Rethinking yield stability through phenotypic plasticity and its link to modern statistical methods
In the context of advocacy for yield stability, trade-offs between yield and yield stability, the frequent lack of definitions, and the variation in methods when they are explicit, we connect two perspectives: phenotypic plasticity and factor analytics. Phenotypic plasticity brings over a century of research in developmental biology, ecology and evolution, and is gaining traction in crop science. Factor analytics is an advanced linear mixed model of multi-environment data with factor analytic variance structures for the variety-by-environment interaction effect. We (1) review phenotypic plasticity, define agronomically adaptive plasticity where varieties (or practices) consistently return superior yield (or other traits) across environments with no trade-off, and describe percentile-plasticity plots to assess the agronomic value of plasticity; (2) outline factor analytic models, and (3) link plasticity and factor analytic models mathematically and empirically. We show that phenotypic plasticity of cereal yield correlates positively with overall performance obtained from factor analytic models when plasticity is adaptive and negatively when it is maladaptive. We conclude that phenotypic plasticity contributes biological meaning to opaque analytical approaches, and that biologically grounded statistics are needed to challenge weak agronomic narratives, such as advocacy for stability that might reflect decision biases rather than critical consideration of its benefits. HighlightUncritical advocacy for crop yield stability is common. Here we advance a biological-statistical synthesis of yield stability and test theoretical predictions with actual wheat and oat yield data.