Abstract: A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

Abstract: Systems, methods, and computer program products for recommending ecosystem credit tokens based on modelled outcomes are provided. In various embodiments, field data comprising geospatial boundaries of one or more field are received. One or more methodology is accessed. For each of the one or more fields, one or more farming practice is accessed, wherein each farming practice comprises a location and time. For each of the one or more fields, for each crop production period, an ecosystem attribute is generated by applying one or more ecosystem attribute quantification methods to each spatially and temporally unique set of one or more farming practices. Selection of one or more program is optimized for each field based on the set of selected programs being compatible within the field and production period.

Abstract: Systems, methods, and computer program products for recommending farming practices based on modelled outcomes are provided. In various embodiments, field data comprising geospatial boundaries of one or more field are received. Management event data comprising one or more management events within the one or more fields is received. For each management event, a management event boundary defining geospatial boundaries is received, and one or more management zones is determined based on the management event boundaries. One or more ecosystem attribute quantification method is applied to each of the one or more management zones to generate one or more ecosystem attributes of the one or more management zones. One or more ecosystem attribute is selected for each management zone. An ecosystem credit token or portion thereof is generated for each selected ecosystem attribute. The ecosystem credit token is associated with a quantity of raw agricultural product.

Abstract: Systems, methods, and computer program products for maintaining a collection of ecosystem credit tokens based on modelled outcomes are provided. In various embodiments, an ecosystem attribute target profile for the collection of ecosystem credit tokens is accessed. The target profile comprises a set of ecosystem characteristics, quantities of one or more ecosystem characteristics, and permanence of one or more ecosystem characteristics. A set of ecosystem credit tokens is accessed, wherein each ecosystem credit token data record comprises one or more of: a validated and verified management event, a methodology, an ecosystem attribute, a boundary, an ecosystem impact, an ecosystem credit, and an ecosystem attribute quantification method. A current profile of the collection of ecosystem credit tokens is determined, wherein the profile comprises the set of ecosystem characteristics within the collection, quantities of ecosystem characteristics, and permanence of ecosystem characteristics.

Abstract: Systems, methods, and computer program products for recommending farming practices based on modelled outcomes are provided. In various embodiments, field data comprising geospatial boundaries of one or more field are received. Management event data comprising one or more management events within the one or more fields is received. For each management event, a management event boundary defining geospatial boundaries is received, and one or more management zones is determined based on the management event boundaries. The determination of one or more management zones comprises sequentially intersecting a geospatial boundary. Each sequential intersection operation creating two branches, one with the intersection of the geometries and one with the difference.

Abstract: A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

Abstract: Systems, methods, and computer program products for recommending farming practices based on modelled outcomes are provided. In various embodiments, field data comprising geospatial boundaries of one or more field are received. Management event data comprising one or more management events within the one or more fields is received. For each management event, a management event boundary defining geospatial boundaries is received, and one or more management zones is determined based on the management event boundaries. The determination of one or more management zones comprises sequentially intersecting a geospatial boundary. Each sequential intersection operation creating two branches, one with the intersection of the geometries and one with the difference.

Abstract: Systems, methods, and computer program products for maintaining a collection of ecosystem credit tokens based on modelled outcomes are provided. In various embodiments, an ecosystem attribute target profile for the collection of ecosystem credit tokens is accessed. The target profile comprises a set of ecosystem characteristics, quantities of one or more ecosystem characteristics, and permanence of one or more ecosystem characteristics. A set of ecosystem credit tokens is accessed, wherein each ecosystem credit token data record comprises one or more of: a validated and verified management event, a methodology, an ecosystem attribute, a boundary, an ecosystem impact, an ecosystem credit, and an ecosystem attribute quantification method. A current profile of the collection of ecosystem credit tokens is determined, wherein the profile comprises the set of ecosystem characteristics within the collection, quantities of ecosystem characteristics, and permanence of ecosystem characteristics.

Abstract: Systems, methods, and computer program products for recommending ecosystem credit tokens based on modelled outcomes are provided. In various embodiments, field data comprising geospatial boundaries of one or more field are received. One or more methodology is accessed. For each of the one or more fields, one or more farming practice is accessed, wherein each farming practice comprises a location and time. For each of the one or more fields, for each crop production period, an ecosystem attribute is generated by applying one or more ecosystem attribute quantification methods to each spatially and temporally unique set of one or more farming practices. Selection of one or more program is optimized for each field based on the set of selected programs being compatible within the field and production period.

Abstract: A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

Abstract: A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

Abstract: A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.