NCCC170

Research Advances in Agricultural Statistics

General objectives of NCCC170:

  1. To identify, foster, and coordinate educational and research efforts in statistics among statisticians serving food and agriculture research programs, thus elevating reputation and advancing collaborative research teams at their home institutions.

  2. To advocate for and champion the visibility of agricultural statisticians and data scientists; and to promote and illuminate the vital contributions across disciplines at all career levels.  This ranges from providing subject matter expertise and feedback during the review of promotion dossiers to promoting their synergistic contributions that amplify the impact of interdisciplinary research.

  3. To provide continuing statistical education to the scientific community through workshops and short courses, thus empowering scientists to conduct specialized analytical techniques and evaluate research that uses those techniques.

  4. To address technical concerns associated with the development of modern statistical methodology and its software implementation, as motivated by problems in agricultural research.  Including but not limited to:

    • General and generalized linear models including non-normal distributions, categorical outcomes, and challenges related to hierarchical/multilevel models;
    • Best practices for reproducible research ranging from data collection and experimental design, data management and curation, methodological and analytic pipelines, computational implementation, quality control protocols and downstream analysis;
    • Deep learning and generative AI models;
    • Data fusion to integrate data of different temporal and spatial resolutions and multi-modalities of data structure, including not only flat tabular data, but also images and videos, sound, unstructured text, for modeling applications.