The Climate Corporation

Lijuan Xu

Ying Xu

Sanjay Lamsal

Ankur Gupta

Anahita Hassanzadeh

A computer system and computer-implemented techniques for determining and presenting improved seeding rate recommendations for planting seeds in a field are provided. In an embodiment, a computer-implemented method includes receiving digital data representing planting parameters including seed type information and planting row width, and retrieving a set of seeding models based upon the planting parameters, where each of the seeding models includes a regression model defining a relationship between plant yield and seeding rate on a specific field. The method also includes generating an empirical mixture model as a composite distribution of the set of seeding models, generating a seeding rate distribution for the planting parameters based upon the empirical mixture model, and calculating a seeding rate recommendation based on the seed rate distribution. The method then also includes planting plant seeds in the specific field consistent with the seeding rate recommendation.

Method for recommending seeding rate for corn seed using seed type and sowing row width

A method for determining national crop yields during the growing season is provided. In an embodiment, a server computer system receives agricultural data records for a particular year that represent covariate data values related to plants at a specific geo-location at a specific time. The system aggregates the records to create geo-specific time series for a geo-location over a specified time. The system creates aggregated time series from a subset of the geo-specific time series. The system selects a representative feature from the aggregated time series and creates a covariate matrix for each specific geographic area in computer memory. The system determines a specific crop yield for a specific year using linear regression to calculate the specific crop yield from the covariate matrix. The system determines a forecasted crop yield for the specific year using a sum of the specific crop yields for the specific year, as adjusted.

Forecasting national crop yield during the growing season

A computer implemented method for generating digital models of relative crop yield based on nitrate values in the soil is provided. In an embodiment, nitrate measurements from soil during a particular portion of a crop's development and corresponding crop yields are received by an agricultural intelligence computing system. Based, at least in part, on the nitrate measurements and corresponding crop yields, the system determines maximum yields for each location of a plurality of locations. The system then converts each crop yield value into a relative crop yield by dividing the crop yield value by the maximum crop yield for the location. Using the relative crop yields and the corresponding nitrate values in the soil, the system generates a digital model of relative crop yield as a function of nitrate in the soil during the particular portion of the crop's development. When the system receives nitrate measurements from soil in a particular field during the particular portion of a crop's development, the system computes a relative yield value for the particular field using the model of relative crop yield.

Generating digital models of relative yield of a crop based on nitrate values in the soil

A computer system and computer-implemented techniques for determining and presenting improved seeding rate recommendations for sowing hybrid seeds in a field is provided. In an embodiment, determining and presenting seeding rate recommendations for a field may be accomplished using a server computer system that receives over a digital communication network, electronic digital data representing hybrid seed properties, including hybrid seed type, and sowing row width. Using digitally programmed seeding query logic, within the server computer system, receiving digital data representing planting parameters including hybrid seed type information and sowing row width. The seeding query logic then retrieves a set of one or more seeding models from an electronic digital seeding data repository based upon the planting parameters. Each of the seeding model retrieved contain a regression model for the hybrid seed type modeling a relationship between plant yield and seeding rate on a specific field. Using mixture model logic, within the server computer system, generating an empirical mixture model in digital computer memory that represents a composite distribution of the set of one or more seeding models. The mixture model logic then generates an optimal seeding rate distribution dataset in digital computer memory based upon the empirical mixture model, where the optimal seeding rate distribution dataset represents the optimal seeding rate across all measure fields. Using optimal seeding rate recommendation logic, within the server computer system, calculating and presenting on a digital display device an optimal seeding rate recommendation that is based upon the optimal seeding rate distribution dataset.

A method for determining national crop yields during the growing season is provided. In an embodiment, a server computer system receives agricultural data records for a particular year that represent covariate data values related to plants at a specific geo-location at a specific time. The system aggregates the records to create geo-specific time series for a geo-location over a specified time. The system creates aggregated time series from a subset of the geo-specific time series. The system selects a representative feature from the aggregated time series and creates a covariate matrix for each specific geographic area in computer memory. The system determines a specific state crop yield for a specific year using linear regression to calculate the specific state crop yield from the covariate matrix. The system determines a national crop yield for the specific year using the sum of the state crop yields for the specific year nationally adjusted.

A method for predicting field specific crop yield recommendations is disclosed. A computer system receives data records, including remotely sensed spectral property of plant records and soil moisture records. The system aggregates the records to create geo-specific time series over a specified time. The system selects representative features from the geo-specific time series and creates, for each specific geographic area, a covariate matrix in computer memory comprising the representative features. The system assigns a probability value to a component group in a set of parameter component groups, where each component group includes one or more regression coefficients and an error term calculated from probability distributions. The system is programmed to generate generates the probability distributions used to determine the regression coefficients and the error term, the probability distribution used to generate the error term is defined with a mean parameter set at zero and a variance parameter set to a field specific bias coefficient.

Forecasting field level crop yield during a growing season

A method for determining national crop yields during a growing season is accomplished using a server computer system that receives observed agricultural data records for a specific geo-location at a specific time. The server calculates weather index values from the agricultural data records that represent crop stress on plants. Geo-specific weather indices are generated from the weather index values, which then are aggregated to generate aggregated weather index data series. Representative features are selected from each aggregated weather index data series to create a covariate matrix for each geographic area. Crop yield for the geographic area is calculated using a linear regression model based on the covariate matrix for the specific geographic area. The server determines a national crop yield for the specific year as a sum of the crop yields for the specific geographic areas nationally adjusted using national yield adjustment instructions.

Forecasting national crop yield during the growing season using weather indices

In an embodiment, a method comprises: receiving digital yield data representing yields of crops that have been harvested from an agricultural field; applying an empirical cumulative density function to the digital yield data to generate transformed digital yield data; smoothing the transformed digital yield data to result in generating and storing smooth transformed digital yield data; determining a first count value for a plurality of management classes; generating a plurality of first management zones for the agricultural field by clustering the smooth transformed digital yield data and using the first count value; generating a set of first merged management zones by merging one or more small management zones of the plurality of first management zones with their respective similar neighboring large zones; and storing the set of first merged management zones and the first count value in a set of management zone metrics.

Delineating management zones based on historical yield maps

A method for determining national crop yields during the growing season using regional agricultural data is provided. In an embodiment, determining national crop yields during the growing season may be accomplished using a server computer system that receives, via a network, agricultural data records that are used to forecast a national crop yield for a particular year. Within the server computer system an agricultural time series module receives one or more agricultural data records that represent a type of covariate data value related to plants at a specific geo-location at a specific time. The agricultural time series module then aggregates the agricultural data records to create one or more geo-specific time series that represent a specific geo-location over a specified time. The agricultural time series module creates one or more aggregated time series that represent geographic areas from a subset of the one or more geo-specific time series. A crop yield estimating module selects a representative feature from the one or more aggregated time series and creates a covariate matrix for each specific geographic area in computer memory of the server computer system. The crop yield estimating module determines a specific state crop yield for a specific year by using a linear regression module to calculate the specific state crop yield from the covariate matrix that represents the specific state for that specific year. The crop estimation module determines a national crop yield for the specific year by using the distribution generation module to calculate the national crop yield for a specific year from the sum of the specific state crop yields for the specific year nationally adjusted using a national yield adjustment module.

A method for determining national crop yields during the growing season may be accomplished using a system that receives agricultural data records that are used to forecast a national crop yield for a particular year. Weather index values are calculated and aggregated from the agricultural data records. Crop yield estimating instructions select representative features from aggregated weather index data and create a covariate matrix for each specific geographic area. Linear regression instructions calculate the crop yield for the specific geographic area for the specific year using the corresponding covariate matrix for that specific year. The crop estimating instructions determine a national crop yield for the specific year using the sum of the crop yields for the specific geographic areas for the specific year nationally adjusted using national yield adjustment instructions. In an embodiment, the crop yield may refer to a specific crop yield such as corn yield.

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Foster City, CA, United States of America

Lijuan Xu