Abstract: A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

Abstract: A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics include a productivity metric.

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics and the valuation for the each of the parcels are expressed relative to all of the parcels within the prescrib

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics and the valuation for the each of the parcels are expressed relative to all of the parcels within the prescrib

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a weighted valuation corresponding to the each of the parcels, where the agricultural metrics and the weighted valuation for the each of the parcels are expressed relative to all of the parcels w

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics include a sustainability metric.

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics include a stability metric.

Abstract: A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

Abstract: A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

Abstract: A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

Abstract: A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

Abstract: A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics and the valuation for the each of the parcels are expressed relative to all of the parcels within the prescrib

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics and the valuation for the each of the parcels are expressed relative to all of the parcels within the prescrib

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics include a stability metric.

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics include a productivity metric.

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a valuation corresponding to the each of the parcels, where the agricultural metrics include a sustainability metric.

Abstract: A method for agricultural land parcel valuation includes: accessing data for parcels within a prescribed region, the data comprising management practices, historical weather conditions, locations and topography, remote sense images, soil types, and crop types; assessing and ranking the management practices for each of the parcels; generating simulation inputs for the each of the parcels, where the simulation inputs comprise highest ranked management practices, the historical weather conditions, the locations and topography, the soil types, and the crop types; simulating crop growth for the each of the parcels over a prescribed number of previous years, where the simulating employs the simulation inputs provided by the generating; and employing selected outputs from the simulating to calculate agricultural metrics and a weighted valuation corresponding to the each of the parcels, where the agricultural metrics and the weighted valuation for the each of the parcels are expressed relative to all of the parcels w