An Integration Framework for Agricultural Analytics

Assembles agricultural analyses around a single unit of observation, the management unit within a season, and keeps climate, soil and remote-sensing covariates aligned to it. Covariates are aggregated over phenological windows derived from accumulated growing degree days rather than calendar months, following McMaster and Wilhelm (1997) . Models are validated with spatial resampling by default, since random cross-validation inflates apparent skill when observations are spatially autocorrelated, as shown by Roberts and others (2017) . Prediction intervals use split conformal inference after Lei and others (2018) . Data sources and learning algorithms are supplied through registries so that new providers and methods can be added without modifying the package.