Systems and methods for ecosystem credit recommendations

1. A computer-implemented method comprising:

receiving, at a computing node, field data comprising geospatial boundaries of one or more field;

accessing, by the computing node, one or more methodology;

for each of the one or more fields, accessing, by the computing node, one or more farming practice, wherein each farming practice comprises a location and time;

determining, by the computing node, one or more farming practice that is missing or non-compliant;

accessing, by the computing node, remote sensing data and, optionally, historical farming practice data;

applying, by the computing node, a trained machine learning model to the remote sensing data and, optionally, historical farming practice data, to generate an estimate of the one or more missing or non-compliant farming practice;

updating the one or more farming practice with the estimate of the one or more missing or non-compliant farming practice;

generating, by the computing node, for each of the one or more fields, for each crop production period, an ecosystem attribute by applying one or more ecosystem attribute quantification methods to each spatially and temporally unique set of one or more updated farming practices; and

optimizing, by the computing node, selection of one or more program for each field based on a set of selected programs being compatible within the field and production period,

wherein optimization is based on one or more of: maximizing a beneficial change in an ecosystem attribute, maximizing permanence of an ecosystem attribute, reducing a permanence risk of an ecosystem attribute, maximizing a benefit to plant health, maximizing a benefit to soil health, reducing uncertainty in a quantification of an ecosystem attribute, and maximizing the overall ecosystem impact for the field.

2. The computer-implemented method of claim 1 , further comprising:

accessing a field boundary for each of the one or more fields, wherein the accessed field boundary is a proposed field boundary,

reading one or more boundary validation criteria,

validating the proposed field boundary against the one or more boundary validation criteria, and

generating a revised boundary based on the proposed field boundary and the validation criteria.

3. The computer-implemented method of claim 2 , wherein validating the proposed field boundary against the one or more boundary validation criteria comprises:

defining one or more boundaries of ineligible areas, wherein the ineligible areas at least partially overlap with an area within field boundary, and

generating a revised boundary of the area within field boundary minus the ineligible area.

4. The computer-implemented method of claim 2 , wherein validating the proposed field boundary against the one or more boundary validation criteria comprises:

accessing remote sensing data corresponding to the field area;

using one or more computer vision or machine learning algorithms to detect one or more of: land use type one or more structure, a number of years of agricultural production, and wetlands; and

applying the one or more boundary validation criteria to the remote sensing derived values.

5. The computer-implemented method of claim 1 , wherein the accessed one or more farm practices comprise at least one farm practice that meets an additionally requirement of a methodology, wherein accessing at least one farming practice meeting an additionally requirement automatically triggers a verification program.

6. The computer-implemented method of claim 1 , wherein accessing one or more farming practice comprises applying a validation program comprising one or more of:

confirming each farming practice is within a range of permitted values, wherein the range of permitted values is a confidence interval determined based on: one or more characteristic of the management event type inferred from remote sensing data, historical farming practice data, or combinations thereof.

7. The computer-implemented method of claim 1 , further comprising:

determining one or more farming practice of the field is missing or non-compliant;

accessing historical farm practices of a plurality of fields within the same geographic regions as the field; and

appending the most frequent value for the missing or non-compliant farming practice based on the historical farm practices data for the geographic region to the data record for the field.

8. The computer-implemented method of claim 1 , wherein accessing one or more farming practice comprises:

accessing geospatial boundaries corresponding to one or more region where one or more farming practice is applied or avoided; and

determining one or more management zones based on the accessed farming practice boundaries, wherein determining the one or more management zone comprises dividing the field area into non-overlapping spatial regions co-extensive with the field area, wherein regions having identical farming practices are considered a single management zone.

9. The computer-implemented method of claim 1 , wherein accessing one or more farming practice comprises:

accessing geospatial boundaries corresponding to one or more region where one or more farming practice is applied or avoided;

determining one or more management zones based on the accessed farming practice boundaries, wherein determining the one or more management zone comprises:

sequentially intersecting a geospatial boundary defining a contiguous region wherein management zones are being determined with each farming practice boundary occurring within that contiguous region, wherein each of the sequential intersection operations creates two branches: one with the intersection of the geometries and one with the difference, wherein this process is repeated for all farming practice boundaries that occurred in the geospatial boundary defining the contiguous region, wherein

the final set of leaf nodes in this branching process define the geospatial extent of the set of management zones within the contiguous region, wherein each management zone is non-overlapping and each individual management zone contains a unique set of farming practices relative to any other management zone within the contiguous region.

10. The computer-implemented method of claim 1 , wherein generating the ecosystem attribute further comprises:

quantifying ecosystem attribute uncertainty using a frequentist statistical method, a Bayesian statistical method, or other statistical method.

11. The computer-implemented method of claim 1 , wherein generating the ecosystem attribute further comprises generating a baseline ecosystem attribute comprising:

for each spatially and temporally unique set of one or more farming practices generating a spatially coextensive counterfactual set of farming practices wherein at least one of the farming practices has not been applied or avoided within the geographic region; and

generating a baseline ecosystem attribute by applying the one or more ecosystem attribute quantification methods to the spatially coextensive counterfactual set of farming practices.

12. The computer-implemented method of claim 1 , wherein generating the ecosystem attribute further comprises generating a baseline ecosystem attribute comprising:

for each spatially and temporally unique set of one or more farming practices; and

generating a baseline ecosystem attribute by applying one or more ecosystem attribute quantification methods to a spatially coextensive set of farming practices applied or avoided during a different production period.

13. The computer-implemented method of claim 1 , wherein generating the ecosystem attribute further comprises generating a baseline ecosystem attribute comprising:

accessing remote sensing data comprising a plurality of fields within the geographic region;

applying a machine learning model to the remote sensing data of the plurality of fields to determine field-level farming practice combinations and their co-occurrence probabilities;

applying the one or more ecosystem attribute quantification methods to the ecosystem observation data and field-level farming practice combinations; and

generating a baseline ecosystem attribute by weighing the ecosystem attributes of the field-level farming practice combinations by the co-occurrence probability of that practice combination within the geographic region.

14. The computer-implemented method of claim 1 , further comprising immutably recording in a ledger of one or more of: a field boundary; a management zone boundary; validated and verified farm practice data; a permanence value; an uncertainty; one or more ecosystem attribute; a methodology; a version number of a methodology; an ecosystem attribute quantification method; a version number of a model; a model parameter set; or a model input data, wherein the ledger is a blockchain ledger.

15. The computer-implemented method of claim 1 , wherein generating an ecosystem attribute by applying one or more ecosystem attribute quantification methods, comprises generating an ecosystem attribute for both a set of farming practices that has been applied or avoided, and also for at least one set of farming practices comprising at least one farming practice that has not yet been applied or avoided.

16. The computer-implemented method of claim 15 , wherein an improvement in one or more ecosystem attribute based on inclusion of a farming practice that had not yet been applied or avoided, triggers automatic evaluation of current or prior program enrollment for compatibility with the at least one farming practice that has not yet been applied or avoided and the ecosystem quantification method.

17. The computer-implemented method of claim 15 , wherein an optimized recommendation of one or more farming practice to apply or avoid is based on an improvement in one or more ecosystem attribute based on inclusion of a farming practice that had not yet been applied or avoided in the ecosystem attribute quantification.

18. The computer-implemented method of claim 17 , wherein the optimization is further based on one or more of: minimizing requirements for future management events, minimizing current or future data collection requirements, maximizing long or short term financial incentives for the user, minimizing a cost of implementation, and providing a desired cash flow profile.