No abstract

Wheat blast (WB) caused by Magnaporthe oryzae pathotype Triticum (MoT) is an important fungal disease in tropical and subtropical wheat production regions. The disease was initially identified in Brazil in 1985, and it subsequently spread to some major wheat-producing areas of the country as well as several South American countries such as Bolivia, Paraguay, and Argentina. In recent years, WB has been introduced to Bangladesh and Zambia via international wheat trade, threatening wheat production in South Asia and Southern Africa with the possible further spreading in these two continents. Resistance source is mostly limited to 2NS carriers, which are being eroded by newly emerged MoT isolates, demonstrating an urgent need for identification and utilization of non-2NS resistance sources. Fungicides are also being heavily relied on to manage WB that resulted in increasing fungal resistance, which should be addressed by utilization of new fungicides or rotating different fungicides. Additionally, quarantine measures, cultural practices, non-fungicidal chemical treatment, disease forecasting, biocontrol etc., are also effective components of integrated WB management, which could be used in combination with varietal resistance and fungicides to obtain reasonable management of this disease.

Abstract:The Sundarbans mangrove forest is an important resource for the people of the Ganges Delta. It plays an important role in the local as well as global ecosystem by absorbing carbon dioxide and other pollutants from air and water, offering protection to millions of people in the Ganges Delta against cyclone and water surges, stabilizing the shore line, trapping sediment and nutrients, purifying water, and providing services for human beings, such as fuel wood, medicine, food, and construction materials. However, this mangrove ecosystem is under threat, mainly due to climate change and anthropogenic factors. Anthropogenic and climate change-induced degradation, such as over-exploitation of timber and pollution, sea level rise, coastal erosion, increasing salinity, effects of increasing number of cyclones and higher levels of storm surges function as recurrent threats to mangroves in the Sundarbans. In this situation, regular and detailed information on mangrove species composition, their spatial distribution and the changes taking place over time is very important for a thorough understanding of mangrove biodiversity, and this information can also lead to the adoption of management practices designed for the maximum sustainable yield of the Sundarbans forest resources. We employed a maximum likelihood classifier technique to classify images recorded by the Landsat satellite series and used post classification comparison techniques to detect changes at the species level. The image classification resulted in overall accuracies of 72%, 83%, 79% and 89% for the images of 1977, 1989, 2000 and 2015, respectively. We identified five major mangrove species and detected changes over the 38-year (1977-2015) study period. During this period, both Heritiera fomes and Excoecaria agallocha decreased by 9.9%, while Ceriops decandra, Sonneratia apelatala, and Xylocarpus mekongensis increased by 12.9%, 380.4% and 57.3%, respectively.

Delivering accurate cyclone forecasts in time is of key importance when it comes to saving human lives and reducing economic loss. Difficulties arise because the geographical and climatological characteristics of the various cyclone formation basins are not similar, which entails that a single forecasting technique cannot yield reliable performance in all ocean basins. For this reason, global forecasting techniques need to be applied together with basin-specific techniques to increase the forecast accuracy. As cyclone track is governed by a range of factors variations in weather conditions, wind pressure, sea surface temperature, air temperature, ocean currents, and the earth' rotational force-the coriolis force, it is a formidable task to combine these parameters and produce reliable and accurate forecasts. In recent years, the availability of suitable data has increased and more advanced forecasting techniques have been developed, in addition to old techniques having been modified. In particular, artificial neural network based techniques are now being considered at meteorological offices. This new technique uses freely available satellite images as input, can be run on standard PCs, and can produce forecasts with good accuracy. For these reasons, artificial neural network based techniques seem especially suited for developing countries which have limited capacity to forecast cyclones and where human casualties are the highest.

Bangladesh has experienced several catastrophic Tropical Cyclones (TCs) during the last decades. Despite the efforts of disaster management organizations, as well as the Bangladesh Meteorological Department (BMD), there were lapses in the residents' evacuation behavior. To examine the processes of TC forecasting and warning at BMD and to understand the reasons for residents' reluctance to evacuate after a cyclone warning, we conducted an individual in-depth interview among the meteorologists at BMD, as well as a questionnaire survey among the residents living in the coastal areas. The results reveal that the forecasts produced by BMD are not reliable for longer than 12-hour. Therefore, longer-term warnings have to be based on gross estimates of TC intensity and motion, which renders the disseminated warning messages unreliable. Our results indicate that residents in the coastal areas studied, do not follow the evacuation orders due to mistrust of the warning messageswhich can deter from early evacuation; and insufficient number of shelters and poor transportation possibilities-which discourages late evacuation. Suggestions made by the residents highlight the necessity of improved warning messages in the future. These findings indicate the need for improved forecasting, and more reliable and more informative warning messages for ensuring a timely evacuation response from residents.

Many places around the world are exposed to tropical cyclones and associated storm surges. In spite of massive efforts, a great number of people die each year as a result of cyclone events. To mitigate this damage, improved forecasting techniques must be developed. The technique presented here uses artificial neural networks to interpret NOAA-AVHRR satellite images. A multi-layer neural network, resembling the human visual system, was trained to forecast the movement of cyclones based on satellite images. The trained network produced correct directional forecast for 98% of test images, thus showing a good generalization capability. The results indicate that multi-layer neural networks could be further developed into an effective tool for cyclone track forecasting using various types of remote sensing data. Future work includes extension of the present network to handle a wide range of cyclones and to take into account supplementary information, such as wind speeds, water temperature, humidity, and air pressure

No abstract

Spot blotch (SB) disease caused by the hemibiotrophic pathogen Bipolaris sorokiniana inflicting major losses to the wheat grown in warm and highly humid areas of the Indian subcontinent, including Bangladesh, necessitates identification of QTLs stably expressing in Indian subcontinent conditions. Thus, two RIL mapping populations, i.e., WC (WUYA × CIANO T79) and KC (KATH × CIANO T79), were phenotyped at Dinajpur, Bangladesh for three consecutive years (2013–2015) and genotyped on a DArTseq genotyping by sequencing (GBS) platform at CIMMYT, Mexico. In both populations, quantitative inheritance along with transgressive segregation for SB resistance was identified. The identified QTLs were mostly minor and were detected on 10 chromosomes, i.e., 1A, 1B, 2A, 2B, 2D, 4B, 4D, 5A, 5D, and 7B. The phenotypic variation explained by the identified QTLs ranged from 2.3–15.0%, whereby QTLs on 4B (13.7%) and 5D (15.0%) were the largest in effect. The identified QTLs upon stacking showed an additive effect in lowering the SB score in both populations. The probable presence of newly identified Sb4 and durable resistance gene Lr46 in the identified QTL regions indicates the importance of these genes in breeding for SB resistance in Bangladesh and the whole of South Asia.