Abstract: A system for implementing a trial a field is provided. In an embodiment, the system is configured to generate a trial recommendation for a field and, based on field data for the field, compute a yield probabilities for the field. The system is also configured to generate a plurality of outcome-based values for the field based on the yield probabilities, compute crop values for each of the outcome-based values and a bushel per acre value, and cause display of an interface that dynamically displays each of the plurality of outcome-based values for the field based on a selected bushel per acre value. The system is further configured to receive user input changing a position of an interactive sliding widget in the interface, to change the bushel per acre value, and in response, compute a crop value for each of the outcome-based values based on the changed bushel per acre value.

Abstract: A system for implementing a trial a field is provided. In an embodiment, the system is configured to generate a trial recommendation for a field and, based on field data for the field, compute a yield probabilities for the field. The system is also configured to generate a plurality of outcome-based values for the field based on the yield probabilities, compute crop values for each of the outcome-based values and a bushel per acre value, and cause display of an interface that dynamically displays each of the plurality of outcome-based values for the field based on a selected bushel per acre value. The system is further configured to receive user input changing a position of an interactive sliding widget in the interface, to change the bushel per acre value, and in response, compute a crop value for each of the outcome-based values based on the changed bushel per acre value.

Abstract: Systems and methods are provided for directing crop disease treatments to agricultural fields. An example computer-implemented method includes receiving a user input from a user at a communication device associated with an interface indicative of likelihood of a first disease in a plurality of agricultural fields and, in response, determining a likelihood of occurrence of the first disease for ones of the plurality of agricultural fields. The method then also includes generating an interface indicative of the likelihood of occurrence of the first disease in the ones of the plurality of agricultural fields and causing the interface to be displayed at the communication device associated with the user.

Abstract: Systems and methods for improving the training of machine learning models to generate probability distributions of yield values are presented. In an embodiment, a system stores a machine learning system trained to compute parameters for a probability distribution of yield values based on seeding density, seed type, and information specific to a field. The system receives inputs for a particular field and computes parameters for a probability distribution of yield. The system generates a probability distribution of yield using the parameters and uses the probability distribution to generate a yield guarantee value. The system supplies the yield guarantee value to a field manager computing device with a seed type and/or seed density recommendation.

Abstract: Systems and methods for predicting likelihoods of multiple crop disease types for target plots. An example computer-implemented method includes receiving a request for a crop disease prediction related to treatment of a target plot for one or more crop diseases and accessing a multiple disease joint model consistent with location data included in the request. The computer-implemented method also includes determining, via the multiple disease joint model, first and second disease likelihood output based on at least the location data, where the first and second disease likelihood outputs are each associated with a different one of the multiple disease types, and generating a treatment recommendation based on the first and second disease likelihood outputs. The computer-implemented method then includes directing application of at least one treatment to the target plot, based on the treatment recommendation output.

Abstract: A system for implementing a trial a field is provided. In an embodiment, the system is configured to generate a trial recommendation for a field and, based on field data for the field, compute a yield probabilities for the field. The system is also configured to generate a plurality of outcome-based values for the field based on the yield probabilities, compute crop values for each of the outcome-based values and a bushel per acre value, and cause display of an interface that dynamically displays each of the plurality of outcome-based values for the field based on a selected bushel per acre value. The system is further configured to receive user input changing a position of an interactive sliding widget in the interface, to change the bushel per acre value, and in response, compute a crop value for each of the outcome-based values based on the changed bushel per acre value.

Abstract: A system for implementing a trial in one or more fields is provided. In an embodiment, an agricultural intelligence computing system receives field data for a plurality of agricultural fields. Based, at least in part, on the field data for the plurality of agricultural fields, the agricultural intelligence computing system identifies one or more target agricultural fields. The agricultural intelligence computing system sends, to a field manager computing device associated with the one or more target agricultural fields, a trial participation request. The server receives data indicating acceptance of the trial participation request from the field manager computing device. The server determines one or more locations on the one or more target agricultural fields for implementing a trial and sends data identifying the one or more locations to the field manager computing device.

Abstract: Systems and methods are provided for use in applying treatments to crops in fields. One example computer-implemented method includes calculating a growth stage of a crop in a field on a defined date based on planting data and weather data for the field and/or crop, and then, in response to the growth stage being within a spray window for the crop, defining a plurality of synthetic sprays within the spray window for the field. The method then includes, for each one of the synthetic sprays, calculating at least one disease risk for the crop in the field and calculating a response to the synthetic spray. The method then further includes compiling a report including a selected one or more of the responses, based on yield differences of the responses, as a recommendation for applying the treatment to the crop consistent with the synthetic spray associated with the selected one or more of the responses.

Abstract: A system for implementing a trial in one or more fields is provided. In an embodiment, an agricultural intelligence computing system receives field data for a plurality of agricultural fields. Based, at least in part, on the field data for the plurality of agricultural fields, the agricultural intelligence computing system identifies one or more target agricultural fields. The agricultural intelligence computing system sends, to a field manager computing device associated with the one or more target agricultural fields, a trial participation request. The server receives data indicating acceptance of the trial participation request from the field manager computing device. The server determines one or more locations on the one or more target agricultural fields for implementing a trial and sends data identifying the one or more locations to the field manager computing device.