- Cultivating PhD Aspirations during College , CBE-LIFE SCIENCES EDUCATION (2022)
- Data-Driven Decisions for Food and Energy , (2022)
- Fostering Responsible Innovation through Stakeholder Engagement: Case Study of North Carolina Sweetpotato Stakeholders , Sustainability (2022)
- Nth-plant scenario for forest resources and short rotation woody crops: Biorefineries and depots in the contiguous US , APPLIED ENERGY (2022)
- Nth-plant supply: corn stover supplies and costs in a fleet of biorefineries , BIOFUELS BIOPRODUCTS & BIOREFINING-BIOFPR (2021)
- Replacing liquid fossil fuels and hydrocarbon chemical feedstocks with liquid biofuels from large-scale nuclear biorefineries , APPLIED ENERGY (2021)
- The nth-plant scenario for blended feedstock conversion and preprocessing nationwide: Biorefineries and depots , APPLIED ENERGY (2021)
- Assessment of Perennial Grass Inventories Predicted in the Billion-Ton Studies , Transactions of the ASABE (2018)
- GIS-based allocation of herbaceous biomass in biorefineries and depots , Biomass and Bioenergy (2017)
- An Excel-Based Decision Support System for Supply Chain Design and Management of Biofuels , International Journal of Operations Research and Information Systems (2014)
A Pipeline of a Resilient Workforce that integrates Advanced Analytics to the Agriculture, Food and Energy Supply Chain
The Agricultural DECision Intelligence moDEling System for huMan-AI collaboRative acTion Elicitation and impRovement (DECIDE-SMARTER) project will lay the foundations of democratized access to Decision Intelligence (DI) technology for stakeholders across the agriculture value chain, filling a longstanding gap between technology and decision makers. Through a process of participatory design, the project team will work with stakeholders in the sweetpotato value chain to: 1) Create a software asset that helps growers with an otherwise difficult decision; 2) conduct experiments that inform the best software interfaces possible to support complex agricultural decision making (through characterizing, understanding, and leveraging human cognitive abilities; 3) identify potential sources of bias in the DI process that would present barriers to democratized access to the technology; and 4) develop a reference architecture and prototype implementation of a modeling, simulation, and visualization framework for implementing multiple DI models with agriculture stakeholders. The project will leverage the ongoing research, data acquisition, and stakeholder efforts by the Sweetpotato Analytics for Produce Provenance and Scanning (Sweet-APPS) team, a multi-disciplinary endeavor that aims to reduce agricultural waste and maximize yield for North CarolinaÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s sweet potato growers.
This project addresses the challenges of concentrated manure volumes in hot-spots across NC, limited agricultural land base to accept these nutrients, and the adverse environmental impacts of manure management. We are proposing a the development of a framework to plan sustainable recycling and export of manure nutrients to preserve and restore air, soil, and water resources across the state of North Carolina. This project will leverage datasets, models, and regulations governing manure across NC to develop an alternative supply-chain for manure management to attain beneficial outcomes to the environment, economy, and society. This goal will be accomplished through the following objectives: (1) developing spatially-explicit datasets to quantify and characterize manure associated with different swine and poultry farm types, (2) developing models for upgrading and treatment technologies applicable to these manures, (3) developing technical/economic models to model performance of each technology alone or coupled, and (4) establishing a logistics optimization framework that integrate spatially-explicit residue datasets with compatible upgrading and recycling technologies.
At peanut buying stations across the U.S. South East, peanut grading is currently implemented using labor-intensive equipment. Many of the steps related to grading have been unchanged for decades. A critical reason for this involves political pressures against updating or expediting the grading process. However, like many other economic sectors, new labor-force pressures are requiring that more be done with fewer people. Given that (1) labor is more challenging to come by; and (2) political pressure exists to maintain the status quo, we propose to update key steps in the existing process to simplify and/or expedite data collection. This projectâ€™s goal is to develop automated imaging and weighing technologies that can serve as a bridge, toward more fully automated systems, by addressing key bottlenecks in the existing grading process. We will achieve this by the following objectives: (1) Automate the weighing and grading of peanuts either traveling down or entering the rollers during pod pre-sizing; and (2) Automate the detection of splits and, if possible, sound versus unsound splits, by adding vision systems to the existing sheller.
Inconsistent quality and aesthetics in agricultural crops can result in increased consumer and producer food waste, reduced industry resiliency and decreased farmersÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ and growersÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ profit, poor consumer satisfaction, and inefficiencies across the supply chain. Although there are opportunities to characterize and quantify sources of phenotypic variability across the agricultural supply chain - from cultural practices of growers and producers to storage and handling by distributors - the data available to allow for assessment of horticultural quality drivers are disparate and disconnected. The absence of data integration platforms that link heterogeneous datasets across the supply chain precludes the development of strategies and solutions to constrain variability in produce quality. This projectÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s central hypothesis is that multi-dimensional produce data can be securely integrated and used to optimize management practices in the field while simultaneously adding value across the entire food supply chain. We propose to develop multi-modal sensing platform along with a trust-based, data management, integration, and analytics framework for systematic organization and dynamic abstraction of heterogeneous data across the supply chain of agricultural crops. The projects short term goals are to (1) engage growers to refine research and extension priorities; (2) develop a first-of-its-kind modular imaging system that responds to grower needs by analyzing existing and novel multi-dimensional data; (3) establish the cyberinfrastructure, including analytics and blockchain, to make meaningful inference of the acquired data as related to management practices while ensuring data security; (4) deploy the sensing system at NCSUÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s Horticultural Crops Research Station in Clinton, NC and on a large-scale system at a major commercial farm and distribution facility, and (5) extend findings to producers and regulators through NC Cooperative Extension. The proposed sensing and cyberinfrastructure platforms will be crop-agnostic and our findings will be transferable to other horticultural crops produced in NC and beyond.
- Need for a uniform format for herbaceous feedstock - Briquettes and pellets are a great alternative as they provide compacted storage with a longer shelf live paving the way for lower transportation cost and year-round feedstock availability - NC already has X commercial pelletizing facilities that could be leveraged as feeding pipelines to a larger national industry of value-added herbaceous biomass
Joint appointment release for Dr. Daniela Jones from Idaho National Laboratory with North Carolina State University
Translational research that couples stakeholder needs, sensor technologies, and data analytics to improve sweetpotato yield estimates. This in turn, has the potential to increase food production without increasing the use of our natural resources, decrease the use of synthetic herbicides, pesticides and fertilizers, and increase rural economies. The specific objectives of this project are to: 1) Leverage knowledge from precision agriculture experts, existing research collaborations with industry, and data analytics skills in the IDEALS Lab towards sustainability. 2) Collect, process, store, manage, analyze, visualize, and interpret heterogeneous datasets in the data lifecycle of growing sweetpotatoes. 3) Prepare preliminary data and statistically significant analysis to predict the factors that affect sweetpotato yield and quality (shape and size). 4) Submit a larger grant proposal with supporting preliminary data and analysis to expand this project.
The goal of this project is to increase visibility of the interdisciplinary cutting-edge research performed by the students in the Agricultural & Biological (AG&Bio) Logistics Laboratory, for which Sloan Scholar Daniela Jones is the research PI and mentor. The main objectives of this project are to: foster healthy, mutually beneficial research relationships between NCSU and Idaho National Laboratory (INL); and provide students with a long-lasting professional network, exposure to different career opportunities, and hands-on opportunities with the cutting-edge technologies available at INL. The project will help create interdisciplinary and multi-institutional research and training proposals for submission to DOE, USDA, NSF, the Sloan Foundation, and other funding institutions. These intentional opportunities to exchange research ideas will also be translated in peer-reviewed manuscripts to bridge identified research gaps.
The primary aim of this project will be to create a review report on the state of technology of Bioenergy Carbon Capture and Storage (BECCS).