Skip to main content

GRADUATE RESEARCH ASSISTANTSHIP IN SPATIAL VARIABILITY – CROP YIELD RELATIONSHIPS - University of Nebraska-Lincoln

GRADUATE RESEARCH ASSISTANTSHIP IN SPATIAL VARIABILITY – CROP YIELD RELATIONSHIPS - University of Nebraska-Lincoln

The University of Nebraska-Lincoln (UNL) invites applications for an MS or PhD graduate research assistantship. The candidate in this assistantship will support work to improve an understanding of spatial variability underlying crop yields and associated producer profitability. Specifically the student will investigate the relationship of the National Commodity Crop Productivity Index (NCCPI), an index in the SSURGO database, to crop yields in Nebraska. These efforts will provide a research-based assessment of the ability of the NCCPI to predict crop  yields in Nebraska. The selected candidate will join a collaborative research team involving faculty in Spatial Sciences (Dr. Yi Qi; https://www.qispatial.com/), Cropping Systems (Dr. Andrea Basche; https://agronomy.unl.edu/basche-research), and Applied Wildlife Ecology (Dr. Andrew Little; https://wildlifeecologylab.unl.edu/).

Responsibilities for the student will include:
·                      Data collection, organization, and analysis of relevant field-scale yield data
·                      Evaluate the spatial relationship of crop yields to the National Commodity Crop Productivity Index (NCCPI)
·                      Conduct spatial analysis and quantitative data analysis to identify hotspots of marginal or less productive regions and mapping their relationship to the NCCPI
·                      Develop map products to allow for visualization and interpretation of results

Qualifications: Applicants must have completed a minimum of a Bachelor of Science degree in a field related Geographical Information Systems, Remote Sensing, and Data Analytics. Applicants should have a GPA ≥3.0. Applicants also should have strong quantitative skills (e.g., correlation analysis, regression analysis) and organizational skills, attention to detail, and excellent oral and written communication skills. Preference will be given to applicants with prior experience or training with GIS (e.g., Esri ArcGIS develop and ArcGIS online), Remote Sensing (e.g., ENVI) or similar software.

GRA Stipend: Starting salary $22,000 for M.S. or $24,000 for Ph.D.
Tuition Waiver: A tuition waiver of up to 12 credit hours per semester and 6-12 credit hours during summer sessions (depending on previous enrollment) is provided with the GRA.
Health Insurance: Students on assistantships are provided health insurance at a reduced rate. 
GRA Availability: Summer or Fall 2020

Application: To be considered for this position, please send a cover letter outlining your interests, research background, and career aspirations as they pertain to this position; a resume or curriculum vitae; copies of transcripts (unofficial); unofficial copies of GRE scores; and contact information for 3 professional references (name, email, phone, address) combined in a single PDF file with the file name formatted as lastname_firstname to Dr. Yi Qi (yi.qi@unl.edu). Review of applications will begin immediately and the position will remain open until filled.

Comments

Popular posts from this blog

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...

Hazard Mapping Spatial Planning Evacuation Planning GIS

Geographic Information Systems (GIS) play a pivotal role in disaster management by providing the tools and frameworks necessary for effective hazard mapping, spatial planning, and evacuation planning. These concepts are integral for understanding disaster risks, preparing for potential hazards, and ensuring that resources are efficiently allocated during and after a disaster. 1. Hazard Mapping: Concept: Hazard mapping involves the process of identifying, assessing, and visually representing the geographical areas that are at risk of certain natural or human-made hazards. Hazard maps display the probability, intensity, and potential impact of specific hazards (e.g., floods, earthquakes, hurricanes, landslides) within a given area. Terminologies: Hazard Zone: An area identified as being vulnerable to a particular hazard (e.g., flood zones, seismic zones). Hazard Risk: The likelihood of a disaster occurring in a specific location, influenced by factors like geography, climate, an...

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

Scope of Disaster Management

Disaster management refers to the systematic approach to managing and mitigating the impacts of disasters, encompassing both natural hazards (e.g., earthquakes, floods, hurricanes) and man-made disasters (e.g., industrial accidents, terrorism, nuclear accidents). Its primary objectives are to minimize potential losses, provide timely assistance to those affected, and facilitate swift and effective recovery. The scope of disaster management is multifaceted, encompassing a series of interconnected activities: preparedness, response, recovery, and mitigation. These activities must be strategically implemented before, during, and after a disaster. Key Concepts, Terminologies, and Examples 1. Awareness: Concept: Fostering public understanding of potential hazards and appropriate responses before, during, and after disasters. This involves disseminating information about risks, safety measures, and recommended actions. Terminologies: Hazard Awareness: Recognizing the types of natural...

Role of Geography in Disaster Management

Geography plays a pivotal role in disaster management by facilitating an understanding of the impact of natural disasters, guiding preparedness efforts, and supporting effective response and recovery. By analyzing geographical features, environmental conditions, and historical data, geography empowers disaster management professionals to identify risks, plan for hazards, respond to emergencies, assess damage, and monitor recovery. Geographic Information Systems (GIS) serve as crucial tools, providing critical spatial data for informed decision-making throughout the disaster management cycle. Key Concepts, Terminologies, and Examples 1. Identifying Risk: Concept: Risk identification involves analyzing geographical areas to understand their susceptibility to specific natural disasters. By studying historical events, topography, climate patterns, and environmental factors, disaster management experts can predict which regions are most vulnerable. Terminologies: Hazard Risk: The pr...