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Crop yield forecasting is the methodology of predicting crop yields prior toharvest. The availability of accurate yield prediction frameworks have enormousimplications from multiple standpoints, including impact on the crop commodityfutures markets, formulation of agricultural policy, as well as crop insurancerating. The focus of this work is to construct a corn yield predictor at thecounty scale. Corn yield (forecasting) depends on a complex, interconnected setof variables that include economic, agricultural, management and meteorologicalfactors. Conventional forecasting is either knowledge-based computer programs(that simulate plant-weather-soil-management interactions) coupled withtargeted surveys or statistical model based. The former is limited by the needfor painstaking calibration, while the latter is limited to univariate analysisor similar simplifying assumptions that fail to capture the complexinterdependencies affecting yield. In this paper, we propose a data-drivenapproach that is "gray box" i.e. that seamlessly utilizes expert knowledge inconstructing a statistical network model for corn yield forecasting. Ourmultivariate gray box model is developed on Bayesian network analysis to builda Directed Acyclic Graph (DAG) between predictors and yield. Starting from acomplete graph connecting various carefully chosen variables and yield, expertknowledge is used to prune or strengthen edges connecting variables.Subsequently the structure (connectivity and edge weights) of the DAG thatmaximizes the likelihood of observing the training data is identified viaoptimization. We curated an extensive set of historical data (1948-2012) foreach of the 99 counties in Iowa as data to train the model.

A Bayesian Network approach to County-Level Corn Yield Prediction using historical data and expert knowledge