Cyclone analysis for the abatement of grain sorghum emmissions in granaries

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Bibliography: leaves 125-127.Not availabl

High-tonnage dedicated energy crops: The potential of sorghum and energy cane

The development of viable lignocellulosic-biofuel industries in the United States will require dependable delivery of supplies of feedstocks. The selection of feedstocks ultimately will vary with geographical region across the United States. Dedicated bioenergy crops as sources of lignocellulose are likely to be most productive in the southern regions of the United States due to more abundant sunlight and longer growing seasons. Of course, dedicated bioenergy crops in the southwest must tolerate heat and drought, whereas species grown along the Gulf Coast must tolerate heat as well as variable soil moisture and variable soil-oxygen environments associated with different soil types. Dedicated energy crops for the panhandle of Texas and the Midwest will have shorter growing seasons and will need to be more cold tolerant

Data from: Unmanned aerial vehicles for high-throughput phenotyping and agronomic research

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1—the summer 2015 and winter 2016 growing seasons–of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project’s goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs

Senecio nikoensis Miq.

原著和名: サハギク科名: キク科 = Compositae採集地: 東京都 高尾山 (武蔵 高尾山)採集日: 1986/7/15採集者: 萩庭丈壽整理番号: JH007357国立科学博物館整理番号: TNS-VS-95735

Case_Study_1_plant_height_corn_Raw_Image_Data_3_of_10

Case_Study_1_plant_height_corn_Raw_Image_Data_3_of_1