease,cellobiohydrolase and peroxidase activities were key determining factors for maize productivity.This combination of factors explains reason of the decreased maize productivity with deep sand burial.We found that degradation of cropland as a result of sand burial changed soil physical-chemical properties and soil enzyme activities in the plow layer,and decreased overall maize productivity.Furthermore,decreased soil enzyme activity was a better indicator to predict sandy cropland degradation.     

ET_0预测的卡尔曼滤波修正ANFIS模型研究

实时、准确地对作物需水量的预测是实现智能节水灌溉的关键技术。预测模型的合理选择及精度提高是作物需水决策系统的核心。本文将陕西西安地区的气象数据环境信息引入自适应神经模糊推理(ANFIS)作物参考蒸腾量(ET_0)预测模型,应用卡尔曼滤波器对气象数据经ANFIS建模得到的ET_0预测值进行滤波去噪,以提高模型的预测精度,并通过仿真和实验验证,从理论和实践两个方面来验证模型的精度。仿真结果得到,反映模型预测值与真实值之间拟合程度的均等系数(EC)值校正前为0.93,校正后达到0.98。实验结果得到,ANFIS预测模型的平均绝对误差是28.94%,平均相对误差是26.37%,卡尔曼修正后的ANFIS预测模型的平均绝对误差是7.24%,平均相对误差是6.59%。仿真和实验结果表明,利用卡尔曼滤波对ANFIS预测模型进行修正,可以提高预测的精度,经卡尔曼修正后的ANFIS模型能更佳地反映ET_0的变化趋势。     

玉米病害发生现状与推广品种抗性对未来病害发展的影响

 随着全球气候变化、耕作方式改变和新品种推广,我国玉米病害的发生也有所改变。在春玉米区,丝黑穗病仍然持续严重为害,大斑病呈加重趋势;在夏玉米区,局部地区小斑病发生较重,而矮花叶病普遍发生较轻;以往的次要病害已成为重要病害:如,南方锈病在夏玉米区南部严重发生,瘤黑粉病成为生产中的突出问题,土传病害日益加重,细菌性病害发生渐多。对玉米主要推广品种、近年国家和主产省份审定品种的抗病性分析表明,在北方春玉米区,由于品种抗性水平降低、个别感病品种的推广及病原菌致病力变异,大斑病在近年仍将呈现较重发生趋势;丝黑穗病的发生则由于推广抗病品种和种子包衣技术而有所减轻,但局部地区仍会严重发生;由于缺乏抗病品种,灰斑病和弯孢菌叶斑病的发生将主要取决于气候因素。在北方夏玉米区,小斑病暴发的可能性较小,但已有强致病力菌株出现;由于推广品种普遍对茎腐病抗性水平较低并受耕作制度的影响,茎腐病和苗枯病将成为主要病害;多数品种对南方锈病缺乏抗性,南方锈病发生面积将继续扩大,发病程度也将增加。     

氮肥配施生物炭和脲酶抑制剂对夏玉米-冬小麦产量及氮素利用的影响

于2019—2021年采用再裂区设计,设置氮肥、生物炭和脲酶抑制剂3个因素,主处理设5个氮水平：0、75、150、225 kg·hm^-2和300 kg·hm^-2,副处理设2个生物炭水平：0 t·hm^-2和7.5 t·hm^-2,副副处理设2个脲酶抑制剂水平：0%和2%,共20个处理,研究氮肥配施生物炭和脲酶抑制剂对夏玉米-冬小麦轮作体系作物产量和氮肥吸收利用的影响。结果表明,施用生物炭显著提高夏玉米和冬小麦产量、植株氮素吸收量、氮肥表观利用率、氮素收获指数以及夏玉米地上部生物量,较不施生物炭处理分别增加4.4%和2.9%、2.3%和3.0%、25.8%和13.5%、4.9%和6.1%、4.5%;氮肥单独配施生物炭可显著提高夏玉米和冬小麦产量、植株氮素吸收量和氮肥表观利用率,且氮肥和生物炭具有显著的交互效应。施用脲酶抑制剂显著增加夏玉米植株氮素吸收量和氮肥表观利用率,较不施脲酶抑制剂处理分别提高1.5%和3.0%;氮肥单独配施脲酶抑制剂可提高夏玉米植株氮素吸收量和氮肥表观利用率,但氮肥与脲酶抑制剂无显著...