Mechanistic modeling for agroecological adaptation: genetic potential, uncertainty, and in silico design.

Abstract

Biological adaptation is increasingly studied through data-rich descriptions of environments, organisms, and their interactions. In crop systems, this challenge is especially concrete: genetic potential, physiological regulation, soil-water dynamics, climate variability, and management decisions jointly determine performance across heterogeneous landscapes. Mechanistic crop models provide a way to organize this information because their parameters, equations, and assumptions make biological interpretation possible. Yet current practice still emphasizes calibration and prediction more strongly than the explicit evaluation of mechanistic adequacy under independently characterized environments, quantified uncertainty, and competing computational descriptions. This Perspective argues that mechanistic models are most useful for agroecological transitions when treated as explicit and revisable hypotheses about biological dynamics. Using rainfed rice under drought as a methodological application, it discusses how environmental domains can be defined before crop response is modeled, how uncertainty and structural adequacy can be examined, how artificial intelligence can complement rather than replace mechanistic reasoning, and how genetically grounded parameter spaces can support in silico exploration of adaptation options. It therefore proposes a transparent evidence-to-design framing in which environmental characterization, mechanistic representation, model evaluation, and in silico adaptation design are treated as connected components of a single analytical workflow. This framing aligns crop modeling with broader questions in bioinformatics, systems modeling, and ecosystem dynamics: how exchanges of genetic, environmental, physiological, and computational information can be represented in ways that remain transparent enough for scientific scrutiny and useful enough for design.