The Climate Corporation

Jyoti Dharna

Morrison Jacobs

Allan Trapp

Beiyan Zeng

Jason Kendrick Bull

Xiao Yang

Tonya S Ehlmann

Yao Xie

Atif Khan

Michael David Lewis

Shalom Friss

Christopher P Aulbach

Jason R Sontheimer

A computer-implemented method for improving crop yield in fields includes accessing data representative of predicted performance of seeds for a field and creating an experiment for the field based on the predicted performance of the seeds. The experiment includes a specific prescription to plant the seeds in the field. The method also includes transmitting the prescription to a planter and/or a harvester associated with the field and collecting, in real time, planting data for the seeds as the seeds are planted in the field and harvesting data for a crop grown in the field from the planted seeds. The method further includes validating that execution of the experiment in the field is consistent with the prescription and generating a recommendation based on a comparison of the collected harvesting data for the crop grown in the field to the predicted performance of the seeds included in the prescription.

Cross-grower study and field targeting

Techniques for recommending side-by-side plantings of pairs of seeds include a server computer receiving agricultural data records that represent crop seed data describing seed and yield properties of seeds and first data for agricultural fields where the seeds were planted. The server receives second data for available seeds and automatically calculates a dataset of success probability scores that describe the probability of a successful yield on the target fields. Data is organized as pairs to facilitate comparison of actual plantings to optimized plantings that have a probability of success (POS), in terms of yield lift or increased yield season-over-season, for different yield values. Confidence values are generated and stored in association with the POS values and can be used as a basis of visual output to support planting and/or field management decisions as part of an automated intelligent agricultural decision support system.

Computer-implemented recommendation of side-by-side planting in agricultural fields

In an embodiment, a computer-implemented method of tracking soil sampling in a field is disclosed. The method comprises receiving, by a processor, digitally stored field map data and digitally stored sampling data. The method further comprises displaying, by the processor, a field map depicting the first set of sampling points in a computer-generated graphical user interface. In addition, the method comprises receiving a selection of a first sampling point and displaying first sampling data associated with the first sampling point. The method also comprises receiving an update indicating that a soil sample has been collected at the first sampling point. Finally, the method comprises determining a second sampling point at which a next soil sample is to be collected and displaying the second sampling point in the field map.

Automated sample collection and tracking system

A computer-implemented method of targeting grower fields for crop yield lift is disclosed. The method comprises receiving, by a processor, crop seeding rate data and corresponding crop yield data over a period of time regarding a group of fields associated with a plurality of grower devices; receiving, by the processor, a current seeding rate for a grower's field associated with one of a plurality of grower devices; determining, whether the grower's field will be responsive to increasing a crop seeding rate for the grower's field from the current seeding rate to a target seeding rate based on the crop seeding rate data and corresponding crop yield data; preparing, in response to determining that the grower's field will be responsive, a prescription including a new crop seeding rate and a specific hybrid to be implemented in the grower's field.

Techniques for recommending side-by-side plantings of pairs of hybrids or seeds include a server computer receiving agricultural data records that represent crop seed data describing seed and yield properties of hybrid seeds and first data for agricultural fields where the hybrid seeds were planted. The server receives second data for available hybrids and seeds and automatically calculates a dataset of success probability scores that describe the probability of a successful yield on the target fields. Data is organized as pairs to facilitate comparison of actual plantings to optimized plantings that have a probability of success (POS), in terms of yield lift or increased yield season-over-season, for different yield values. Confidence values are generated and stored in association with the POS values and can be used as a basis of visual output to support planting and/or field management decisions as part of an automated intelligent agricultural decision support system.

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San Francisco, CA, United States of America

Jyoti Dharna