Systems and methods for ecosystem credit recommendations
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. The collection is automatically updated to maintain a current profile.
1. A method of maintaining a collection of ecosystem credit tokens, comprising:
accessing, by a computing node, an ecosystem attribute target profile for the collection of ecosystem credit tokens, wherein the target profile comprises a set of one or more ecosystem characteristics, quantities of one or more ecosystem characteristics, and permanence of one or more ecosystem characteristics;
accessing, by the computing node, a set of ecosystem credit tokens, 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;
for each ecosystem credit token of the set of ecosystem credit tokens:
detecting at least one difference between the ecosystem credit token data record and a field object comprising field metadata;
generating an updated data record comprising:
the values from the immutable data record if the immutable data record and field object values are the same or if the field object values are missing; and
the values from the field data object if immutable data record and field object values are different or if the immutable data record values are missing;
applying an ecosystem attribute quantification method to the updated data record;
determining a probability of reversal of an ecosystem attribute;
automatically triggering modification of the ecosystem credit token if applying an ecosystem attribute quantification method to the updated data record produces a change in program eligibility or a change in an ecosystem attribute;
determining, by the computing node, a current profile of the collection of ecosystem credit tokens, wherein the current profile comprises: the unique set of ecosystem characteristics within the collection, quantities of the ecosystem characteristics within the collection, and permanence of the ecosystem characteristics within the collection; and
automatically updating the collection of ecosystem credit tokens to maintain a current profile matching the target profile.
2. The method of claim 1 , wherein the set of ecosystem credit tokens is associated with a single user ID.
3. The method of claim 1 , wherein the set of ecosystem credit tokens is associated with one or more product identifier.
4. The method of claim 1 , wherein determining a probability of reversal of an ecosystem attribute comprises:
accessing remote sensing data comprising a plurality of fields;
accessing historical farming practice data for a plurality of fields;
training one or more machine learning algorithm to predict one or more ecosystem attribute;
accessing the field object comprising field metadata, wherein the field metadata comprises at least one management event derived from remote sensing data; and
applying the trained machine learning model to the accessed field object to generate a probability of reversal of an ecosystem attribute.
5. The method of claim 4 , wherein the accessed data comprise time series data.
6. The method of claim 4 , further comprising accessing a methodology of the ecosystem credit token data record.
7. The method of claim 1 , further comprising, for each ecosystem credit token of the set of ecosystem credit tokens:
detecting at least one difference between the ecosystem credit token data record and a field object comprising field metadata;
generating an updated data record comprising:
the values from the immutable data record if the immutable data record and field object values are the same or if the field object values are missing; and
the values from the field data object if immutable data record and field object values are different or if the immutable data record values are missing;
applying an ecosystem attribute quantification method to the updated data record; and
automatically triggering modification of the ecosystem credit token if applying an ecosystem attribute quantification method to the updated data record produces a change in program eligibility or a change in an ecosystem attribute.
8. The method of claim 7 , wherein updating the collection of ecosystem credit tokens comprises adding additional ecosystem credit tokens.
9. The method of claim 7 , wherein additional ecosystem credit tokens are added to the collection if applying an ecosystem attribute quantification method to the updated data record results in: an ecosystem credit of one or more ecosystem credit tokens becoming ineligible for a program, or a decrease in a quantification of an ecosystem attribute.
10. The method of claim 1 , wherein each field object comprises one or more field-level farming practice generated by accessing remote sensing data for the boundary of the ecosystem credit token data record.
11. The method of claim 1 , wherein each field object comprises data continuously received from one or more sources.