NCCC_temp170: Research Advances in Agricultural Statistics
(Multistate Research Coordinating Committee and Information Exchange Group)
Status: Draft Project
NCCC_temp170: Research Advances in Agricultural Statistics
Duration: 10/01/2026 to 09/30/2031
Administrative Advisor(s):
NIFA Reps:
Non-Technical Summary
Statement of Issues and Justification
Statisticians and data scientists who consult or collaborate with subject-matter scientists and actively participate in research at an Agricultural Experiment Station (AES) have a unique role in fulfilling the land grant mission of their institution. Consultations with faculty and students provide a fundamental service for research by ensuring a rigorous experimental design and analysis that meet scrutiny under peer review. Collaborations occur across a broad range of disciplines including animal and dairy science, crop and soil science, entomology, food science and technology, horticulture, plant pathology, poultry science, and many others. Furthermore, these faculty often conduct research of their own and contribute to the advancement of their specific sub-field and local institution. As a result, those providing analytical expertise at an AES must have a broad understanding of statistical methodology that can be applied well beyond their domain-specific research areas. The NCCC-170 multi-state coordinating committee provides a mechanism to (i) bring together AES statisticians from academia, research stations, government, and industry (ii) create resources directed to support the discussion of evolving issues needing to be addressed, and (iii) facilitate the development of educational resources and curricula. The resulting exchange of ideas and the sharing of knowledge is of benefit to all project members, as well as to the broad scope of research conducted at the institutions they represent.
The charge to remain current in the statistical profession is especially challenging given the recent development and widespread adoption of machine learning (ML) and artificial intelligence (AI) techniques. These emerging ML and AI techniques advance the analytical possibilities of designed experiments and observational studies, requiring rigorous, computationally intensive, and sometimes novel methodologies that ensure the quality of the data and the inferences drawn. In particular, emerging ML and AI techniques are revolutionizing the scale at which high-throughput agronomic data can be obtained and analyzed. Because statistics is the analytical backbone of ML and AI, AES statisticians and data scientists have a critical responsibility to agriculture to ensure that these innovative approaches are implemented and interpreted correctly, and deployed in a timely manner. This requires us to keep abreast of the latest research, as well as to work to develop methodology appropriate to their consulting and collaborative responsibilities. Developing guidelines on best practices in the use of ML and AI is essential for promoting awareness and avoiding misleading uses of ML and AI in data analysis.
Objectives
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To address technical concerns associated with the development of modern statistical methodology and its software implementation, as motivated by problems in agricultural research
Comments: Including but not limited to: 1. General and generalized linear models including non-normal distributions, categorical outcomes, and challenges related to hierarchical/multilevel models 2. 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; 3. Deep learning and generative AI models; 4. Data fusion to integrate data of different temporal and spatial resolutions for modeling applications in field studies, geospatial applications, among other applications. -
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.
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To advocate for and enhance visibility of agricultural statistics by means of a) mentoring colleagues interested in incorporating agricultural statistics into their research programs, b) actively nominating members for professional awards and c) educating other statisticians and administrators about the scholarship contributions of AES statisticians and their role in the scientific community, as well as that of Departments of Statistics at land grant and agricultural research institutions.
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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.