Abstract: A computing system includes a processor; and one or more non-transitory, computer-readable storage media storing: a trained machine learning model; and machine-readable instructions that, when executed by the one or more processors, cause the computing system to: process agronomic input feature vectors to generate one or more predicted corn growth efficiency values; and provide the corn growth efficiency values as output. A computing system includes a processor; and one or more non-transitory, computer-readable storage media storing machine-readable instructions that, when executed by the one or more processors, cause the computing system to: process labeled agronomic data with a machine learning model to generate one or more predicted corn growth efficiency values; and modify a parameter of the machine learning model.

Abstract: A computing system includes a processor and a memory having instructions stored thereon that, when executed by the one or more processors, cause the computing system to: determine critical level data, generate a spatial critical level map, generate soil nutrients recommendations data, and cause the spatial critical level map and the soil nutrients recommendations data to be displayed. A method includes determining critical level data, generating a spatial critical level map, generating soil nutrients recommendations data, and causing the spatial critical level map and the soil nutrients recommendations data to be displayed. A non-transitory computer readable medium includes program instructions that when executed by one or more processors, cause a computer to determine critical level data, generate a spatial critical level map, generate soil nutrients recommendations data, and cause the spatial critical level map and the soil nutrients recommendations data to be displayed.

Abstract: A computing system includes a processor and a memory having instructions stored thereon that, when executed by the one or more processors, cause the computing system to: determine a geographic field boundary, obtain topographic data, generate a digital elevation model including derivative data, determine critical level data and generate soil nutrients recommendations data. A method includes determining a geographic field boundary, obtaining topographic data, generating a digital elevation model including derivative data, determining critical level data; and generating the soil nutrients recommendations data. obtaining, by one or more processors, topographic data within an area of land.

Abstract: A computing system includes a processor and a memory having instructions stored thereon that, when executed by the one or more processors, cause the computing system to: determine a geographic field boundary, obtain topographic data, generate a digital elevation model including derivative data, determine critical level data and generate soil nutrients recommendations data. A method includes determining a geographic field boundary, obtaining topographic data, generating a digital elevation model including derivative data, determining critical level data; and generating the soil nutrients recommendations data. obtaining, by one or more processors, topographic data within an area of land.

Abstract: A method for determining topographic attributes and environmental characteristics for environments includes determining respective topographic attributes and respective underlying environmental characteristics by processing raw machine data corresponding to environments encoded into respective hexagrid data structures; and storing the respective hexagrid data structures in memory. A computing system includes a processor; and a memory storing instructions that, when executed by the one or more processors, cause the computing system to determine respective topographic attributes and respective underlying environmental characteristics by processing raw machine data corresponding to environments encoded into respective hexagrid data structures; and store the respective hexagrid data structures in memory.

Abstract: A computing system includes a processor; and one or more non-transitory, computer-readable storage media storing: a trained machine learning model; and machine-readable instructions that, when executed by the one or more processors, cause the computing system to: process agronomic input feature vectors to generate one or more predicted corn growth efficiency values; and provide the corn growth efficiency values as output. A computing system includes a processor; and one or more non-transitory, computer-readable storage media storing machine-readable instructions that, when executed by the one or more processors, cause the computing system to: process labeled agronomic data with a machine learning model to generate one or more predicted corn growth efficiency values; and modify a parameter of the machine learning model.

Abstract: A method includes receiving raw machine data; determining topographic attributes and underlying environmental characteristics; encoding the respective topographic attributes and the underlying environmental characteristics in hexagrid data structures; and storing the hexagrid data structures in a memory. A computing system includes a processor; and a memory storing instructions that, when executed by the one or more processors, cause the computing system to: receive raw machine data; determine topographic attributes and underlying environmental characteristics; encode the respective topographic attributes and the underlying environmental characteristics in hexagrid data structures; and store the hexagrid data structures in the memory.

Abstract: Example machine learning (ML) methods and systems for characterizing corn growth efficiency (CGE), and generating field management recommendations based on CGE values are disclosed. An example computing system includes one or more processors, and storage media. The media storing an ML model trained using a training agronomic data set labeled with one or more known CGE values corresponding to one or more trial agricultural fields. The media further storing instructions that, when executed, cause the system to: obtain a production agronomic data set corresponding to a target agricultural field; determine one or more input feature vectors based on the production agronomic data set; process the one or more input feature vectors, with the ML model, to generate one or more predicted CGE values for one or more portions of the target agricultural field; and provide the one or more predicted CGE values as an output.

Abstract: A method includes receiving raw machine data; determining topographic attributes and underlying environmental characteristics; encoding the respective topographic attributes and the underlying environmental characteristics in hexagrid data structures; and storing the hexagrid data structures in a memory. A computing system includes a processor; and a memory storing instructions that, when executed by the one or more processors, cause the computing system to: receive raw machine data; determine topographic attributes and underlying environmental characteristics; encode the respective topographic attributes and the underlying environmental characteristics in hexagrid data structures; and store the hexagrid data structures in the memory.

Abstract: A computer-implemented method for providing an agricultural recommendation based on environmental matching includes collecting an underlying environmental characteristic corresponding to an agricultural environment, generating matches of the agricultural environment to a trials; and determining the recommendation based on the matches. A computing system includes a processor and a memory storing instructions that, when executed by the one or more processors, cause the computing system to collect an environmental characteristic corresponding to an agricultural environment, generate matches of the agricultural environment to trials; and determine the recommendation based on the matches. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to collect an environmental characteristic corresponding to an agricultural environment, generate matches of the agricultural environment to trials; and determine the recommendation based on the matches.

Abstract: A computer-implemented method for providing an agricultural recommendation based on environmental matching includes collecting an underlying environmental characteristic corresponding to an agricultural environment, generating matches of the agricultural environment to a trials; and determining the recommendation based on the matches. A computing system includes a processor and a memory storing instructions that, when executed by the one or more processors, cause the computing system to collect an environmental characteristic corresponding to an agricultural environment, generate matches of the agricultural environment to trials; and determine the recommendation based on the matches. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to collect an environmental characteristic corresponding to an agricultural environment, generate matches of the agricultural environment to trials; and determine the recommendation based on the matches.

Abstract: A method for environmental matching includes receiving first and second observations, identifying characteristics of interest, computing an variance-covariance matrix for the characteristics, computing a spatial distance function for the first observations using a distribution of the second observations, and identifying matching observations. A computing system includes a processor and a memory storing instructions that when executed by the processor cause the system to receive first and second observations, identify characteristics of interest, compute a variance-covariance matrix for the characteristics, compute a spatial distance function for the first observations using a distribution of the second observations, and identify matching observations.