Patent Application
20240020774
The present disclosure relates generally to the field of environmental sciences, and more particularly to the technological field of precision conservation, including precision conservation systems and methods for reducing carbon dioxide, nitrous oxide, and similar greenhouse gas emissions from fertilization operations in agricultural ecosystems.
Most large scale, commercial crop, agriculture operations utilize nitrogen-based fertilizers, sprayed at one or more times during a growing season. These fertilizers are applied in a variety of ways, but some of the most common methods for row crops involve spraying water-born fertilizers. Regardless of the method of application, there are several ways that nitrous oxide (N2O) emissions can occur, ranging from direct emission to indirect nitrogen loss.
Three common nitrogen fertilizer sources are anhydrous ammonia, urea and urea ammonium nitrate solutions (UAN). If applied properly, anhydrous ammonia can be a relatively stable nitrogen source, as it is the slowest fertilizer to be converted to nitrate. In contrast, urea is a comparatively unstable source, owning in large part to its susceptibility to volatilization losses. Volatilization of urea can occur rapidly depending upon weather and soil conditions, which can change quickly in uncertain climates. UAN solutions typically contain a mixture of urea and ammonium nitrate, meaning it can be partially subject to a variety of losses such as volatilization, leaching or denitrification.
Some studies have shown evidence that in some cases only about 40% to 60% of nitrogen applied is taken up by a crop. Another 20 to 30% can remain in the soil after harvest and 10 to 20% becomes unavailable to plants during the growing season. The losses are due to many factors, which can include volatilization, leaching and denitrification. Volatilization occurs when fertilizers containing urea undergo rapid hydrolysis in the soil. Significant losses of ammonia gas can occur. Nitrate-nitrogen is susceptible to losses from leaching and denitrification. Leaching is most likely to take place in coarse-textured soils. Denitrification of nitrate-nitrogen occurs under saturated conditions on fine-textured soils. Over 100 lbs. of nitrogen per acre can be lost from denitrification in five days under the proper conditions. Indirect N2O emission can occur in several ways based upon these types of losses.
Direct N2O emission from farms and other agricultural zones can be caused by soil microbial activity. This can include nitrification and/or denitrification, which (due to these processes being microbial in nature), are affected by multiple factors including some that can be controlled and some that cannot (temperature and rainfall).
Regardless of the formulations and application methods, nitrogen-based fertilizers cause the release of N2O into the atmosphere. Nitrous oxide is recognized to be a potent greenhouse gas, similar to carbon dioxide and methane. The emission of nitrous oxide into the atmosphere has been correlated with contributing to global warming by trapping heat and destroying stratospheric ozone.
New data and sensing technologies have the potential for more effective precision agricultural methods and management systems, including more precise application of nitrogen-based fertilizer application. Improved application of nitrogen fertilizer through precision agriculture technologies can lead to co-increases in productivity, profitability, conservation, and environmental co-benefits at an agricultural site. These scientific and technological advances suggest a number of alternative pathways to more sustainable agricultural systems, including improved spatial and temporal management of existing cropping systems, as well as system changes that involve new crops and alternative land uses. While profitability of an agricultural site is dependent on costs and productivity of crop yield, reduction of greenhouse gas emission can allow for another factor to be taken into account in profitability: the value of farms participating in incentive programs, such as carbon cap and trade systems.
Therefore, it would be desirable to have a system and method which use verified, variable rate prescription maps, normalized by yield stability zones, to calculate N2O emission reduction achieved through use of optimized prescription maps (e.g., based on IPCC method of emission factors, assuming that 1% of fertilizer applied is lost to N2O). For example, it would be desirable to have systems and methods that achieve reduction of nitrogen greenhouse gas emissions in agro-ecosystems, such as by using yield stability maps, crop modeling, and/or remote sensing, and generate verifiable carbon credits maps from precision application of reduced nitrogen fertilizer.
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
The present disclosure describes aspects and features of various systems and methods that improve upon several technological fields including precision conservation and agricultural equipment. These aspects and features may be embodied, combined and substituted in combinations and permutations as implemented for one or more specific applications.
In one respect, various methods are provided which may achieve some or all of the advantages described herein. For example, a method for reducing nitrogen application to agricultural land may comprise the steps of: determining yield stability for at least two zones of an agricultural land; generating a nitrogen fertilizer prescription for the agricultural land, comprising different fertilizer applications for the at least two zones; receiving an indication of nitrogen fertilizer application corresponding to the prescription; determining a reduction in nitrogen fertilizer from the indication; and selling carbon credits corresponding to the reduction in nitrogen fertilizer.
In another respect, various systems are provided which can include some or all of the features, components, connections, and software described herein. For example, a system for precision conservation may comprise: at least one processor; at least one memory in communication with the processor, having stored thereon a set of instructions which, when executed, cause the processor to: receive a request for a fertilizer prescription for a crop to be grown on an agricultural area of interest; determine a crop yield stability map for the agricultural area of interest; send at least one proposed prescription to a remote computer associated with the agricultural area of interest; receive data corresponding to at least one actual fertilizer application for the agricultural area of interest; and determine a reduction in greenhouse gas emission based upon the at least one fertilizer application.
In another aspect, a kit may be provided, which may comprise at least one processor; a connection for receiving location positioning system; a valve controller adapted to connect to valves of a fertilizer sprayer; and a memory connected to the processor, having a set of instructions stored thereon which, when executed by the processor, cause the processor to: obtain real time location information from the connection for receiving location positioning system; send control signals to the valve controller to adjust spray application of a fertilizer to be sprayed by the sprayer at zones of a farm according to a fertilizer prescription stored in the memory for that farm; and record data indicative of the applied amounts of fertilizer over the farm.
In another aspect, a method may be provided for optimizing utilization of farmland. The method may comprise obtaining information from a user concerning a farmland and attributes (including size, shape, and/or access requirements) of at least one alternative farmland purpose; determining a crop yield stability map for the farmland; determining a profitability map for the farmland based upon the crop yield stability map, crop commercial data, and farming cost data; selecting at least one section of the farmland having a low profitability compared to at least some of a remainder of the farmland, while still being suitable for the alternative farmland purpose; providing a visual depiction to the user of a location of the at least one subset, a baseline overall profitability of the farmland, and an alternative profitability of the farmland if the subset were no longer used for crop growth but instead for the alternative farmland purpose. Additional embodiments of this method may also include the alternative profitability of the farmland taking into account revenue generated through the alternative farmland purpose (such as through solar or wind power generation, generation of carbon credits, or the like). Further embodiments of this method may also provide alternative estimates of profitability based on a set of potential alternative farmland purposes, including solar power generation, wind power generation, native vegetation, or reduced crop growth, and a recommendation of an optimal alternative purpose for maximizing profitability of the farm.
The various systems and methods disclosed herein embody and implement numerous advantageous features. A more efficient application of fertilizer to an agricultural unit results in reduced usage of fertilizer without sacrificing yield. This increases profitability for agricultural units. In addition, allowing the operator of the agricultural unit to select from among various parameters for developing a fertilizer prescription map can leverage the operator's direct knowledge of the agricultural land, helping to fill gaps in available data. Providing an automated system with prescription options presented in a user-friendly interface can allow for ease of coordinating user preferences, while optimizing many environmental and conservation factors. Water quality is improved from reduced nitrogen losses, soil organic carbon sequestration is increased from the a reduced amount of soil disturbance (no tillage in locations that are prescribed for no planting), perennial vegetation cover and perennial root system from curated or native species, carbon credits from the abatement of GHG emission for no fertilizer N applied to these areas as described in the prescription map.
The reduction of N fertilizer in accordance with the systems and methods herein is based on the difference between N applied to high stable zones (the highest amount) and the N applied to Low, Medium and Unstable Zones through a verified machine-readable file.
A method has been developed which can determine the likelihood that a given area within an agricultural field will provide similar yields year after year. If sections of a field consistently produce high, medium, or low yields, then they may be characterized as high-and-stable (HS), medium-and-stable (MS), or low-and-stable (LS) zones, respectively. If crop productivity on a given area continually fluctuates between high and low yields from year to year, then it may classified as an unstable (U) zone. It is to be understood that more or less granularity (e.g., more or fewer levels of classification) can be utilized to classify zones of an agro-ecosystem.
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From the above-mentioned study, it was determined that yield stability tightly correlates with profits; that trend held across most of the analyzed fields. Tables 1-4 below, show exemplary data generated by an analysis of yield stability and productivity for an 80M acre area of the upper Midwest.
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Based on the knowledge ascertained from the above-referenced studies and further analysis performed by the inventor, systems and methods have been developed for implementing precision conservation methods across one or more agroecosystems, such as an individual farm, or a group of managed farm lands, a county or other governmental unit, or customers of farm equipment or farm management services. The systems and methods described herein allow those managing the agroecosystems to reduce collective utilization of N fertilizer in a targeted and informed way, to maximize yield of better performing zones of an agroecosystem, to minimize expenditures and reduce losses in poorer performing zones, and to capture value from reduced fertilizer use through carbon offset credits. For example, one method is based on an integrated geospatial system that helps predict yield and N requirements in HS zones and to understand the underlying causes for the poor performance of the LS or U zones and design appropriate strategies to manage them. This data-driven platform capitalizes on the strengths of three major precision technologies (remote sensing, crop modeling, and yield stability analysis) to deliver a turn-key solution to farmers in the form of N application rate prescription (Rx) maps for easy implementation in the field. These big-data analytics have been shown to increase resource use efficiency, environmental sustainability, and profitability for the producers.
In one implementation, a method first determines a yield stability map using one or multiple techniques: e.g., from grower yield monitor data, using airborne or satellite imagery, or both. Then the method develops an N fertilizer map corresponding to the different stability zones. In one implementation, nitrogen fertilizer rates may be determined using the validated SALUS model for each yield stability zone to spatially account for the implicit variability of soil N mineralization rates, and losses of nitrates to deeper layers in each field.
The SALUS model is described by way of illustration, but other crop models known in the art also may be used, for example including CROPGRO for major grain legumes, CERES for cereal crops, and SUBSTOR for crops with belowground storage organs, all of which are available in the Decision Support System for Agrotechnology Transfer (DSSAT) suite of crop models used to simulate crop biomass and yield as influenced by weather, soil, crop management, and crop genotype.
The Systems Approach to Land Use Sustainability (SALUS) is similar to the DSSAT family of models, but further simulates yields of crops in rotation as well as soil, water and nutrient dynamics as a function of management strategies over multiple years. SALUS accounts for the effects of rotations, planting dates, plant populations, irrigation and fertilizer applications, and tillage practices. The model simulates daily plant growth and soil processes on a daily time step during the growing season and fallow periods. SALUS contains (i) crop growth modules, (ii) soil organic matter (SOM) and nutrient cycling modules, and (iii) soil water balance and temperature modules. The model simulates the effects of climate and management on the water balance, SOM, nitrogen (N) and phosphorous (P) dynamics, heat balance, plant growth, and plant development. Within the water balance, surface runoff, infiltration, surface evaporation, saturated and unsaturated soil water flow, drainage, root water uptake, soil evaporation, and transpiration are simulated. Soil organic matter decomposition, along with N mineralization and formation of ammonium and nitrate, N immobilization, and gaseous N losses are also simulated.
Crop development in the SALUS model is based on thermal time calculations modified by daylength and vernalization. Potential crop growth depends on intercepted light using solar radiation data and simulated LAI, and is reduced by water or nitrogen limitations. The main external inputs for the crop growth simulations are the plant genetic coefficients and climate data (e.g., daily solar radiation, precipitation, and air temperature). The SALUS model simulates SOM and N mineralization/immobilization from three soil organic carbon pools (active, slow, and passive) that vary in their turnover rates and characteristic C:N ratios. A soil P model incorporates inorganic P (labile, active, and stable) and organic P dynamics. The soil water balance calculates infiltration, drainage, evaporation, and runoff.
Input data to the SALUS model includes weather, soil and crop management activities, soil properties, genetic characteristics of the crop, and the site location. SALUS accounts for weather variability by using up to several decades of existing weather information to represent a historical weather data period. Daily totals of rainfall and solar radiation along with the maximum and minimum temperature over the historical weather data period provide a relatively accurate crop simulation. Weather data is preferably obtained at a site near the area where the crop model is to be applied, especially for daily rainfall. Temperature and radiation are more spatially uniform, so the weather station need not be on site. Most weather stations record rainfall and temperature but not always solar radiation. Accurate solar radiation data can be obtained from NASA (e.g., directly via an internet connection) with a spatial resolution in 1-degree grid cells. This NASA data source also provides all of the daily weather data input for the SALUS model with the same spatial resolution issues as with solar radiation. Soil input properties include the lower limit of available soil water, the field capacity or drained upper limit (DUL) water content, soil texture, soil bulk density, and soil organic matter content. Irrigation input characteristics include the dates, amounts, and mode of application. The crop variety, genotype, or cultivars also are specified, for example with wheat and corn cultivar information generally being expressed as genetic coefficients, which allow models to simulate crop phenology over a wide range of latitudes and planting times. A modeling method such as SALUS can be used to determine optimal (or optional) N-fertilizer rates and applications on a spatial distribution basis over the area of an agro-ecosystem (farm, co-op, county, etc.), by simulating in each of the stability zones the rates of N-fertilizer that do not impact yield (or only cause a minimal or acceptable impact to yield when considering other factors, such as overall profitability) while reducing N losses. The underlying stability maps and analyses may be generated from analysis of data from yield monitor (yield maps) or from imagery.
The variable rate prescriptions for each fertilization cycle of a given farm are spatially assigned—in other words, fertilizer application is prescribed on a zone-by-zone basis with high specificity. In one example, a prescription map was developed with resolution on a 2 m2 basis. The prescription maps can be generated using the SALUS model to determine which zones would likely benefit (and how much) from various levels of N fertilizer application, and then supplemented or modified on in-season remotely sensed imagery, feedback from the farmer or manager of the land, changes to intended irrigation amounts/timing, weather, or the like. For example, in one embodiment, areas of low reflectance in remote sensing/imaging (indicative of poor plant health, or more bare soil visible because the plants were not present or have not grown as rapidly or fully), can be identified in historical data. Based upon yield modeling, the systems and methods described herein can determine how much (if any) N fertilizer to apply to those zones versus medium or high zones. For example, LS zones that have been determined to have consistently low yield may be prescribed to receive little to no N fertilizer at the initial fertilizer application as well as some or all subsequent fertilizer application cycles (if any). This is because it is determined that those zones are not producing a worthwhile yield in recent seasons. And, reflectance can continue to be monitored during a season remotely. For LS zones that have not responded well to initial fertilizer applications (as determined by low reflectance), subsequent applications of N fertilizer can be cancelled altogether or reduced.
For unstable zones, an initial application of fertilizer can be made based upon modeling from historical data. Then, in-season monitoring of reflectance can aid in determining whether and how much additional N fertilizer should be applied. If a historically-unstable zone exhibits signs of good growth in a given season, then additional N fertilizer may be applied. If a historically-unstable zone exhibits signs of poor growth (e.g., low reflectance), then additional N fertilizer may be avoided.
The areas of high reflectance, as a result of good plant health, will receive higher rates of N fertilizer (and typically be found in HS and MS zones). In other words, areas or zones of a farm or other agro-ecosystem that exhibit high reflectance can be interpreted as corresponding to high yield/stable zones. This is because these zones have better soil/topographic conditions and, as a consequence, generally produce higher yielding plants on a consistent basis. In comparison, zones of low reflectance are the result of poor optical remotely sensed vegetation indices, and thus can be interpreted as low yield/stable zones. Reflectance data for in-season growth can be used to determine which areas will best respond to a prescription map modeling (such as SALUS) to determine the best N rates for maximum reduction in N loss and better overall profitability. In some embodiments, however, reflectance data is not converted directly to N-fertilizer prescription rates. Rather, reflectance data may be used to inform where to utilize the SALUS model to determine optimal N rates for yield and lower N losses. Historical reflectance data can be used to develop stability maps, and in-season reflectance data can be used to generate a prescription map for that specific season.
In other embodiments, a sustainability index (e.g. a sustainability score given to a specific zone) may be utilized directly for N fertilizer prescription. The sustainability index is based on the ranking of site-specific results such as crop yield, nitrogen use efficiency (“NUE”), water use efficiency (“WUE”), surface water runoff (or just “runoff”), nitrate leaching (or just “leaching”), soil organic carbon change (or “C % change”), carbon dioxide emission, and nitrous oxide emission. These values can be obtained by running a crop model for a long-term time period (e.g., 5 or 10 to 20 or 30 years), to determine the distribution of the different results and to rank with scores the different percentiles (from low to high) of different simulated variables. Each percentile is assigned a score (e.g., 1 being low to represent a poor or undesirable value, 2 being high to represent a good or desirable value). This calculation is done for all the dependent variables incorporated into a given sustainability index definition. Based on the classification by zone of a farm, an application of fertilizer can be prescribed. Zones with high nitrogen use efficiency, good water use efficiency, low runoff, low nitrate leaching and low emission of carbon dioxide and/or nitrous oxide may be given comparatively higher levels of N fertilizer.
The variable rate prescription can be converted to a shapefile or other suitable map-based file for use by a fertilizer application equipment, and sent electronically to the farmers. The Rx N prescription maps will be applied using a variable rate applicator—the prescriptions can take the form of electronic files that instruct the operator or the equipment itself (in an automated or autonomous mode of operation) to vary the rate at which fertilizer is mixed with a carrier, to vary the pressure/flow of dissolved/carried fertilizer, vary the speed of the application equipment (e.g., it moves more quickly over LS zones), or to simply prescribe the travel path of the application equipment (e.g., it altogether skips LS zones, or only travels over them for a subset of the all/regular fertilizer applications).
As described herein the number of zones for prescription fertilizer application within a farming location may be set by a user (e.g., for purposes of coordination with the abilities of available spreader equipment, or for other preferences), or may be determined by a system or method that is providing the prescription map. The number of zones may be as low as two (e.g., “high” and “low” yield zones), may be three (e.g., “high”, “medium,” and “low”), may be four (e.g., “high,” “medium,” “low”, and unstable), may be five (e.g., “high,” “medium-high,” “medium-low,” “low,” and unstable) or may include other variations (including “somewhat unstable,” “very unstable,” “unstable high yield,” “unstable medium yield,” “unstable low yield,” or the like). Additionally, a farmer may have the ability (e.g., via a web portal, mobile app, control display of a spreader truck, or other user interface) to change the zone categorization for some or all of one or more zones within a prescription map, or to request more zone categorizations (e.g., adding “medium-high”, “medium-low”) or fewer zone categorizations be used.
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At step 614, one or a group of files and information may be generated for the subject land and sent to the requestor. This may include: a stability map of the land, a set of profitability projections based upon various comparative rates of fertilizer application from high to low zones, suggested options for how to approach unstable and low-yield zones, software patches and/or instructions for how a prescription map would be installed and used and the resulting “as-applied” data returned to the company performing the method. In some embodiments, an optimal recommended prescription map may be included.
A stability map for the land may be created in any of the techniques described herein, such as using remote sensing techniques (including, e.g., satellite imagery or drone images), actual yield data provided by the farmer, both, or other techniques known in the art.
In some embodiments, the requestor may be provided with several options for how to approach unstable and/or low yield zones, such as: not planting those zones at all and allowing them to return to a natural/wild state; planting cover crops in those zones; planting the zones but not fertilizing them; planting the zones but fertilizing at fewer instances than other zones; and/or planting the zones but applying less fertilizer at each scheduled application. Along with these options, the requestor may also be given various projections of the impacts of selecting each option, such as: total reduced greenhouse gas emission, total N fertilizer savings, total predicted yield, or similar information. Such projections may also be given in a map format, on a zone-by-zone basis, or geographic basis.
For example, a report including and/or based upon variations of the maps of
In other embodiments, the requestor may optionally be asked at step 616 to provide specific input on the extent to which low yield or unstable zones should be fertilized or planted, or to simply select one of the options presented in step 614. The requestor's preferences and selections can then be taken into account when generating optimal prescription maps.
In some embodiments, the requestor may be provided with several alternative options for how to utilize unstable and/or low yield zones, apart from simply eliminating or reducing fertilization; using a cover crop or alternative crop; allowing native vegetation; or the like. For example, the method may determine one or more zones are unstable and/or low yield, and the requestor is suggested not to continue regular growing/fertilization habits in those zones, such as via the processes described above. The method may further determine that the one or more zones are suitable for an alternative renewable energy source (e.g., a wind turbine and/or solar panel) and the requestor is provided with a recommendation to utilize the unstable and/or low yield zone with renewable energy sources as an alternative to planting in the zone. The method may determine a particular zone is suitable for renewable energy sources based on, for example, the stability of the zone, an area of the zone, solar radiation data associated with the zone, wind capacity data associated with the zone, and the like. Along with these options, the requestor may also be given various projections of the impacts of selecting each option, such as: total reduced greenhouse gas emission, the impact on N Savings, Carbon Credits, Cash from Carbon Credits, Profitability, or similar information. Such projections may also be given in a map format, on a zone-by-zone basis, or geographic basis.
At step 618, the method determines whether the selections from the requestor reflect the optimal prescription for the land of interest. If the user declined the optimal prescription, or requested modifications (e.g., planting in LS zones despite a recommendation not to do so, or increasing the minimal fertilizer application for LS or U zones) at 620, then the method generates a new prescription map at 624 according to the user's request and sends it to the user at 626. If the user accepted the original optimal prescription map 622, then the map may be sent to the user at 626 if not previously done. The prescription map sent to the user may be in a format that can be installed on an agricultural sprayer truck capable of dynamic variable fertilizer application. In other words, when the prescription map is installed on the sprayer, the onboard controller of the sprayer will automatically and dynamically adjust the valves and spray rate of its nozzles to cause the prescribed amount of fertilizer to be sprayed onto the specified zones of the agricultural unit or land of interest.
At step 628, the method waits to receive data from the requestor indicating actual amounts of N fertilizer applied, based on the chosen prescription map. In some embodiments, the farmer or land manager may obtain an “as-applied” file from the fertilizer equipment that indicates a mapping of actual application of N fertilizer, then upload that file via a web portal. Many existing commercial fertilizer sprayers will automatically generate such a file. To promote ease of use, the software operating the web portal may accept multiple formats of “as-applied” files (e.g., the native file format from a commercial sprayer made by, e.g., John Deere, Hagie, Miller, Hardie, or Case), then pre-process them to extract spatial as-applied data for uniform usage and comparison to stability maps and target prescription amounts.
In other embodiments, a software update or patch may be provided for the fertilizer application equipment so that a custom “as-applied” file format is generated. This file could be formatted so as to allow for security, immutability, and/or verification of the stored data, such as in a blockchain format. For systems utilizing consistent, verifiable file formats, the fertilizer sprayer/equipment may have a software patch or upgrade installed that operates on-board to output an individual verifiable data file (e.g., via a cellular, wifi, or other wireless connection) or simply append “as-applied” data to a blockchain associated with the fertilized land. Alternatively, for spraying equipment that does not natively contain the capability to generate “as-applied” data, a retrofit kit may be supplied that connects to an existing sprayer such that it manages and records the opening/closing of valves for the spray heads of a sprayer, pressure, flow rate, and the like so as to generate as-applied data.
At step 630, the method may optionally continue to monitor conditions relating to growth of crops on the land of interest. For example, the method may monitor: weather (including daily temperature highs and lows, average daily temperature, average weekly temperature, amount of daily sunlight vs. cloud cover, average humidity, amount of total rainfall, volume of rainfall during individual storms, and the like) for the land of interest; reflectance data from remote sensing (e.g., drone imaging or satellite imaging); individual crop height measurements uploaded by the farmer for a number of specified locations within each type of zone; and the like.
From these measurements (or by merely scheduling), the method may then propose to the farmer prescriptions for subsequent, in-season fertilization at step 632. These proposals may be auto-generated or as requested by the farmer. In some implementations, the SALUS method (or other methods as discussed above) can be used to prescribe fertilizer application maps for in-season fertilization. The prescription map can thus be updated to reflect actual, in-season conditions for the specific farm or region at issue. This can allow further savings of N fertilizer in two respects: first, N fertilizer savings may be achieved through spatially tailored application of fertilizer in accordance with stability zones as described above, with LS zones receiving generally less fertilizer than HS or MS zones; second, N fertilizer savings may be achieved with respect to unstable zones. For unstable zones, acquired in-season data can be leveraged to determine whether the unstable zone will have a productive yield or not for this particular growing season. Where crops in unstable zones of a farm have experienced weather, washout, or other conditions not conducive to a productive yield for that zone (e.g., as evidenced by historical data), or the crops are measured as not having sufficient early growth to result in a yield as productive as desired (e.g., as evidenced by remote sensing or on-premises measurements), fertilizer application can then be reduced or stopped altogether for the unstable zones in the prescription maps for future applications in that growing season.
At step 634, the method can obtain a farmer's confirmation of the proposed prescription map 636 or alternatively can obtain requested modifications 638 from the farmer. If modifications are requested, then the in-season prescription map can be modified accordingly at step 640 as described above in connection with step 624. The prescription map can then be sent at step 642 to the farmer, directly to a contract provider that fertilizes or manages the land, or directly to the sprayer equipment or kit via over-the-air connection.
At step 644, the method then receives as-applied data corresponding to the second prescription map, in a manner similar to that of the first as-applied data. This process can then iterate for subsequent fertilizer applications at step 646 during a season. At the end of the season (or at various points during a season, such as after each fertilizer application, every other fertilizer application, or dynamically as a threshold N-savings is achieved), the as-applied data can be processed to determine N-savings at step 648. In some implementations, this is done in a verifiable way, such as through a blockchain, a public record (e.g., county land records, or state agriculture agency records), or other record so that future use of this data to justify a carbon credit sale can be preserved.
Next, assumed CO2/N2O reduction can be calculated at step 650 (or equivalent greenhouse gas or other environmental benefits). Based upon the type of fertilizer applied, and the relative reduction in N-applied as between the high yield (HS) zones and reduced zones (e.g., LS, MS, U, etc.), total CO2/N2O reduction can be computed using known methods such as: IPCC emission factor, where 1% of N fertilizer applied is lost as N2O. 1 kg of N2O is equivalent to 300 kg of CO2. It should be understood that other methods of calculating N2O emissions may be utilized, such as methods that more particularly take into account site factors affecting loss of N2O (soil conditions, temperature, etc.). From total CO2/N2O reduction, the equivalent number of carbon credits can be measured. Records relating to each credit can be associated with the underlying N-reduction data, such as a blockchain record or other verification as discussed above.
At step 652, the carbon credits can then be sold in a suitable marketplace such as the California Cap and Trade System or the European Union Emission Trading Scheme (EU ETS). As part of this step, the verified N-reduction data and calculated CO2/N2O reduction that supports carbon credits can be provided to the appropriate verification body. Reduced N application and CO2/N2O reduction for multiple farms, regions, states, etc. can be aggregated to support sales of carbon credits. At step 654, the proceeds from sale of the credits can then be allocated to users of the system. When proceeds are remitted, at the end of a growing season, or at another suitable time, information can then be provided to users regarding profitability of using the system. This may include calculated savings of reduced N fertilizer usage plus proceeds from carbon credit sales, less any impact on yield. A fuller profitability analysis can be provided if the farmer also provides actual yield data (e.g., directly from combine/harvester data) on a spatial basis over the farm.
In a similar way, other environmental benefits of reduced fertilizer usage can be leveraged. For example, as governments or other institutions implement credit programs to incentivize lessening of fertilizer runoff/leaching into waterways, groundwater, etc., a similar method of capturing precise data on savings of fertilizer application could be implemented.
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The server 702 may be connected to a private database 712, which contains information 714 pertinent to the generation of prescription maps as described herein. For example, private database 712 may include remote imaging records, yield information, and impact of various types of N fertilizer on various types of crops (such as corn, soybeans, wheat, etc.) both with and without prescription maps as discussed herein. Database 712 may also store data correlating actual prescription maps, to actual as-applied N fertilizer data, and actual resulting yield on a farm-by-farm, or zone-by-zone basis, or in a manner that can be queried by a number of different attributes (e.g., location, climate, rainfall, weather, crop variety, etc.). This data can be used to implement better predictions of yield for future prescription maps. Database 704 can also store historical records for a given farm, such as profitability before and after use of prescription maps and precision conservation methods as described herein. These records could be provided to real estate services and farm management services for their business purposes. The private database may also contain profitability information for prior years, including crop prices, N-fertilizer prices, etc. Server 702 may also connect to repositories of public information 716. These databases may contain information 718 such as historical satellite imagery for relevant locales, historical weather data, crop prices, as well as similar data.
Server 702 interacts with users that desire tailored prescription maps that achieve precision conservation, as described herein. Users may include farms, such as farm 720, or may include group co-ops, farm management services providers, counties, university extensions, or government/non-profit conservation initiatives. Farm 720 may include at least one locally-stored device that requests and receives communications with the server 702. This can comprise a local computer, a WiFi connection that connects to a kit or equipment 706, or other suitable network connection. The farm 720 sends requested information to the server 702, and receives recommendations and prescription maps from the server 702. The prescription maps may be manually loaded into an onboard memory/controller of the sprayer equipment 706 (or alternatively, a retrofit kit) or automatically installed. The prescription maps will be tailored to the specific geography of the farm land 720. Thus, when the farm computer makes a request for a prescription map, some of the information the farm computer may provide to the server 702 includes: a description of the land parcel, the type of spraying equipment 706, the desired type of fertilizer, the crop type, planting date, irrigation schedule, and similar information.
The server 702 may utilize information concerning the farm 720 provided by the farm computer to access satellite or other remote imaging data from a remote repository 712, 716. This can be done to develop stability maps for the farm 720, initial prescription maps, and subsequent in-season prescription maps. The server may also be in communication with an exchange marketplace or auction site 722 to verify and sell carbon credits. In some embodiments, the server 702 may communicate directly with the marketplace 722 to verify sales of the carbon credits and collect the proceeds for distribution to farms. In other embodiments, the server 702 may simply communicate with a third party verification service. The farms may then collect remuneration for their carbon credits directly. Similarly, the server 702 may communicate with networks of a government or similar agency that provides incentive programs for greenhouse gas reduction or carbon sequestration 724.
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Referring now to
Referring now to
By not planting crops at all in low/stable or similar zones of undesirable yield characteristics, the profitability determination for those zones is affected differently than the profitability of other zones in which crops will still be grown. For example, savings in seed, time/resources spent in tilling and land management, irrigation, etc. must be accounted for. Likewise, the fact that no harvest will be obtained must also be taken into account. Thus, profitability map 1040 can be tailored to take these factors into account, and can be included as an option for a user requesting prescriptions that might desire alternatives. Furthermore, cover crops and native vegetation could in some instances generate eligibility for further carbon sequestration incentive programs.
In some embodiments, a user interface or portal may be provided to a user, in which maps such as those shown in
Alternatively, in some embodiments, a user that has already determined to reduce use of available acreage in favor of a renewable energy installation (or any other use, such as alternative crops, dividing parcels, etc.), can utilize the systems and methods described herein to select which segments of a farm or other property to sacrifice for the alternative purpose. For example, even if no portions of a farm are low/stable, low/unstable, etc. (or otherwise clearly unprofitable or inferior), a user may still want to know the least profitable zones of a farm (relative to the rest of the farm) so that the lesser-profitable zones can be sacrificed to, e.g., a wind turbine. Accordingly, a dynamic user interface could be provided, to allow a user to select areas on a profitability map or satellite map that would potentially be removed from crop growth purposes, and select alternative uses for that land. E.g., a user might select an area of land the size of a wind turbine foundation and an access area, and the interface may provide to the user impact on overall profitability for the entire farm. The interface may take into account positive revenue generation from power generation, available government incentives and rebates and the like (including potential carbon reduction credits, if available), as well as savings from no longer planting and growing crops, against loss of potential revenue from the expected harvest that would otherwise have been available for that land. The interface may also predict for the user the optimal locations on a plot of land to devote to non-farming purposes.
Various designs, implementations, and associated examples and evaluations of a system for precision conservation through reduction of greenhouse gas in agricultural operations are described above. However, it is to be understood the present invention has been described in terms of one or more preferred embodiments, and it should be appreciated that many equivalents, alternatives, variations, and modifications, aside from those expressly stated, are possible and within the scope of the invention.
This application claims priority to U.S. Provisional Application No. 63/123,340, filed on Dec. 9, 2020, the entirety of which is incorporated herein by reference.
This invention was made with government support under 2015-68007-23133 awarded by the U.S. Department of Agriculture. The government has certain rights in the invention.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2021/062703 | 12/9/2021 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63123340 | Dec 2020 | US |
