🤖 AI Summary
This study addresses longstanding challenges in agricultural experimentation, where traditional statistical analyses often suffer from a disconnect between experimental design and computational implementation, leading to subjective model specification, mischaracterized error structures, and biased interpretation of interaction effects. To resolve these issues, the authors propose the first declarative Python framework that deeply embeds experimental design semantics into the analytical workflow. The framework automatically translates complex designs—such as randomized complete blocks, split-plots, and multi-environment trials—into valid linear or mixed-effects models, accurately identifies error strata, performs hypothesis tests and mean comparisons, and unifies ANOVA, mixed-model inference, and stability analysis. By rigorously enforcing correct interpretation of hierarchical and interaction effects, the approach maintains consistency with classical methodologies while substantially enhancing inferential accuracy, reproducibility, and reliability.
📝 Abstract
Statistical analysis of agricultural experiments is based on structured experimental designs such as randomized block, factorial, split-plot, and multi-environment trials. While the theoretical bases of these approaches are sound, their implementation in modern programming frameworks usually involves manual specification of statistical models, choice of error terms, and subjective interpretation of interaction effects. This divide between experimental design and computational implementation opens the door to misleading inference and inconsistent reporting. We introduce AgroDesign, a Python framework that makes experimental design the central specification of statistical analysis. The framework translates specified experimental designs directly into valid linear models, automatically identifies error strata, conducts hypothesis testing and mean separation, checks assumptions of linear models, and provides decision-focused interpretations. The framework integrates fixed-effect ANOVA, hierarchical designs, linear mixed models, and genotype-by-environment stability analysis into a single declarative framework. AgroDesign is validated on canonical designs in agricultural statistics and shows consistency with traditional statistical analysis while strictly enforcing correct interpretation constraints, especially in interaction-dominant and multi-stratum designs. By integrating design semantics into computation, the framework minimizes analyst-driven modeling choices and enhances reproducibility.