Modeling of crop growth for desired moisture content of bovine feedstuff and determination of harvest windows for high-moisture corn using field-level diagnosis and forecasting of weather conditions and observations and user input of harvest condition states

A system comprising:

• a computing environment including at least one computer-readable storage medium having program instructions stored therein and a computer processor operable to execute the program instructions to profile crop growth for a harvest of a corn crop at a desired moisture content in a harvest advisory model within a plurality of data processing modules, the plurality of data processing modules including:
• a weather modeling module configured to diagnose and predict expected weather conditions at a specific location impacting a moisture content of the corn crop during crop growth by applying weather information including current field-level weather data and forecasted weather data to one or more predictive numerical weather models;
• one or more modules configured to develop an agricultural model of one or more physical and empirical characteristics impacting harvest operations of the corn crop, to enable a harvest operation to obtain a bovine feedstuff comprised of the corn crop at the desired moisture content, by 1) aggregating the expected weather conditions with crop-specific information comprised at least of planting data and bovine feedstuff data that includes a desired moisture content of between 24% and 35% moisture for the bovine feedstuff comprised of the corn to be stored in one or more post-harvest storage structures, into the agricultural model, 2) simulating the moisture content of the corn crop at one or more crop growth stages, and 3) comparing additional data representing sampled one or more observations of a crop moisture content taken at the specific location at times corresponding to the one or more crop growth stage to determine a variance between the crop moisture content and the simulated moisture content expected from in-field dry-down of the crop to modify the simulation based on identified differences between the otherwise simulated moisture content and the one or more sampled observations of the crop moisture content;
• a harvest condition prediction module configured to predict one or more harvest opportunity windows to produce bovine feedstuff having the desired moisture content from the corn crop in a harvest output condition profile for planning a timing of harvest operations and storage of the corn crop;
• and
• an output module configured to initiate a harvest operation from the harvest condition output profile representing the predicted harvest condition to harvest the corn crop at the desired moisture content for the bovine feedstuff, wherein a user performs the harvest operation in the particular field based on the predicted harvest condition for the corn crop, or an automated harvest operation is controlled based on the predicted harvest condition for the corn crop.

A method of evaluating agricultural conditions to support harvest operations and storage of a bovine feedstuff, comprising:

• within a computing environment comprised of a computer processor and at least one computer-readable storage medium operably coupled to the computer processor and having program instructions stored therein, the computer processor being operable to execute the program instructions to profile crop growth for a harvest of a corn crop at a desired moisture content in a harvest advisory model configured to perform the steps of:
• developing an agricultural model of one or more physical and empirical characteristics impacting harvest operations of the corn crop, to enable a harvest operation to obtain bovine feedstuff comprised of the corn crop at the desired moisture content, by
• forecasting weather conditions impacting moisture content during crop growth of the corn crop to be harvested in a specific location, by applying weather information comprised of current field-level weather data and forecasted weather data to one or more predictive numerical weather models;
• simulating the moisture content of the corn crop at one or more crop growth stages by applying the forecasted weather conditions and crop-specific information comprised at least of planting data and bovine feedstuff data that includes a desired moisture content of between 24% and 35% moisture for a bovine feedstuff comprised of corn to be stored in one or more post-harvest storage structures, to the agricultural model, the agricultural model comprising at least one of a crop-specific growth model, a soil model, and a land surface model;
• and
• adjusting the simulation of the moisture content of the corn crop at one or more crop growth stages using at least one of 1) differences between the simulated moisture content and additional data representing in-field observations of the timing of the crop growth as a growing season progresses and sampled observations of a crop moisture content taken at the specific location at times corresponding to the one or more crop growth stages, determined from a variance between the crop moisture content and the simulated moisture content expected from in-field dry-down of the crop, and 2) one or more artificial intelligence models developed to analyze the moisture content of the corn crop at one or more crop growth stages from a comprehensive harvest condition dataset for the agricultural model of the one or more physical and empirical characteristics impacting harvest operations of the corn crop,
• to generate a prediction of one or more harvest opportunity windows to achieve the desired moisture content of the corn crop for storage in one or more of an upright tower silo, a bunker silo, a bag silo, and an oxygen-limiting silo;
• and
• initiating a harvest operation from the harvest condition output profile representing the predicted harvest condition to harvest the corn crop at the desired moisture content for the bovine feedstuff, wherein a user performs the harvest operation in the particular field based on the predicted harvest condition for the corn crop, or an automated harvest operation is controlled based on the predicted harvest condition for the corn crop.