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.

翻译：农业实验的统计分析基于随机区组、析因、裂区及多环境试验等结构化实验设计。尽管这些方法的理论基础坚实，但它们在现代编程框架中的实现通常涉及统计模型的手动设定、误差项的选择以及交互效应的主观解释。实验设计与计算实现之间的这种割裂可能导致误导性推断和不一致的报告。本文介绍AgroDesign，一个将实验设计作为统计分析核心规范的Python框架。该框架将指定的实验设计直接转换为有效的线性模型，自动识别误差层次，进行假设检验和均值比较，检验线性模型假设，并提供面向决策的解释。该框架将固定效应方差分析、层次设计、线性混合模型以及基因型-环境互作稳定性分析集成至统一的声明式框架中。AgroDesign在农业统计学经典设计上得到验证，结果显示其与传统统计分析保持一致，同时严格强制执行正确的解释约束，特别是在交互效应主导和多层次设计中。通过将设计语义融入计算过程，该框架最大限度地减少了分析人员驱动的建模选择，并增强了可重复性。