AgroDesign: A Design-Aware Statistical Inference Framework for Agricultural Experiments in Python

TL;DR

AgroDesign is a Python framework that makes experimental design the central specification of statistical analysis and minimizes analyst-driven modeling choices and enhances reproducibility by integrating design semantics into computation.

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.

Figures (8)

• Figure 1: Design-aware inference pipeline implemented in agrodesign. Phase I maps experimental design to the appropriate statistical model. Phase II performs admissible inference, assumption validation, and decision generation.
• Figure 2: Variety comparison and compact letter display for the RCBD experiment. Letters denote Tukey HSD groupings at $\alpha = 0.05$ after adjusting for block effects.
• Figure 3: Variety comparison after blocking adjustment in the RCBD experiment. Letters denote Tukey HSD groupings at $\alpha = 0.05$. Mean differences reflect within-block contrasts.
• Figure 4: Nitrogen $\times$ Spacing interaction plot for the factorial experiment. Nearly parallel response profiles indicate absence of interaction, supporting marginal interpretation of main effects.
• Figure 5: Irrigation $\times$ Variety interaction in the split-plot experiment. Non-parallel response profiles indicate interaction-dominant inference, requiring interpretation at the combination level.