MACHINE LEARNING IN AGRICULTURAL PLANTING, GROWING, AND HARVESTING CONTEXTS

DETAILED ACTION Claims 1,3-13,19,20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–44 of U.S. Patent No. 11,263,707: Claims 2,14,15,16,17,18 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-44 of U.S. Patent No. 11,263,707 in view of U.S. Patent No. 11,776,071. Claims 1–20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–20 of U.S. Patent No. 11,776,071: Claims 1–20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–20 of U.S. Patent No. 12,243,112: Response to Amendment The amendment was received 9/2/2025. Claims 1-20 pending. 35 USC § 101 – Positive Statement The claim 1,19,20 “itself reflects the disclosed improvement” (MPEP 2106.04(d)(1), 2nd para) in a technical (computers/digital technology & farming) field via applicant’s disclosure [0002][0040]: TECHNICAL FIELD [0002] This specification relates generally to the application of machine learning1 operations to data from disparate sources to optimize agricultural2 production. [0040] The agronomist client device 108 communicates with the crop prediction system 125 via the network 120 to access crop prediction information generated by the crop prediction system. In one embodiment, a user of the agronomist client device 108 (such as an agricultural specialist, an individual scouting or observing a planted crop, etc.) can review the crop prediction information, including a set of farming operations identified by the crop prediction system 125 to optimize a predicted crop production, and can modify3 the identified set of farming operations. For example, a user of the agronomist client device 108 may access prediction information and a corresponding set of farming operations identified by the crop prediction system 125 that includes the application of a particular type of fertilizer and a harvest date. The user of the agronomist client device 108 can change the type of fertilizer to be applied, for instance based on the type of fertilizer being unavailable to a particular grower, and can change the harvest date, for instance by moving the harvest date up based on expected inclement weather. In other words, a user of the agronomist client device 108 can modify4 farming operations identified by the crop prediction system 125 as optimal based on information available to the user but not available to the crop prediction system 125 at the time the predictions were made. Likewise, an agronomist can modify5 field information on which the crop prediction system 125 is applied (such as a field location, a crop type, an expected rainfall, and the like), and can request that the crop prediction information generated by the crop prediction system be re-generated in order to observe the effect of the modified6 field information on7 the crop prediction information. It should be noted that in some embodiments, the agronomist client device 108 and the broker client device 104 can be the same device, and the broker and agronomist can be the same entity. Double Patenting The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/forms/. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp. Claims 1,3-13,19,20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–44 of U.S. Patent No. 11,263,707: Claims 1–20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–20 of U.S. Patent No. 11,776,071: Claims 1–20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–20 of U.S. Patent No. 12,243,112: Regarding Claims 1–20: The following table illustrates the correspondence between the claimed limitation of 1–20 of the current application and the claimed limitation of 1–44 of US 11,263,707 Patent. 19/027,517 (instant app) 11,263,707 (U.S. Patent) 1. (Original) A method comprising: {identifying a cluster of portions of land}8 associated with a threshold similarity; selecting a portion of land from the cluster of portions of land, the selected portion of land {{associated with crop growth information}}9 describing 1) characteristics of the selected portion of land, 2) a first set of farming operations to be performed , and 3) a first expected carbon sequestration10 corresponding to the first set of farming operations; {{{applying a prediction model trained on crop growth information from a plurality of geographically diverse locations to}}} {{{{the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected carbon sequestration corresponding to the first set of farming operations}}}}, {{{{{the prediction model configured to identify a second set of farming operations that can produce a second expected}}}}} carbon sequestration; and for the selected portion of land, 1) modifying the first set of farming operations to be performed based on the identified second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. 1. A method comprising: accessing, for each of a plurality of portions of land, crop growth information describing {{{{1) characteristics of the portion of land, 2) a first set of farming operations yet to be performed, and 3) a first expected crop productivity corresponding to the first set of farming operations}}}}; identifying a cluster of portions of land associated with a threshold similarity; {{{applying a prediction model trained on crop growth information from a plurality of geographically diverse locations}}} to the accessed [{{crop growth information associated with}}11 the cluster of portions of land], the {{{{{prediction model configured to output an optimized prediction, for a selected portion of land within the {identified cluster of the portions of land}12, comprising: 1) a selected variety or type of crop to plant, 2) a second set of farming operations that can produce a second expected}}}}} crop productivity, and 3) the second expected crop productivity; wherein the prediction is based on inputs to the prediction model comprising: 1) the characteristics of the selected portion of land, 2) the first set of farming operations yet to be performed on the selected portion of land , and 3) the first expected crop productivity corresponding to the first set of farming operations yet to be performed on the selected portion of land; and for the selected portion of land within the identified cluster of the portions of land, 1) modifying the first set of farming operations yet to be performed on the selected portion of land based on the selected variety or type of crop to plant and the second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. 2. (New) The method of claim 1, further comprising: normalizing [the crop growth information associated with the selected portion of land]13 into a format or scale based on historical crop growth information used to train the prediction model. Claim 2 is not taught: 13. The method of claim 1, wherein the accessed crop growth is normalized, and wherein normalizing the crop growth information comprises one or more of: removing format-specific content from the crop growth information, removing or modifying portions of the crop growth information associated with values that fall outside of one or more predefined ranges, and scaling image information such that each image pixel represents a same distance. Claim 13 does not disclose: based on historical crop growth information used to train the prediction model 3. (New) The method of claim 2, wherein normalizing the crop growth information associated with the selected portion of land comprises one or more of: removing format-specific content from the crop growth information, removing or modifying portions of the crop growth information associated with values that fall outside of one or more predefined ranges, and scaling image information such that each image pixel represents a same distance. 13. The method of claim 1, wherein the accessed crop growth is normalized, and wherein normalizing the crop growth information comprises one or more of: removing format-specific content from the crop growth information, removing or modifying portions of the crop growth information associated with values that fall outside of one or more predefined ranges, and scaling image information such that each image pixel represents a same distance. 4. (New) The method of claim 1, wherein the first set of farming operations identifies one or more of: a type or variant of crop to plant, an intercrop to plant, a cover crop to plant, a date to plant a crop, a planting rate, a planting depth, a microbial composition, a date to apply a microbial composition, a rate of application for a microbial composition, an agricultural chemical to apply, a date to apply an agricultural chemical, a rate of application for an agricultural chemical, type of irrigation, a date to apply irrigation, a rate of application for irrigation, whether to replant the crop, whether to replant a different crop within the portion of land, a replant date, a type of nutrient to apply, a quantity of nutrient to apply, a location to apply a nutrient, a date to apply a nutrient, a frequency to apply a nutrient, a nutrient application method, a quantity of water to apply, a type of treatment to apply, a quantity of treatment to apply, a location to apply treatment, a date to apply treatment, a frequency to apply treatment, a treatment application method, a harvest date, a harvest method, a harvest order, a piece of equipment to use or purchase, a drainage method to implement, a crop insurance policy to purchase, a period to store a crop, one or more potential crop brokers, one or more potential crop purchasers, one or more harvested crop purchase prices, and one or more harvested crop qualities, wherein the harvested crop qualities includes at least one of: a crop moisture content, a crop protein content, a crop carbohydrate content, a crop oil content, a crop fat content, a crop color, a crop hardness, a measure of wet gluten, a number or percentage of broken grains, a toxin level, a damage level, whether the crop is organic, whether the crop is shade grown, whether the crop is greenhouse grown, whether the crop is fair-wage grown, whether the crop is no-till grown, when the crop is pollution-free grown, when the crop is carbon neutral, and a grading or certification by an organization or agency. 2. The method of claim 1, wherein a first set of farming operations identifies one or more of: a type or variant of crop to plant, an intercrop to plant, a cover crop to plant, a date to plant a crop, a planting rate, a planting depth, a microbial composition, a date to apply a microbial composition, a rate of application for a microbial composition, an agricultural chemical to apply, a date to apply an agricultural chemical, a rate of application for an agricultural chemical, type of irrigation, a date to apply irrigation, a rate of application for irrigation, whether to replant the crop, whether to replant a different crop within the portion of land, a replant date, a type of nutrient to apply, a quantity of nutrient to apply, a location to apply a nutrient, a date to apply a nutrient, a frequency to apply a nutrient, a nutrient application method, a quantity of water to apply, a type of treatment to apply, a quantity of treatment to apply, a location to apply treatment, a date to apply treatment, a frequency to apply treatment, a treatment application method, a harvest date, a harvest method, a harvest order, a piece of equipment to use or purchase, a drainage method to implement, a crop insurance policy to purchase, a period to store a crop, one or more potential crop brokers, one or more potential crop purchasers, one or more harvested crop purchase prices, and one or more harvested crop qualities, wherein the harvested crop qualities includes at least one of: a crop moisture content, a crop protein content, a crop carbohydrate content, a crop oil content, a crop fat content, a crop color, a crop hardness, a measure of wet gluten, a number or percentage of broken grains, a toxin level, a damage level, whether the crop is organic, whether the crop is shade grown, whether the crop is greenhouse grown, whether the crop is fair-wage grown, whether the crop is no-till grown, when the crop is pollution-free grown, when the crop is carbon neutral, and a grading or certification by an organization or agency. 5. (New) The method of claim 1, wherein the threshold similarity comprises a measure of similarly based on one or more of: geography, climate, soil type, soil composition, soil and atmospheric temperature, number of growing degree days, and precipitation. 3. The method of claim 1, wherein the threshold similarity identifies one or more of: geography, climate, soil type, soil composition, soil and atmospheric temperature, number of growing degree days, and precipitation. 6. (New) The method of claim 1, wherein the crop growth program is periodically modified in response to re-applying the prediction model to updated crop growth information associated with the selected portion of land. 4. The method of claim 1, wherein the crop growth program is periodically modified in response to re-applying the prediction model to periodically accessed crop growth information. 7. (New) The method of claim 1, wherein portions of land in the cluster are fields, plots of land, planting regions, zones, management zones, or sub-portions thereof. 5. The method of claim 1, wherein the portions of land are fields, plots of land, planting regions, zones, management zones, or sub-portions thereof. 8. (New) The method of claim 1, wherein the prediction model is applied to the crop growth information associated with the selected portion of land in response to a triggering event, wherein the triggering event comprises one of: a weather event, a temperature event, a plant growth stage event, a water event, a pest event, a fertilizing event, a farming machinery-related event, a market event, a contract event, and a product supply event. 6. The method of claim 1, wherein the prediction model is applied to the accessed crop growth information in response to a triggering event, wherein the triggering event comprises one of: a weather event, a temperature event, a plant growth stage event, a water event, a pest event, a fertilizing event, a farming machinery-related event, a market event, a contract event, and a product supply event. 9. (New) The method of claim 1, wherein the cluster of portions of land are selected from locations associated with one or more of: a threshold geographic diversity, a threshold environmental diversity, a threshold geographic similarity, and a threshold environmental similarity. 7. The method of claim 1, wherein the plurality of portions of land are selected from locations associated with one or more of: a threshold geographic diversity, a threshold environmental diversity, a threshold geographic similarity, and a threshold environmental similarity. 10. (New) The method of claim 1, wherein the prediction model is applied to the crop growth information associated with the selected portion of land in response to a request from a grower, a technology provider, a service provider, a commodity trader, a broker, an insurance provider, an agronomist, or other entity associated with one or more portions of land in the cluster of portions of land. 8. The method of claim 1, wherein the prediction model is applied to the accessed crop growth information in response to a request from a grower, a technology provider, a service provider, a commodity trader, a broker, an insurance provider, an agronomist, or other entity associated with one or more portions of land in the plurality of portions of land. 11. (New) The method of claim 1, wherein the crop growth information associated with the selected portion of land further describes one or more of: rainfall associated with the selected portion of land, canopy temperature associated with the selected portion of land, soil temperature of the selected portion of land, soil moisture of the selected portion of land, soil nutrients within the selected portion of land, soil type of the selected portion of land, topography within the selected portion of land, humidity associated with the selected portion of land, growing degree days associated with the selected portion of land, microbial community associated with the selected portion of land, pathogen presence associated with the selected portion of land, prior farming operations performed at the selected portion of land, prior crops grown at the selected portion of land, other historical field information associated with the selected portion of land, a crop plant stage, a crop color, a crop stand count, a crop height, a crop root length, a crop root architecture, a crop immune response, a crop flowering, and a crop tasseling. 10. The method of claim 1, wherein the accessed crop growth information further describes one or more of: rainfall associated with the portion of land, canopy temperature associated with the portion of land, soil temperature of the portion of land, soil moisture of the portion of land, soil nutrients within the portion of land, soil type of the portion of land, topography within the portion of land, humidity associated with the portion of land, growing degree days associated with the portion of land, microbial community associated with the portion of land, pathogen presence associated with the portion of land, prior farming operations performed at the portion of land, prior crops grown at the portion of land, other historical field information associated with the portion of land, a crop plant stage, a crop color, a crop stand count, a crop height, a crop root length, a crop root architecture, a crop immune response, a crop flowering, and a crop tasseling. 12. (New) The method of claim 1, wherein the prediction model comprises one or more of: a generalized linear model, a generalized additive model, a non-parametric regression operation, a random forest classifier, a spatial regression operation, a Bayesian regression model, a time series analysis, a Bayesian network, a Gaussian network, a decision tree learning operation, an artificial neural network, a recurrent neural network, a reinforcement learning operation, linear/non-linear regression operations, a support vector machine, a clustering operation, and a genetic algorithm operation. 11. The method of claim 1, wherein the prediction model comprises one or more of: a generalized linear model, a generalized additive model, a non-parametric regression operation, a random forest classifier, a spatial regression operation, a Bayesian regression model, a time series analysis, a Bayesian network, a Gaussian network, a decision tree learning operation, an artificial neural network, a recurrent neural network, a reinforcement learning operation, linear/non-linear regression operations, a support vector machine, a clustering operation, and a genetic algorithm operation. 13. (New) The method of claim 1, wherein the crop growth information associated with the selected portion of land is collected from one or more of: sensors located at the selected portion of land, satellites, aircraft, unmanned aerial vehicles, land-based vehicles, and land-based camera systems. 12. The method of claim 1, wherein the accessed crop growth information is collected from one or more of: sensors located at a portion of land, satellites, aircraft, unmanned aerial vehicles, land-based vehicles, and land-based camera systems. 14. (New) The method of claim 1, wherein modifying the first set of farming operations comprises replacing a first crop to be planted with a carbon neutral plant identified by the second set of farming operations. Claim 14 not taught. 15. (New) The method of claim 1, wherein the prediction model is trained on historical crop growth information accessed from sensors located at the plurality of geographically diverse locations. Claim 15 not taught. 16. (New) The method of claim 1, wherein the prediction model is trained on historical crop growth information captured at different times during one or more growing seasons. Claim 16 not taught. 17. (New) The method of claim 1, wherein the crop growth information associated with the selected portion of land comprises carbon dioxide content information. Claim 17 not taught. 18. (New) The method of claim 1, wherein the prediction model is trained on historical crop growth information comprising carbon dioxide content information. Claim 18 not taught. 19.14 (New) A system comprising: one or more processors; and a non-transitory computer-readable storage medium storing executable instructions that, when executed by the one or more processors, cause the one or more processors to perform steps comprising: identifying a cluster of portions of land associated with a threshold similarity; selecting a portion of land from the cluster of portions of land, the selected portion of land associated with crop growth information describing 1) characteristics of the selected portion of land, 2) a first set of farming operations to be performed, and 3) a first expected carbon sequestration corresponding to the first set of farming operations; applying a prediction model trained on crop growth information from a plurality of geographically diverse locations to the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected carbon sequestration corresponding to the first set of farming operations, the prediction model configured to identify a second set of farming operations that can produce a second expected carbon sequestration; and for the selected portion of land, 1) modifying the first set of farming operations to be performed based on the identified second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. 19. A system comprising: a processor; and a non-transitory computer-readable storage medium storing executable instructions that, when executed by the processor, cause the processor to perform steps comprising: accessing, for each of a plurality of portions of land, crop growth information describing 1) characteristics of the portion of land, 2) a first set of farming operations yet to be performed, and 3) a first expected crop productivity corresponding to the first set of farming operations; identifying a cluster of portions of land associated with a threshold similarity; applying a prediction model trained on crop growth information from a plurality of geographically diverse locations to the accessed crop growth information associated with the cluster of portions of land, the prediction model configured to output an optimized prediction for a selected portion of land within the identified cluster of the portions of land, comprising: 1) a selected variety or type of crop to plant, 2) a second set of farming operations that can produce a second expected crop productivity, and 3) the second expected crop productivity; wherein the prediction is based on inputs to the prediction model comprising: 1) the characteristics of the selected portion of land, 2) the first set of farming operations yet to be performed on the selected portion of land, and 3) the first expected crop productivity corresponding to the first set of farming operations yet to be performed on the selected portion of land; and for the selected portion of land within the identified cluster of the portions of land, 1) modifying the first set of farming operations yet to be performed on the selected portion of land based on the selected variety or type of crop to plant and the second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. 20.15 (New) A non-transitory computer-readable storage medium16 storing executable instructions that, when executed by a processor, cause the processor to perform steps comprising: identifying a cluster of portions of land associated with a threshold similarity; selecting a portion of land from the cluster of portions of land, the selected portion of land associated with crop growth information describing 1) characteristics of the selected portion of land, 2) a first set of farming operations to be performed , and 3) a first expected carbon sequestration corresponding to the first set of farming operations; applying a prediction model trained on crop growth information from a plurality of geographically diverse locations to the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected carbon sequestration corresponding to the first set of farming operations, the prediction model configured to identify a second set of farming operations that can produce a second expected carbon sequestration; and for the selected portion of land, 1) modifying the first set of farming operations to be performed based on the identified second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. 32. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause the processor to perform steps comprising: accessing, for each of a plurality of portions of land, crop growth information describing 1) characteristics of the portion of land, 2) a first set of farming operations yet to be performed, and 3) a first expected crop productivity corresponding to the first set of farming operations; identifying a cluster of portions of land associated with a threshold similarity; applying a prediction model trained on crop growth information from a plurality of geographically diverse locations to the accessed crop growth information associated with the cluster of portions of land, the prediction model configured to output an optimized prediction for a selected portion of land within the identified cluster of the portions of land, comprising: 1) a selected variety or type of crop to plant, 2) a second set of farming operations that can produce a second expected crop productivity, and 3) the second expected crop productivity; wherein the prediction is based on inputs to the prediction model comprising: 1) the characteristics of the selected portion of land, 2) the first set of farming operations yet to be performed on the selected portion of land , and 3) the first expected crop productivity corresponding to the first set of farming operations yet to be performed on the selected portion of land; and for the selected portion of land within the identified cluster of the portions of land, 1) modifying the first set of farming operations yet to be performed on the selected portion of land based on the selected variety or type of crop to plant and the second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. Table 1 The table (Table 1) above shows that independent claims 1, 19 and 20 of this Application is not identical to the claims of U.S. Patent No. 11,263,707. However, the claims are not patentably distinct. The U.S. Patent No. 11,263,707 is narrower than independent claims 1, 19 and 20 since it includes several additional limitations not found in claim 1, 19 and 20 of the instant Application. Regarding Claims 1–20: The following table illustrates the correspondence between the claimed limitation of 1–20 of the current application and the claimed limitation of 1–20 of US 11,776,071 Patent. 19/027,517 (instant app) 11,776,071 (U.S. Patent) 1. (Original) A method comprising: identifying a cluster of portions of land associated with a threshold similarity; selecting a portion of land from the cluster of portions of land, the selected portion of land associated with crop growth information describing 1) characteristics of the selected portion of land, 2) a first set of farming operations to be performed, and 3) a first expected carbon sequestration corresponding to the first set of farming operations; applying a prediction model trained on crop growth information from a plurality of geographically diverse locations to the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected carbon sequestration corresponding to the first set of farming operations, the prediction model configured to identify a second set of farming operations that can produce a second expected carbon sequestration; and for the selected portion of land, 1) modifying the first set of farming operations to be performed based on the identified second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. 1. A method comprising: identifying a cluster of portions of land associated with a threshold similarity; selecting a portion of land from the cluster of portions of land, the selected portion of land associated with crop growth information describing 1) characteristics of the selected portion of land, 2) a first set of farming operations to be performed, and 3) a first expected carbon sequestration corresponding to the first set of farming operations; applying a prediction model trained on historical crop growth information from a plurality of geographically diverse locations to inputs comprising the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected carbon sequestration corresponding to the first set of farming operations, wherein the prediction model optimizes a second expected carbon sequestration by producing an output comprising a second set of farming operations that can produce the optimized second expected carbon sequestration; and for the selected portion of land, 1) modifying the first set of farming operations to be performed based on the identified second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. 2. (New) The method of claim 1, further comprising: normalizing the crop growth information associated with the selected portion of land into a format or scale based on historical crop growth information used to train the prediction model. 2. The method of claim 1, further comprising normalizing the crop growth information associated with the selected portion of land into a format or scale based on the historical crop growth information used to train the prediction model. 3. (New) The method of claim 2, wherein normalizing the crop growth information associated with the selected portion of land comprises one or more of: removing format-specific content from the crop growth information, removing or modifying portions of the crop growth information associated with values that fall outside of one or more predefined ranges, and scaling image information such that each image pixel represents a same distance. 3. The method of claim 2, wherein normalizing the crop growth information associated with the selected portion of land comprises one or more of: removing format-specific content from the crop growth information, removing or modifying portions of the crop growth information associated with values that fall outside of one or more predefined ranges, and scaling image information such that each image pixel represents a same distance. 4. (New) The method of claim 1, wherein the first set of farming operations identifies one or more of: a type or variant of crop to plant, an intercrop to plant, a cover crop to plant, a date to plant a crop, a planting rate, a planting depth, a microbial composition, a date to apply a microbial composition, a rate of application for a microbial composition, an agricultural chemical to apply, a date to apply an agricultural chemical, a rate of application for an agricultural chemical, type of irrigation, a date to apply irrigation, a rate of application for irrigation, whether to replant the crop, whether to replant a different crop within the portion of land, a replant date, a type of nutrient to apply, a quantity of nutrient to apply, a location to apply a nutrient, a date to apply a nutrient, a frequency to apply a nutrient, a nutrient application method, a quantity of water to apply, a type of treatment to apply, a quantity of treatment to apply, a location to apply treatment, a date to apply treatment, a frequency to apply treatment, a treatment application method, a harvest date, a harvest method, a harvest order, a piece of equipment to use or purchase, a drainage method to implement, a crop insurance policy to purchase, a period to store a crop, one or more potential crop brokers, one or more potential crop purchasers, one or more harvested crop purchase prices, and one or more harvested crop qualities, wherein the harvested crop qualities includes at least one of: a crop moisture content, a crop protein content, a crop carbohydrate content, a crop oil content, a crop fat content, a crop color, a crop hardness, a measure of wet gluten, a number or percentage of broken grains, a toxin level, a damage level, whether the crop is organic, whether the crop is shade grown, whether the crop is greenhouse grown, whether the crop is fair-wage grown, whether the crop is no-till grown, when the crop is pollution-free grown, when the crop is carbon neutral, and a grading or certification by an organization or agency. 4. The method of claim 1, wherein the first set of farming operations identifies one or more of: a type or variant of crop to plant, an intercrop to plant, a cover crop to plant, a date to plant a crop, a planting rate, a planting depth, a microbial composition, a date to apply a microbial composition, a rate of application for a microbial composition, an agricultural chemical to apply, a date to apply an agricultural chemical, a rate of application for an agricultural chemical, type of irrigation, a date to apply irrigation, a rate of application for irrigation, whether to replant the crop, whether to replant a different crop within the portion of land, a replant date, a type of nutrient to apply, a quantity of nutrient to apply, a location to apply a nutrient, a date to apply a nutrient, a frequency to apply a nutrient, a nutrient application method, a quantity of water to apply, a type of treatment to apply, a quantity of treatment to apply, a location to apply treatment, a date to apply treatment, a frequency to apply treatment, a treatment application method, a harvest date, a harvest method, a harvest order, a piece of equipment to use or purchase, a drainage method to implement, a crop insurance policy to purchase, a period to store a crop, one or more potential crop brokers, one or more potential crop purchasers, one or more harvested crop purchase prices, and one or more harvested crop qualities, wherein the harvested crop qualities includes at least one of: a crop moisture content, a crop protein content, a crop carbohydrate content, a crop oil content, a crop fat content, a crop color, a crop hardness, a measure of wet gluten, a number or percentage of broken grains, a toxin level, a damage level, whether the crop is organic, whether the crop is shade grown, whether the crop is greenhouse grown, whether the crop is fair-wage grown, whether the crop is no-till grown, when the crop is pollution-free grown, when the crop is carbon neutral, and a grading or certification by an organization or agency. 5. (New) The method of claim 1, wherein the threshold similarity comprises a measure of similarly based on one or more of: geography, climate, soil type, soil composition, soil and atmospheric temperature, number of growing degree days, and precipitation. 5. The method of claim 1, wherein the threshold similarity comprises a measure of similarly based on one or more of: geography, climate, soil type, soil composition, soil and atmospheric temperature, number of growing degree days, and precipitation. 6. (New) The method of claim 1, wherein the crop growth program is periodically modified in response to re-applying the prediction model to updated crop growth information associated with the selected portion of land. 6. The method of claim 1, wherein the crop growth program is periodically modified in response to re-applying the prediction model to updated crop growth information associated with the selected portion of land. 7. (New) The method of claim 1, wherein portions of land in the cluster are fields, plots of land, planting regions, zones, management zones, or sub-portions thereof. 7. The method of claim 1, wherein the portions of land are fields, plots of land, planting regions, zones, management zones, or sub-portions thereof. 8. (New) The method of claim 1, wherein the prediction model is applied to the crop growth information associated with the selected portion of land in response to a triggering event, wherein the triggering event comprises one of: a weather event, a temperature event, a plant growth stage event, a water event, a pest event, a fertilizing event, a farming machinery-related event, a market event, a contract event, and a product supply event. 8. The method of claim 1, wherein the prediction model is applied to the crop growth information associated with the selected portion of land in response to a triggering event, wherein the triggering event comprises one of: a weather event, a temperature event, a plant growth stage event, a water event, a pest event, a fertilizing event, a farming machinery-related event, a market event, a contract event, and a product supply event. 9. (New) The method of claim 1, wherein the cluster of portions of land are selected from locations associated with one or more of: a threshold geographic diversity, a threshold environmental diversity, a threshold geographic similarity, and a threshold environmental similarity. 9. The method of claim 1, wherein the cluster of portions of land are selected from locations associated with one or more of: a threshold geographic diversity, a threshold environmental diversity, a threshold geographic similarity, and a threshold environmental similarity. 10. (New) The method of claim 1, wherein the prediction model is applied to the crop growth information associated with the selected portion of land in response to a request from a grower, a technology provider, a service provider, a commodity trader, a broker, an insurance provider, an agronomist, or other entity associated with one or more portions of land in the cluster of portions of land. 10. The method of claim 1, wherein the prediction model is applied to the crop growth information associated with the selected portion of land in response to a request from a grower, a technology provider, a service provider, a commodity trader, a broker, an insurance provider, an agronomist, or other entity associated with one or more portions of land in the cluster of portions of land. 11. (New) The method of claim 1, wherein the crop growth information associated with the selected portion of land further describes one or more of: rainfall associated with the selected portion of land, canopy temperature associated with the selected portion of land, soil temperature of the selected portion of land, soil moisture of the selected portion of land, soil nutrients within the selected portion of land, soil type of the selected portion of land, topography within the selected portion of land, humidity associated with the selected portion of land, growing degree days associated with the selected portion of land, microbial community associated with the selected portion of land, pathogen presence associated with the selected portion of land, prior farming operations performed at the selected portion of land, prior crops grown at the selected portion of land, other historical field information associated with the selected portion of land, a crop plant stage, a crop color, a crop stand count, a crop height, a crop root length, a crop root architecture, a crop immune response, a crop flowering, and a crop tasseling. 11. The method of claim 1, wherein the crop growth information associated with the selected portion of land further describes one or more of: rainfall associated with the selected portion of land, canopy temperature associated with the selected portion of land, soil temperature of the selected portion of land, soil moisture of the selected portion of land, soil nutrients within the selected portion of land, soil type of the selected portion of land, topography within the selected portion of land, humidity associated with the selected portion of land, growing degree days associated with the selected portion of land, microbial community associated with the selected portion of land, pathogen presence associated with the selected portion of land, prior farming operations performed at the selected portion of land, prior crops grown at the selected portion of land, other historical field information associated with the selected portion of land, a crop plant stage, a crop color, a crop stand count, a crop height, a crop root length, a crop root architecture, a crop immune response, a crop flowering, and a crop tasseling. 12. (New) The method of claim 1, wherein the prediction model comprises one or more of: a generalized linear model, a generalized additive model, a non-parametric regression operation, a random forest classifier, a spatial regression operation, a Bayesian regression model, a time series analysis, a Bayesian network, a Gaussian network, a decision tree learning operation, an artificial neural network, a recurrent neural network, a reinforcement learning operation, linear/non-linear regression operations, a support vector machine, a clustering operation, and a genetic algorithm operation. 12. The method of claim 1, wherein the prediction model comprises one or more of: a generalized linear model, a generalized additive model, a non-parametric regression operation, a random forest classifier, a spatial regression operation, a Bayesian regression model, a time series analysis, a Bayesian network, a Gaussian network, a decision tree learning operation, an artificial neural network, a recurrent neural network, a reinforcement learning operation, linear/non-linear regression operations, a support vector machine, a clustering operation, and a genetic algorithm operation. 13. (New) The method of claim 1, wherein the crop growth information associated with the selected portion of land is collected from one or more of: sensors located at the selected portion of land, satellites, aircraft, unmanned aerial vehicles, land-based vehicles, and land-based camera systems. 13. The method of claim 1, wherein the crop growth information associated with the selected portion of land is collected from one or more of: sensors located at the selected portion of land, satellites, aircraft, unmanned aerial vehicles, land-based vehicles, and land-based camera systems. 14. (New) The method of claim 1, wherein modifying the first set of farming operations comprises replacing a first crop to be planted with a carbon neutral plant identified by the second set of farming operations. 14. The method of claim 1, wherein modifying the first set of farming operations comprises replacing a first crop to be planted with a carbon neutral plant identified by the second set of farming operations. 15. (New) The method of claim 1, wherein the prediction model is trained on historical crop growth information accessed from sensors located at the plurality of geographically diverse locations. 15. The method of claim 1, wherein the prediction model is trained on historical crop growth information accessed from sensors located at the plurality of geographically diverse locations. 16. (New) The method of claim 1, wherein the prediction model is trained on historical crop growth information captured at different times during one or more growing seasons. 16. The method of claim 1, wherein the prediction model is trained on historical crop growth information captured at different times during one or more growing seasons. 17. (New) The method of claim 1, wherein the crop growth information associated with the selected portion of land comprises carbon dioxide content information. 17. The method of claim 1, wherein the crop growth information associated with the selected portion of land comprises carbon dioxide content information. 18. (New) The method of claim 1, wherein the prediction model is trained on historical crop growth information comprising carbon dioxide content information. 18. The method of claim 1, wherein the prediction model is trained on historical crop growth information comprising carbon dioxide content information. 19. (New) A system comprising: one or more processors; and a non-transitory computer-readable storage medium storing executable instructions that, when executed by the one or more processors, cause the one or more processors to perform steps comprising: identifying a cluster of portions of land associated with a threshold similarity; selecting a portion of land from the cluster of portions of land, the selected portion of land associated with crop growth information describing 1) characteristics of the selected portion of land, 2) a first set of farming operations to be performed, and 3) a first expected carbon sequestration corresponding to the first set of farming operations; applying a prediction model trained on crop growth information from a plurality of geographically diverse locations to the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected carbon sequestration corresponding to the first set of farming operations, the prediction model configured to identify a second set of farming operations that can produce a second expected carbon sequestration; and for the selected portion of land, 1) modifying the first set of farming operations to be performed based on the identified second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. 19. A system comprising: a processor; and a non-transitory computer-readable storage medium storing executable instructions that, when executed by the processor, cause the processor to perform steps comprising: identifying a cluster of portions of land associated with a threshold similarity; selecting a portion of land from the cluster of portions of land, the selected portion of land associated with crop growth information describing 1) characteristics of the selected portion of land, 2) a first set of farming operations to be performed, and 3) a first expected carbon sequestration corresponding to the first set of farming operations; applying a prediction model trained on historical crop growth information from a plurality of geographically diverse locations to inputs comprising the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected carbon sequestration corresponding to the first set of farming operations, wherein the prediction model optimizes a second expected carbon sequestration by producing an output comprising a second set of farming operations that can produce the optimized second expected carbon sequestration; and for the selected portion of land, 1) modifying the first set of farming operations to be performed based on the identified second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. 20. (New) A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause the processor to perform steps comprising: identifying a cluster of portions of land associated with a threshold similarity; selecting a portion of land from the cluster of portions of land, the selected portion of land associated with crop growth information describing 1) characteristics of the selected portion of land, 2) a first set of farming operations to be performed, and 3) a first expected carbon sequestration corresponding to the first set of farming operations; applying a prediction model trained on crop growth information from a plurality of geographically diverse locations to the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected carbon sequestration corresponding to the first set of farming operations, the prediction model configured to identify a second set of farming operations that can produce a second expected carbon sequestration; and for the selected portion of land, 1) modifying the first set of farming operations to be performed based on the identified second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. 20. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause the processor to perform steps comprising: identifying a cluster of portions of land associated with a threshold similarity; selecting a portion of land from the cluster of portions of land, the selected portion of land associated with crop growth information describing 1) characteristics of the selected portion of land, 2) a first set of farming operations to be performed, and 3) a first expected carbon sequestration corresponding to the first set of farming operations; applying a prediction model trained on historical crop growth information from a plurality of geographically diverse locations to inputs comprising the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected carbon sequestration corresponding to the first set of farming operations, wherein the prediction model optimizes a second expected carbon sequestration by producing an output comprising a second set of farming operations that can produce the optimized second expected carbon sequestration; and for the selected portion of land, 1) modifying the first set of farming operations to be performed based on the identified second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. Table 1 The table (Table 1) above shows that independent claims 1, 19 and 20 of this Application is not identical to the claims of U.S. Patent No. 11,767,071. However, the claims are not patentably distinct. The U.S. Patent No. 11,767,071 is narrower than independent claims 1,19 and 20 since it includes several additional limitations not found in claim 1,19 and 20 of the instant Application. Regarding Claims 1–20: The following table illustrates the correspondence between the claimed limitation of 1–20 of the current application and the claimed limitation of 1–20 of US 12,243,112 Patent. 19/027,517 (instant app) 12,243,112 (U.S. Patent) 1. (Original) A method comprising: identifying a cluster of portions of land associated with a threshold similarity; selecting a portion of land from the cluster of portions of land, the selected portion of land associated with crop growth information describing 1) characteristics of the selected portion of land, 2) a first set of farming operations to be performed, and 3) a first expected carbon sequestration corresponding to the first set of farming operations; applying a prediction model trained on crop growth information from a plurality of geographically diverse locations to the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected carbon sequestration corresponding to the first set of farming operations, the prediction model configured to identify a second set of farming operations that can produce a second expected carbon sequestration; and for the selected portion of land, 1) modifying the first set of farming operations to be performed based on the identified second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. 1. A method comprising: selecting a portion of land from a cluster of identified portions of land, the selected portion of land associated with crop growth information describing 1) characteristics of the selected portion of land, 2) a first set of farming operations to be performed, and 3) a first expected crop productivity corresponding to the first set of farming operations; applying a prediction model trained on historical crop growth information from a plurality of geographically diverse locations to inputs comprising the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected crop productivity corresponding to the first set of farming operations, wherein the prediction model optimizes a second expected crop productivity by producing an output comprising a second set of farming operations that can produce the optimized second expected crop productivity, wherein the second set of farming operations comprises an agricultural chemical to apply; and for the selected portion of land, 1) modifying the first set of farming operations to be performed based on the identified second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. 2. (New) The method of claim 1, further comprising: normalizing the crop growth information associated with the selected portion of land into a format or scale based on historical crop growth information used to train the prediction model. 2. The method of claim 1, further comprising normalizing the crop growth information associated with the selected portion of land into a format or scale based on the crop growth information used to train the prediction model. 3. (New) The method of claim 2, wherein normalizing the crop growth information associated with the selected portion of land comprises one or more of: removing format-specific content from the crop growth information, removing or modifying portions of the crop growth information associated with values that fall outside of one or more predefined ranges, and scaling image information such that each image pixel represents a same distance. 3. The method of claim 2, wherein normalizing the crop growth information associated with the selected portion of land comprises one or more of: removing format-specific content from the crop growth information, removing or modifying portions of the crop growth information associated with values that fall outside of one or more predefined ranges, and scaling image information such that each image pixel represents a same distance. 4. (New) The method of claim 1, wherein the first set of farming operations identifies one or more of: a type or variant of crop to plant, an intercrop to plant, a cover crop to plant, a date to plant a crop, a planting rate, a planting depth, a microbial composition, a date to apply a microbial composition, a rate of application for a microbial composition, an agricultural chemical to apply, a date to apply an agricultural chemical, a rate of application for an agricultural chemical, type of irrigation, a date to apply irrigation, a rate of application for irrigation, whether to replant the crop, whether to replant a different crop within the portion of land, a replant date, a type of nutrient to apply, a quantity of nutrient to apply, a location to apply a nutrient, a date to apply a nutrient, a frequency to apply a nutrient, a nutrient application method, a quantity of water to apply, a type of treatment to apply, a quantity of treatment to apply, a location to apply treatment, a date to apply treatment, a frequency to apply treatment, a treatment application method, a harvest date, a harvest method, a harvest order, a piece of equipment to use or purchase, a drainage method to implement, a crop insurance policy to purchase, a period to store a crop, one or more potential crop brokers, one or more potential crop purchasers, one or more harvested crop purchase prices, and one or more harvested crop qualities, wherein the harvested crop qualities includes at least one of: a crop moisture content, a crop protein content, a crop carbohydrate content, a crop oil content, a crop fat content, a crop color, a crop hardness, a measure of wet gluten, a number or percentage of broken grains, a toxin level, a damage level, whether the crop is organic, whether the crop is shade grown, whether the crop is greenhouse grown, whether the crop is fair-wage grown, whether the crop is no-till grown, when the crop is pollution-free grown, when the crop is carbon neutral, and a grading or certification by an organization or agency. 4. The method of claim 1, wherein a first set of farming operations identifies one or more of: a type or variant of crop to plant, an intercrop to plant, a cover crop to plant, a date to plant a crop, a planting rate, a planting depth, a microbial composition, a date to apply a microbial composition, a rate of application for a microbial composition, an agricultural chemical to apply, a date to apply an agricultural chemical, a rate of application for an agricultural chemical, type of irrigation, a date to apply irrigation, a rate of application for irrigation, whether to replant the crop, whether to replant a different crop within the portion of land, a replant date, a type of nutrient to apply, a quantity of nutrient to apply, a location to apply a nutrient, a date to apply a nutrient, a frequency to apply a nutrient, a nutrient application method, a quantity of water to apply, a type of treatment to apply, a quantity of treatment to apply, a location to apply treatment, a date to apply treatment, a frequency to apply treatment, a treatment application method, a harvest date, a harvest method, a harvest order, a piece of equipment to use or purchase, a drainage method to implement, a crop insurance policy to purchase, a period to store a crop, one or more potential crop brokers, one or more potential crop purchasers, one or more harvested crop purchase prices, and one or more harvested crop qualities, wherein the harvested crop qualities includes at least one of: a crop moisture content, a crop protein content, a crop carbohydrate content, a crop oil content, a crop fat content, a crop color, a crop hardness, a measure of wet gluten, a number or percentage of broken grains, a toxin level, a damage level, whether the crop is organic, whether the crop is shade grown, whether the crop is greenhouse grown, whether the crop is fair-wage grown, whether the crop is no-till grown, when the crop is pollution-free grown, when the crop is carbon neutral, and a grading or certification by an organization or agency. 5. (New) The method of claim 1, wherein the threshold similarity comprises a measure of similarly based on one or more of: geography, climate, soil type, soil composition, soil and atmospheric temperature, number of growing degree days, and precipitation. 5. The method of claim 1, wherein portions of land in the cluster of identified portions of land are identified based on having a threshold similarity to one another, and wherein the threshold similarity comprises a measure of similarly based on one or more of: geography, climate, soil type, soil composition, soil and atmospheric temperature, number of growing degree days, and precipitation. 6. (New) The method of claim 1, wherein the crop growth program is periodically modified in response to re-applying the prediction model to updated crop growth information associated with the selected portion of land. 6. The method of claim 1, wherein the crop growth program is periodically modified in response to re-applying the prediction model to updated crop growth information associated with the selected portion of land. 7. (New) The method of claim 1, wherein portions of land in the cluster are fields, plots of land, planting regions, zones, management zones, or sub-portions thereof. 7. The method of claim 1, wherein the portions of land are fields, plots of land, planting regions, zones, management zones, or sub-portions thereof. 8. (New) The method of claim 1, wherein the prediction model is applied to the crop growth information associated with the selected portion of land in response to a triggering event, wherein the triggering event comprises one of: a weather event, a temperature event, a plant growth stage event, a water event, a pest event, a fertilizing event, a farming machinery-related event, a market event, a contract event, and a product supply event. 8. The method of claim 1, wherein the prediction model is applied to the crop growth information associated with the selected portion of land in response to a triggering event, wherein the triggering event comprises one of: a weather event, a temperature event, a plant growth stage event, a water event, a pest event, a fertilizing event, a farming machinery-related event, a market event, a contract event, and a product supply event. 9. (New) The method of claim 1, wherein the cluster of portions of land are selected from locations associated with one or more of: a threshold geographic diversity, a threshold environmental diversity, a threshold geographic similarity, and a threshold environmental similarity. 9. The method of claim 1, wherein the cluster of portions of land are selected from locations associated with one or more of: a threshold geographic diversity, a threshold environmental diversity, a threshold geographic similarity, and a threshold environmental similarity. 10. (New) The method of claim 1, wherein the prediction model is applied to the crop growth information associated with the selected portion of land in response to a request from a grower, a technology provider, a service provider, a commodity trader, a broker, an insurance provider, an agronomist, or other entity associated with one or more portions of land in the cluster of portions of land. 10. The method of claim 1, wherein the prediction model is applied to the crop growth information associated with the selected portion of land in response to a request from a grower, a technology provider, a service provider, a commodity trader, a broker, an insurance provider, an agronomist, or other entity associated with one or more portions of land in the cluster of portions of land. 11. (New) The method of claim 1, wherein the crop growth information associated with the selected portion of land further describes one or more of: rainfall associated with the selected portion of land, canopy temperature associated with the selected portion of land, soil temperature of the selected portion of land, soil moisture of the selected portion of land, soil nutrients within the selected portion of land, soil type of the selected portion of land, topography within the selected portion of land, humidity associated with the selected portion of land, growing degree days associated with the selected portion of land, microbial community associated with the selected portion of land, pathogen presence associated with the selected portion of land, prior farming operations performed at the selected portion of land, prior crops grown at the selected portion of land, other historical field information associated with the selected portion of land, a crop plant stage, a crop color, a crop stand count, a crop height, a crop root length, a crop root architecture, a crop immune response, a crop flowering, and a crop tasseling. 11. The method of claim 1, wherein the crop growth information associated with the selected portion of land further describes one or more of: rainfall associated with the selected portion of land, canopy temperature associated with the selected portion of land, soil temperature of the selected portion of land, soil moisture of the selected portion of land, soil nutrients within the selected portion of land, soil type of the selected portion of land, topography within the selected portion of land, humidity associated with the selected portion of land, growing degree days associated with the selected portion of land, microbial community associated with the selected portion of land, pathogen presence associated with the selected portion of land, prior farming operations performed at the selected portion of land, prior crops grown at the selected portion of land, other historical field information associated with the selected portion of land, a crop plant stage, a crop color, a crop stand count, a crop height, a crop root length, a crop root architecture, a crop immune response, a crop flowering, and a crop tasseling. 12. (New) The method of claim 1, wherein the prediction model comprises one or more of: a generalized linear model, a generalized additive model, a non-parametric regression operation, a random forest classifier, a spatial regression operation, a Bayesian regression model, a time series analysis, a Bayesian network, a Gaussian network, a decision tree learning operation, an artificial neural network, a recurrent neural network, a reinforcement learning operation, linear/non-linear regression operations, a support vector machine, a clustering operation, and a genetic algorithm operation. 12. The method of claim 1, wherein the prediction model comprises one or more of: a generalized linear model, a generalized additive model, a non-parametric regression operation, a random forest classifier, a spatial regression operation, a Bayesian regression model, a time series analysis, a Bayesian network, a Gaussian network, a decision tree learning operation, an artificial neural network, a recurrent neural network, a reinforcement learning operation, linear/non-linear regression operations, a support vector machine, a clustering operation, and a genetic algorithm operation. 13. (New) The method of claim 1, wherein the crop growth information associated with the selected portion of land is collected from one or more of: sensors located at the selected portion of land, satellites, aircraft, unmanned aerial vehicles, land-based vehicles, and land-based camera systems. 13. The method of claim 1, wherein the crop growth information associated with the selected portion of land is collected from one or more of: sensors located at the selected portion of land, satellites, aircraft, unmanned aerial vehicles, land-based vehicles, and land-based camera systems. 14. (New) The method of claim 1, wherein modifying the first set of farming operations comprises replacing a first crop to be planted with a carbon neutral plant identified by the second set of farming operations. 14. The method of claim 1, wherein modifying the first set of farming operations comprises replacing a first crop to be planted with a carbon neutral plant identified by the second set of farming operations. 15. (New) The method of claim 1, wherein the prediction model is trained on historical crop growth information accessed from sensors located at the plurality of geographically diverse locations. 15. The method of claim 1, wherein the prediction model is trained on crop growth information accessed from sensors located at the plurality of geographically diverse locations. 16. (New) The method of claim 1, wherein the prediction model is trained on historical crop growth information captured at different times during one or more growing seasons. 16. The method of claim 1, wherein the prediction model is trained on crop growth information captured at different times during one or more growing seasons. 17. (New) The method of claim 1, wherein the crop growth information associated with the selected portion of land comprises carbon dioxide content information. 17. The method of claim 1, wherein the crop growth information associated with the selected portion of land comprises carbon dioxide content information. 18. (New) The method of claim 1, wherein the prediction model is trained on historical crop growth information comprising carbon dioxide content information. 18. The method of claim 1, wherein the prediction model is trained on crop growth information comprising carbon dioxide content information. 19. (New) A system comprising: one or more processors; and a non-transitory computer-readable storage medium storing executable instructions that, when executed by the one or more processors, cause the one or more processors to perform steps comprising: identifying a cluster of portions of land associated with a threshold similarity; selecting a portion of land from the cluster of portions of land, the selected portion of land associated with crop growth information describing 1) characteristics of the selected portion of land, 2) a first set of farming operations to be performed, and 3) a first expected carbon sequestration corresponding to the first set of farming operations; applying a prediction model trained on crop growth information from a plurality of geographically diverse locations to the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected carbon sequestration corresponding to the first set of farming operations, the prediction model configured to identify a second set of farming operations that can produce a second expected carbon sequestration; and for the selected portion of land, 1) modifying the first set of farming operations to be performed based on the identified second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. 19. A system comprising: a processor; and a non-transitory computer-readable storage medium storing executable instructions that, when executed by the processor, cause the processor to perform steps comprising: selecting a portion of land from a cluster of identified portions of land, the selected portion of land associated with crop growth information describing 1) characteristics of the selected portion of land, 2) a first set of farming operations to be performed, and 3) a first expected crop productivity corresponding to the first set of farming operations; applying a prediction model trained on historical crop growth information from a plurality of geographically diverse locations to inputs comprising the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected crop productivity corresponding to the first set of farming operations, wherein the prediction model optimizes a second expected crop productivity by producing an output comprising a second set of farming operations that can produce the optimized second expected crop productivity, wherein the second set of farming operations comprises an agricultural chemical to apply for the selected portion of land, 1) modifying the first set of farming operations to be performed based on the identified second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. 20. (New) A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause the processor to perform steps comprising: identifying a cluster of portions of land associated with a threshold similarity; selecting a portion of land from the cluster of portions of land, the selected portion of land associated with crop growth information describing 1) characteristics of the selected portion of land, 2) a first set of farming operations to be performed, and 3) a first expected carbon sequestration corresponding to the first set of farming operations; applying a prediction model trained on crop growth information from a plurality of geographically diverse locations to the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected carbon sequestration corresponding to the first set of farming operations, the prediction model configured to identify a second set of farming operations that can produce a second expected carbon sequestration ; and for the selected portion of land, 1) modifying the first set of farming operations to be performed based on the identified second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. 20. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause the processor to perform steps comprising: selecting a portion of land from a cluster of identified portions of land, the selected portion of land associated with crop growth information describing 1) characteristics of the selected portion of land, 2) a first set of farming operations to be performed, and 3) a first expected crop productivity corresponding to the first set of farming operations; applying a prediction model trained on historical crop growth information from a plurality of geographically diverse locations to inputs comprising the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected crop productivity corresponding to the first set of farming operations, wherein the prediction model optimizes a second expected crop productivity by producing an output comprising a second set of farming operations that can produce the optimized second expected crop productivity , wherein the second set of farming operations comprises an agricultural chemical to apply ; and for the selected portion of land, 1) modifying the first set of farming operations to be performed based on the identified second set of farming operations, and 2) modifying a user interface displayed by a client device of the user to display a crop growth program based on the modified first set of farming operations. Table 1 The table (Table 1) above shows that independent claims 1, 19 and 20 of this Application is not identical to the claims of U.S. Patent No. 12,243,112. However, the claims are not patentably distinct. The U.S. Patent No. 12,243,112 is narrower than independent claims 1, 19 and 20 since it includes several additional limitations not found in claim 1, 19 and 20 of the instant Application. Claims 2,14,15,16,17,18 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-44 of U.S. Patent No. 11,263,707 in view of U.S. Patent No. 11,776,071. U.S. Patent No. 11,263,707 does not teach claims 2,14,15,16,17,18. U.S. Patent No. 11,776,071 already teaches claims 2,14,15,16,17,18 in the above double patenting rejection: Claims 1–20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–20 of U.S. Patent No. 11,776,071. Thus, it would have been obvious to respectively combine U.S. Patent No. 11,263,707’s claims 2,14,15,16,17,18 with U.S. Patent No. 11,776,071’s claims 2,14,15,16,17,18 for: “applying a prediction model trained on historical crop growth information from a plurality of geographically diverse locations to inputs comprising the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected carbon sequestration corresponding to the first set of farming operations, wherein the prediction model optimizes a second expected carbon sequestration by producing an output comprising a second set of farming operations that can produce the optimized second expected carbon sequestration” (US 11,776,071, claim 1, 3rd limitation). Allowable Subject Matter Claims 1-20 can be/may be/possibly be allowable pending the above double patenting rejections since there is no 35 USC 102 or 35 USC 103 rejection in this Office action. The following is a statement of reasons for the indication of allowable subject matter: MPEP 1302.14 Reasons for Allowance [R-01.2024] I. REASONS FOR ALLOWANCE, 7th para: Where specific reasons are recorded by the examiner, care must be taken to ensure that statements of reasons for allowance (or indication of allowable subject matter) are accurate, precise, and do not place unwarranted interpretations, whether broad or narrow, upon the claims. The examiner should keep in mind the possible misinterpretations of their statement that may be made and its possible effects. Each statement should include at least (1) the major difference in the claims not found in the prior art of record, and (2) the reasons why that difference is considered to define patentably over the prior art if either of these reasons for allowance is not clear in the record. The statement is not intended to necessarily state all the reasons for allowance or all the details why claims are allowed and should not be written to specifically or impliedly state that all the reasons for allowance are set forth. Where the examiner has a large number of reasons for allowing a claim, it may suffice to state only the major or important reasons, being careful to so couch the statement. For example, a statement might start: "The primary reason for the allowance of the claims is the inclusion of the limitation in all the claims which is not found in the prior art references," with further amplification as necessary. Regarding statement (1), the major difference not found in the art is claim 1’s: applying a prediction model trained on crop growth information from a plurality of geographically diverse locations to the characteristics of the selected portion of land, the first set of farming operations to be performed, and the first expected carbon sequestration corresponding to the first set of farming operations, the prediction model configured to identify a second set of farming operations that can produce a second expected carbon sequestration For example, A) and B) and C): A) MARINO (CA 2813442 A1/WO 2012/044953 A1) teaches a carbon sequestration model includes carbon pools (live/dead trees, forest floor litter): Additionally, while nearly all carbon sequestration forest models appear to include above ground biomass and a variety of carbon pools (e.g., live/dead trees, forest floor litter) few contain realistic dynamic below ground soil carbon pools including labile and mineralized forms of carbon, fine and coarse root structures and decomposing organic carbon in the upper most layers of the soil profile. Thus MARINO does not teach “a second expected carbon sequestration”. B) Smith (US 2014/0164070 A1) teaches sequestering carbon via “carbon storage” “over a time window” of [0023] last Ss: The carbon credits calculator 102 may compare the prediction from the probabilistic carbon model with corresponding amounts from the satellite carbon data and find a difference indicating how much carbon fixation and/or carbon storage may be achieved through change in land use. Alternatively the carbon credits calculator may directly estimate the amount of carbon sequestered over a time window given comparison data comprising estimates of the carbon contents for the geographical area. Thus Smith does not teach “a second expected carbon sequestration”. C) Richt (US 2018/0070527 A1) teaches a prediction model outputting CO2 sequestration [0110] last S: “For example, for every soil layer and for every time period agronomic outputs can also include… CO.sub.2 sequestration”. Thus Richt does not teach “a second expected carbon sequestration”. Regarding statement (2), the difference patentably defines over the prior art by improving farming along with improved crop prediction system 125 as discussed in the above 35 USC § 101 – Positive Statement section: PNG media_image1.png 748 952 media_image1.png Greyscale Conclusion The prior art “nearest to the subject matter defined in the claims” (MPEP 707.05) made of record and not relied upon is considered pertinent to applicant's disclosure. The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action: Citation Relevance MARINO ( CA 2813442 A1/ WO 2012/044953 A1) as discussed in the Allowable Subject Matter section MARINO grammatically suggests “parcels17 of land according to natural climate zones or similar physiographic regions” are grammatically equivalent/similar and not that regions/lands are like each other as claim 1 suggests, page 14, [0047] last S and fig. 27C: PNG media_image2.png 415 1065 media_image2.png Greyscale “Individual states may define leakage on a state-wide scale defining parcels of land according to natural climatic zones or similar physiographic regions and have an option of enacting state level legislation incorporating cap and trade provisions as well as practical options as described above to address leakage” as the closest to the claimed “a cluster of portions of land associated with a threshold similarity” and “geographically diverse locations” of claim 1. Smith (US 2014/0164070 A1) as discussed in the Allowable Subject Matter section Smith teaches a map with geographic-similarity-threshold via [0054] last S: PNG media_image3.png 710 1037 media_image3.png Greyscale “For example, graphically as error bars, color shading on a map of a geographical region18, by omitting to display data that is below a threshold certainty, by graphical display of the probability distributions themselves, or in other ways.” as the closest to the claimed “a cluster of portions of land associated with a threshold similarity”. Richt (US 2018/0070527 A1) as discussed in the Allowable Subject Matter section Richt teaches “zone…cluster…threshold amount of similarity” via [0012] & fig. 1B: PNG media_image4.png 884 1068 media_image4.png Greyscale [0012] Generating a plurality of farmable zones that are each agriculturally distinct based on the subset of the agricultural data items may include generating one or more clusters of agricultural data items based on at least a threshold amount of similarity in one or more agricultural characteristics amongst the subset of the agricultural data items. The actions of the method may include, for each cluster, merging neighborhoods that are encompassed by the cluster to create a farmable zone. Generating a plurality of farmable zones that are each agriculturally distinct based on the subset of the agricultural data items may include generating one or more clusters of agricultural data items based on the representative value of each neighborhood. The actions of the method may include, for each cluster, merging neighborhoods that are encompassed by the cluster to create a farmable zone. as the closest to the claimed “a cluster of portions of land associated with a threshold similarity” of claim 1. Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENNIS ROSARIO whose telephone number is (571)272-7397. The examiner can normally be reached Monday-Friday, 9AM-5PM EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at 571-272-4637. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DENNIS ROSARIO/Examiner, Art Unit 2676 /Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676 1 machine learning: Computers, Digital Technology. the capacity of a computer to process and evaluate data beyond programmed algorithms, through contextualized inference (often used attributively), wherein data is defined: (usually used with a singular verb) information in digital format, as encoded text or numbers, or multimedia images, audio, or video. 2 agricultural: the science, art, or occupation concerned with cultivating land, raising crops, and feeding, breeding, and raising livestock; farming. (Dictionary.com) 3 modify: to change somewhat the form or qualities of; alter partially; amend, wherein amend is defined: to change for the better; improve. (Dictionary.com) 4 modify: to change somewhat the form or qualities of; alter partially; amend, wherein amend is defined: to change for the better; improve. (Dictionary.com) 5 modify: to change somewhat the form or qualities of; alter partially; amend, wherein amend is defined: to change for the better; improve. (Dictionary.com) 6 modify: to change somewhat the form or qualities of; alter partially; amend, wherein amend is defined: to change for the better; improve. (Dictionary.com) 7 on: in connection, association, or cooperation with; as a part or element of. (Dictionary.com) 8 single {curly brackets} represent a first matching limitation at a different line number in the other claim 9 double {{curly brackets}} represent a second matching limitation at a different line number in the other claim 10 “carbon sequestration” is in claim 9 of US 11,263,707 B2 11 double {{curly brackets}} represent said second matching limitation at a different line number in the other claim 12 single {curly brackets} represent said matching limitation at a different line number in the other claim 13 See corresponding single [brackets] in claim 1 14 Claim 19 rejected like claim 1 15 Claim 20 rejected like claims 1 and 19 16 Applicant’s disclosure: [00104] The storage device 1008 is any non-transitory computer-readable storage medium, such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. [00117] Embodiments may also relate to an apparatus or system for performing the operations herein. Such an apparatus or system may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory, tangible computer readable storage medium, or any type of media suitable for storing electronic instructions, which may be coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability. [00118] Embodiments may also relate to a product that is produced by a computing process described herein. Such a product may include information resulting from a computing process, where the information is stored on a non-transitory, computer readable storage medium and may include any embodiment of a computer program product or other data described herein. 17 parcel: a distinct, continuous portion or tract of land, wherein distinct is defined: different in nature or quality; dissimilar (sometimes followed by from ) wherein dissimilar is defined: not similar; unlike; different. (Dictionary.com) 18 region: an extensive, continuous part of a surface, space, or body, wherein continuous is defined: being in immediate connection or spatial relationship, wherein immediate is defined: having no object or space intervening; nearest or next, wherein near is defined: close in relation; closely with respect to connection, similarity, intimacy, etc. (often used in combination). (Dictionary.com)