Includes bibliographical references and index.

Basics of agricultural system models -- The R Programming language and software -- Simulation with dynamic system models -- Statistical notions useful for modeling -- Regression analysis, frequentist -- Uncertainty and sensitivity analysis -- Calibration of systems models -- Parameter estimation with Bayesian methods -- Model evaluation -- Putting it all together in a case study -- Metamodeling -- Multimodel ensembles -- Gene-based crop models -- Data assimilation for dynamic models -- Models as an aid to sampling.