Development of a field biotest using artificial inoculation to evaluate resistance and yield effects in sugar beet cultivars against <Emphasis Type="Italic">Cercospora beticola</Emphasis>

Cercospora beticola resistance and disease yield loss relationships in sugar beet cultivars are best characterised under field conditions with heavy natural infection; this does not occur regularly under German climatic conditions. Since Cercospora resistance reduces the rate of pathogen development, high yield loss was observed in studies using artificial inoculation. Our study, therefore aimed to optimise inoculum density to obtain cultivar differentiation, which correlates to natural infection. In 2005 and 2006, field trials were carried out to determine the effect of different inoculum densities on Cercospora resistance of three sugar beet cultivars possessing variable resistance. The epidemic progress and white sugar yield loss (WSY_loss) were determined and their relationship evaluated. An optimal inoculum concentration range (between 10,000–20,000 infectious Cercospora units ml⁻¹ inoculum suspension) was determined which allowed maximum resistance parameter differentiation in terms of C. beticola disease severity (DS), area under the disease progress curve (AUDPC) and WSY_loss. The correlation between AUDPC and WSY_loss was identical for all cultivars independent of the resistance level, demonstrating that tolerant reactions of the cultivars under study were not detectable. This study provides evidence that even under optimal inoculum levels necessary to obtain maximum differentiation between cultivars, climatic conditions are important for disease management, but remain unpredictable, indicating that artificial inoculation needs to be optimised, but that single field locations are not sufficient and reliable to evaluate Cercospora resistance.     

Sensorik für einen präzisierten Pflanzenschutz

Precision agriculture is a management concept depending on information technologies related to within-field variability. Site-specific plant production requires the use of technologies, such as global positioning systems, sensors, and information management tools to assess variations in soil, crop canopy and micro-climate. Crop protection is an important production factor, which at present is applied in high-input cropping systems homogeneously in the field despite of site-specific heterogeneity in the incidence and distribution of weeds, pests and pathogens. The spatial and temporal heterogeneity of pests in the field is assessed using remote sensing techniques linked to global positioning systems. The generation and management of information on pest incidence with high spatial resolution and its conversion into precise control systems will enable a targeted and resource-preserving integrated pest management system under high productivity conditions, which is economically successful, environmentally sound and socially acceptable. The recording of disease-related weather data and the assessment of spatial heterogeneity of micro-climate in the field as well as the detection of disease specific symptoms with remote and near range sensors (multispectral and hyperspectral cameras, thermography, chlorophyll fluorescence etc.) have the potential to make crop protection more precise in space and time. Innovative approaches are discussed in detail.     

Recent advances in sensing plant diseases for precision crop protection

Near-range and remote sensing techniques have demonstrated a high potential in detecting diseases and in monitoring crop stands for sub-areas with infected plants. The occurrence of plant diseases depends on specific environmental and epidemiological factors; diseases, therefore, often have a patchy distribution in the field. This review outlines recent insights in the use of non-invasive optical sensors for the detection, identification and quantification of plant diseases on different scales. Most promising sensor types are thermography, chlorophyll fluorescence and hyperspectral sensors. For the detection and monitoring of plant disease, imaging systems are preferable to non-imaging systems. Differences and key benefits of these techniques are outlined. To utilise the full potential of these highly sophisticated, innovative technologies and high dimensional, complex data for precision crop protection, a multi-disciplinary approach—including plant pathology, engineering, and informatics—is required. Besides precision crop protection, plant phenotyping for resistance breeding or fungicide screening can be optimized by these innovative technologies.     

肯尼亚水稻土和甘蔗地土壤中~(14)C-六氯苯和~(14)C-滴滴涕的自然消解

氯代持久性有机污染物的农田土壤污染呈现污染浓度低、面积大、新源污染不断输入的特点。农田土壤本身微生物种类丰富,对氯代有机污染物具有较大的降解潜力和未知性。本试验以典型高氯代和低氯代持久性有机污染物——六氯苯(HCB)和滴滴涕(DDT)为研究对象,结合~(14)C同位素示踪技术,研究HCB和DDT在热带水稻土和甘蔗地土壤的矿化现象,同时监测HCB和DDT在两种土壤中的挥发、降解产物以及结合残留。结果表明,经84 d好氧培养,HCB和DDT在两种土壤中的矿化量分别仅为0.14%和3%,低氯代有机污染物DDT的矿化速率显著高于高氯代有机污染物HCB。然而,两种土壤对HCB或DDT的矿化没有显著性差异。HCB或DDT在水稻土中的挥发量略微高于甘蔗地土壤,两种土壤中HCB和DDT的挥发量在0.1%~0.6%之间,表明挥发不是其主要的环境过程。在DDT污染水稻土和甘蔗地土壤中添加1.25%的堆肥增加了DDT在土壤中的矿化与结合残留,减少了DDT的挥发。本研究结果表明土壤在好氧条件下对氯代持久性有机污染物的自然消解能力非常弱,而有机肥的使用有助于土壤中持久性氯代有机污染物的矿化消除。