WO2014011810A1 - Système et procédé pour la gestion des risques agricoles - Google Patents

Système et procédé pour la gestion des risques agricoles Download PDF

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Publication number
WO2014011810A1
WO2014011810A1 PCT/US2013/049979 US2013049979W WO2014011810A1 WO 2014011810 A1 WO2014011810 A1 WO 2014011810A1 US 2013049979 W US2013049979 W US 2013049979W WO 2014011810 A1 WO2014011810 A1 WO 2014011810A1
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data
information
entity
database
feeder
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PCT/US2013/049979
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English (en)
Inventor
Terry Griffin
Clint JAYROE
Chism CRAIG
Barry Knight
Charles Michell
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Cresco Ag, Llc
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Priority claimed from US13/833,792 external-priority patent/US20140278731A1/en
Application filed by Cresco Ag, Llc filed Critical Cresco Ag, Llc
Publication of WO2014011810A1 publication Critical patent/WO2014011810A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06—Asset management; Financial planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Mining

Definitions

  • the invention relates generally to portfolio analysis of agricultural products.
  • portfolio analysis allows a financial player to evaluate and hedge risk across the player's financial holdings as a whole, on the recognition that many financial products with higher potential payoff also carry higher potential losses. Stated differently, risk frequently accompanies potential reward, such that the financial products that carry the highest potential return on investment also carry the greatest risk of dramatic loss of principal.
  • Principles of portfolio analysis seek to counterbalance riskier investments by including more stable investments in the portfolio as well. The result is that a portfolio may be stacked with products having varying degrees of risk in order to target a fairly predictable outcome range in terms of ultimate performance.
  • Environmental characteristics (some based on soil qualities, e.g., nutrient density, water holding capacity, depth of nutrients, texture, depth to impermeable layer, percent organic matter, structure, etc.; and others based on external environmental factors, such as rain, river flooding, sun exposure, temperature, etc.) and management techniques (tillage interval, formalized watering or fertilizing schemes, post-production burn, etc.) simply are not uniform. Therefore whereas a given stock behaves identically in any portfolio, an agricultural product will not perform the same on every field.
  • a farmer might improve his or her balance of risk and potential yield by selecting a portfolio of seeds or other crop inputs to plant or apply in a given year, on a particular schedule, or at given locations in his or her field(s). Given the right portfolio of products, as growing conditions in a given year lead to lower yield in one variety, strong performance from another variety might offset some of the realized risk, providing balance and predictability to overall yield and expectations.
  • inventions discussed herein involves the application of portfolio analysis processes to various forms of agricultural production.
  • embodiments may take into account the variability of environmental characteristics and management techniques, and apply to them an aggregated array of data selected from sources such as public data, field trials, vendor data, and observed commercial production data collected from growers.
  • the data is gathered and maintained in a manner that is indifferent to outcomes and expectations.
  • the aggregated data is applied in the current methodology to create a custom-tailored prescription plan for a grower that takes into account not only the grower's risk tolerance as in traditional portfolio analysis, but also the unique circumstances of the grower's environmental characteristics and management techniques.
  • the methodology involves grouping growers with similar environmental characteristics and/or management techniques, and at the point of calculation limiting the data considered to, or giving greater weight to, data that originated from that group of growers or from environmental characteristics and/or management techniques involving the same or a similar paradigm.
  • One embodiment disclosed is a process for providing benefits such as those discussed above, while maintaining confidentiality in a manner that traditional analytics could not do.
  • Figure 1 shows a field map exemplifying the variance in management techniques that may be exhibited across an agricultural field.
  • Figure 2 shows an exemplary process for determining risk for a portfolio of agricultural products according to one embodiment of the disclosure.
  • Figure 3 shows exemplary data that may be used or reported in connection with the teaching of the present disclosure, in this case, the yield per acre rate determined from actual production and harvest data drawn from observational reports across 70,000 acres of soybean fields planted with 31 different soybean varieties.
  • Figure 4 shows a graph based on the information that underlies the chart shown in Figure 3, depicting the average output (means) and covariances between all bundle combinations of products.
  • Figure 5 displays, with reference to Figure 3, the depicted Grower 2's selected soybean varieties and the percentage of total crop acreage planted, plotted against average yield versus risk.
  • Figure 6 depicts a process for performing the regression analysis and mathematical optimization on an agricultural portfolio, according to one embodiment of the disclosure.
  • Figure 7 is an alternate view of information in a similar scenario, as the same may be output in connection with the teachings of the present disclosure.
  • Figure 8A shows an exemplary report showing benchmarking results using an embodiment of the current disclosure.
  • Figure 8B shows another exemplary report showing benchmarking results using an embodiment of the current disclosure.
  • Figure 9 shows a diagram of an exemplary arrangement of databases and data flow in an embodiment of the present disclosure.
  • Agricultural production refers to any one or more of farming, orcharding, silviculture, pomology, olericulture, plantationing, other product-growing or product-harvesting endeavors in the nature of agriculture, or the like.
  • the term "storage device” as used herein refers to a machine-readable device that retains data that can be read by mechanical, optical, or electronic means, for example by a computer. Such devices are sometimes referred to as "memory," although as used herein a machine-readable data storage device cannot comprise a human mind in whole or in part, including human memory.
  • a storage device may be classified as primary, secondary, tertiary, or off-line storage. Examples of a storage device that is primary storage include the register of a central processing unit, the cache of a central processing unit, and random-access memory (RAM) that is accessible to a central processing unit via a memory bus (generally comprising an add ress bus and a data bus).
  • Primary storage is generally volatile memory, which has the advantage of being rapidly accessible.
  • a storage device that is secondary storage is not directly accessible to the central processing unit, but is accessible to the central processing unit via an input/output channel.
  • Examples of a storage device that is secondary storage include a mass storage device, such as a magnetic hard disk, an optical disk, a drum drive, flash memory, a floppy disk, a magnetic tape, an optical tape, a paper tape, and a plurality of punch cards.
  • a storage device that is tertiary storage is not connected to the central processing unit until it is needed, generally accessed robotically. Examples of a storage device that is tertiary storage may be any storage device that is suitable for secondary storage, but configured such that it is not constantly connected to the central processing unit.
  • a storage device that is off-line storage is not connected to the central processing unit, and does not become so connected without human intervention.
  • Examples of a storage device that is off-line storage may be any storage device that is suitable for secondary storage, but configured such that it is not constantly connected to the central processing unit, and does not become so connected without human intervention.
  • Secondary, tertiary, and offline storage are generally non-volatile, which has the advantage of requiring no source of electrical current to maintain the recorded information.
  • a storage device cannot be construed to be a mere signal, although information may be comm unicated to and from a storage device via a signal.
  • the term "telecommunications network” as used herein refers to a network capable of transferring information spatially by conducting signals, such as but not limited to electrical or optical signals.
  • the network itself cannot be construed to be a mere signal.
  • the "optical" signal need not comprise radiation in an optically visible wavelength, and may be in any suitable wavelength.
  • the network may be a packet-switched network (such as a local area network or the Internet) or a circuit-switched network (such as some telephone networks or the global system for mobile communications (GSM)).
  • Information sent via a packet-switched network may be for example electronic mail, an SMS text message, and a digital file sent via file transfer protocol (FTP).
  • Information sent via a circuit-switched network may be for example a voice mail message, a facsimile message, an SMS text message, or a digital file.
  • processor or "central processing unit” (CPU) as used herein refers to a software execution device capable of executing a sequence of instructions (“program”).
  • the CPU comprises an arithmetic logic unit, and may further comprise one or both of a register and cache memory.
  • variable refers to a symbolic name corresponding to a value stored at a given memory address on a data storage device (although this address may change).
  • the value may represent information of many types, such as integers, real numbers, Boolean values, characters, and strings, as is understood in the art.
  • value of a variable is always stored in a data storage device, and shall not be construed to refer to information only stored in a human mind. Any recitation of a variable implicitly requires the use of a data storage device.
  • machine-readable format refers to a medium of storing information that is configured to be read by a machine. Such formats include magnetic media, optical media, and paper media (punch cards, paper tape, etc.). Printed writing in a human language, if not intended or configured to be read by a machine, is not considered a machine readable format. In no case shall a human mind be construed as "machine readable format.”
  • database refers to an organized data structure comprising a plurality of records stored in machine-readable format.
  • a process is provided for data management and manipulation for use in portfolio analysis for agricultural production business.
  • the process is implemented by various embodiments of the system described in Part C and on storage devices capable of being read by a machine.
  • the process includes:
  • linking 114 a plurality of feeder databases to the master database in a manner such that a subset of information added to the feeder database populates forward into the master database, wherein the subset of information comprises the indication of source type of the data in the subset, information about environmental characteristics of geographical areas regarding which data is present in the subset, and information about management techniques of geographical areas regarding which data is present in the subset, and wherein further the subset does not include information to be treated as personal;
  • Other embodiments may perform only some of the steps outlined above, or may collect data or produce analyses that rely on more or fewer source types or contributors.
  • the source types relied upon may be limited to observed commercial production data.
  • statistical weight may be placed on or discounted from certain types of data during a portfolio analysis to agricultural production business.
  • the process includes some or all of the features of the embodiment discussed above, and further involves analysis wherein a higher significance is placed on information from the first entity and the other entities than other information.
  • the analysis may place a higher significance on information from source types including observational data than other source types.
  • greater weight may be placed on observed commercial production data more proximate to the location of the field-in-interest, while less weight is given to observed commercial production data taken at longer distances from the field-in interest.
  • the analysis is to be conducted without regard to an expected outcome, such as a favored product ranking high in any results.
  • Financial portfolio analyses may include developing a value of the portfolio- holder's risk tolerance, such as by asking the portfolio holder to subjectively report his or her own opinion of his risk tolerance, or by putting the portfolio-holder through a series of questions or tests to evaluate what his or her risk tolerance may be, or otherwise evaluating the portfolio-holder's likely risk tolerance.
  • While some embodiments of the present invention may allow a grower (the "first entity" described above) to input a risk-tolerance level at the outset, the inventors consider that self-reporting and other techniques for determining an individual's risk tolerance level are of suspect utility, given that growers tend to mischaracterize their own level of risk acceptance, at least in connection with agricultural production; a vast majority of growers will identify themselves as risk-averse, which would be expected in an industry that is known for its conservative business principles and unpredictable boom-and- bust cycles. Yet their very participation in such an unpredictable industry belies growers' self- judgment as risk-averse. Such situations present an obstacle to using traditional portfolio analysis principles, where the portfolio is created based on the risk acceptable to the owner.
  • the analysis disclosed herein might be conducted so as to avoid relying on or taking into account in the first instance an individual risk tolerance factor associated with the grower.
  • the grower may be asked to define a yield target for his or her particular commodity, and the process will analyze the optimal bundle and characterize the associated risk.
  • the system may then receive further inputs from the grower, such as selecting particular crop varieties to be used or avoided, or altering the yield goal, environmental characteristics, or management techniques, and the system will display a new crop variety bundle meeting the revised inputs and display the associated risk.
  • the analysis is conducted without including a value for the grower's subjective or detected risk in the calculations at all.
  • the results output from the analysis might identify a level of risk associated with the option or options so presented, rather than limiting the calculation of the options based on a presumed (but likely incomplete or incorrect) risk tolerance level associated with the grower, in this manner, the portfolio-holder may be allowed to select or accept an option, or seek alternate calculation of options, with knowledge of a risk indicator while avoiding the potential difficulties associated with self-reporting or otherwise divining a risk tolerance level.
  • the above would be particularly tailored to maintaining confidentiality and privacy of the information collected from growers, based on the maintenance of separate databases and populating forward into the master database of only information that is deemed not to be personal.
  • Such an embodiment might include placing access and security level control in the hands of the contributor, rather than the one that controls the master database (recognizing that in many cases at least some level of access or control of the feeder database by the one who controls the master database would be appropriate, at least for configuration and coordination purposes).
  • each circle 2 represents the average yield and risk associated with obtaining that yield with a particular variety. Varieties that appear to the upper right 2a have high potential yields but also have correspondingly high associated risk. As shown, the boxes 4a and 4b represent the portfolio of varieties actually chosen by each of two growers, Grower 1 (depicted by box 4a) and Grower 2 (depicted by box 4b).
  • the curve 8 to the upper left shows the efficient frontier drawn with respect to 7500 possible combinations of soybean varieties based on yield goal and risk preference.
  • the efficient frontier provides the least risky combinations to achieve a particular yield rate. Making decisions in light of the curve would allow Growers 1 and 2 to manage their risk while maximizing potential return without undertaking undesired risk levels.
  • Figure 5 displays grower 2's yield rate 4b plotted against the risk associated with the selected soybean varieties 2 and the percentage of total crop acreage planted in each variety 3. While Grower 2 obtained a similar yield per acre to grower 1 as shown in Figure 4, grower 2 relied on relatively riskier crop varieties, for which minute changes in environmental factors or management techniques may dramatically decrease (or increase) the yield the next year. Grower 2 may be satisfied with this risk level, or he may opt to find a mix of crop varieties that produce a similar yield result with more predictability. Grower 2 may select predictable yields at lower risk by choosing a combination of products that approach the efficient frontier 8.
  • Data may be indexed against its source to identify whether the source is grower-originated, observed commercial production data (which presumably may have advantages in terms of being unbiased and derived from actual production scenarios), field trials (which may have advantages due to robust controls and observations, but which also may be suspect for those very same reasons), vendor data (which may include additional details regarding intended application and proposed best case expectations), or public sources such as university or government studies. Any type of data may be entered into the system.
  • contributors will have the option of validating data to ensure its accuracy prior to uploading to the master database. Furthermore, either before exporting to the master database or before performing a portfolio analysis, the system may scan the data and remove sets containing clearly inconsistent information (e.g., a non-existent crop variety) or outlier data that is clearly unlikely (e.g., a yield rate that is 300% greater than the next highest yield rate).
  • clearly inconsistent information e.g., a non-existent crop variety
  • outlier data e.g., a yield rate that is 300% greater than the next highest yield rate
  • the method may give greater weight when actually running portfolio analysis calculations to (1) particular sources of data (e.g., observed commercial production data weighted more significantly than vendor "optimal yield” data); (2) the proximity of observed commercial production data to the field-of-interest (on the theory that fields adjacent to or near the field-of-interest will share more environmental characteristics than fields of greater distance from the field-of-interest); or (3) data sets relating to crop seasons that had a "typical” or average yield rather than crop seasons for which yield was abnormally high or abnormally low.
  • One advantage of this methodology is that it allows data to "mature” in the system as additional data of the same type or of context or other characterization is collected.
  • the original data will not change, but that original data at one point in time may be considered too sparse or otherwise subject to bias for inclusion in a calculation, yet after additional data is collected the original data may be desirous for inclusion.
  • the database when the database is first populated, it may have data provided by only a single vendor. In order to avoid potential bias, that single-vendor provided data may be excluded from calculations u ntil a population of vendors have input data in number deemed sufficient to offset any bias.
  • the method can also be applied to other sourcing factors, or to data characteristics other than source. For example, when a database in a particular cou nty is originally populated, it may have data contributed by only one or a few growers.
  • the system may be configured to refuse running a calculation or report based on a geographical limitation (such as the county) until the population of grower-sources in the database is deemed sufficient to obfuscate the particular source to which any given data can be attributed .
  • a geographical limitation such as the county
  • Such consultants and retailers often may benefit from analysis of their clientele growers' actual inputs (e.g., varieties planted, fertilizers, soil amendments, water, herbicides, defoliants, nutrients, other items "input” to the soil or otherwise "input” to the agricultural production efforts), alternatives, and management techniques (such as tillage, row spacing, etc.)) in helping them to best advise the grower.
  • clientele growers' actual inputs e.g., varieties planted, fertilizers, soil amendments, water, herbicides, defoliants, nutrients, other items "input” to the soil or otherwise “input” to the agricultural production efforts
  • alternatives e.g., etc.
  • management techniques such as tillage, row spacing, etc.
  • the information is populated into only the database(s) most closely associated with the grower.
  • the personal information is populated directly into the database that is accessible to the contributor who originally inputs the data, whether that contributor is the grower, a consultant, etc.
  • This database called a feeder database for simplicity, is accessible (at least in part) to the contributor so that the contributor can view and perform its own analysis with data contained therein.
  • the feeder database would have data related to multiple fields, acres, and/or growers.
  • the contributor may also have access to data from other entities that themselves could be, or in the future might be, contributors.
  • a trusted advisor who contributes information from various growers that use the advisor's services may also work with crop consultants.
  • crop consultants would also represent various growers, and may have information that can be populated into the same or a different feeder database, depending on the size of the consultant's grower clientele.
  • the advisor as a contributor may have access to data source types other than the growers' data, such as trials and even third-party-sourced data like government, university, or privately-held data sets.
  • the entity that contributes this information to the feeder database is considered the contributor, even if that contributor relies on underlying sources.
  • it is considered that the contributor either generates the data, or has some relationship of trust with the underlying source.
  • a contributor may be the grower himself, or the contributor may be a crop consultant, sales representative of a retailer, or other agent of the grower.
  • a person may request an analysis to be performed by the system. Again, this requestor may be the grower himself, or a crop consultant, retailer sales representative, or other agent.
  • a seed vendor may contribute information related to the first entity and may request a production analysis, particularly in scenarios in which the vendor is conducting trials for seed and variety testing and evaluation and has contracted with the grower to assist in those trials.
  • the personal information is available to the contributor, and the contributor may have need to reference that personal information in connection with fulfilling its role.
  • the contributor or the sources may not want to extend the trust so far as to place the personal data in a database that is not closely associated with the contributor, in this embodiment, the feeder database associated with each contributor is connected to a master database that spans information contributed by multiple contributors.
  • the master database is linked to the feeder databases in such a way that information populated into a feeder database is uploaded into the master database.
  • some of the fields in the feeder database are not populated forward to the master database, or are forwarded only with modification. For example, the fields containing personal information may be excluded from any data forwarding, such that they do not appear in the master database at all.
  • data in the fields containing such information may be replaced by indexing values, aliases, or other data that is not considered personal.
  • indexing values e.g., a grower's name
  • aliases e.g., a grower's name
  • any equivalent field may have a numeric identifier instead.
  • Another feature of the described methodology is the attention to avoiding disclosure of analytic results that may lead to ready identification of a grower, where such identification is not required. For example, if it is known that only one grower in a particular county or zip code grows cherimoya, a report run by a different grower on cherimoya productivity and inputs in that county or zip code would naturally reveal the identity of the sole cherimoya grower. To deal with this, some embodiments will include within the method a step of checking the scope of the request against the number of growers taken into account in the analysis. Where the number of growers to be considered is below a predetermined threshold, the method would either demand a wider scope of inquiry, or would itself broaden the factors under consideration until the number of growers is appropriate. Alternately, the analysis might simply be refused.
  • record may be made of management techniques (e.g., whether external irrigation is or may be applied), again for purposes of comparing the environmental differences of growers.
  • the analysis takes into account similarity of growers. These growers would form a custom cohort based on the selected similarities to be analyzed. Thus, a query from one grower would then be answered by analyzing information derived from growers in the same cohort. It should be noted that the grouping into cohorts need not occur before the time of analysis of a request from a grower. Rather, because the master database is constantly evolving with new information, the master database may be evaluated at the time of each request to identify the relevant paradigms and relevant cohorts.
  • the described method also in many embodiments defeats bias by providing that the data populated into the master database is from as many source types as is possible.
  • the source type believed to be most reliable is historical information derived from observational data of inputs, environmental characteristics, management techniques, and harvest information. Ideally, this observational data will comprise a substantial portion of the information in the master database.
  • Other source types should also be represented, where appropriate, including vendor-provided data, information from field trials, information from university studies, and information from government studies and databases.
  • a preferred embodiment would involve data from all of these source types, or at least a plurality of source types, in the master database. Where data from a plurality of source types is present, at least one of those source types should be observational data.
  • the presence of observational data in the database is ensured by providing that requests for analysis must be associated with an entity that actually has observational data within a feeder database.
  • This both incentivizes increased submission of data to the system for analysis, improving the reliability of results, and acts as a guard against a free-riding system in which observational data may be lacking.
  • Using such a methodology means that the more the system is used, the more reliable it becomes.
  • the community of data and users becomes larger more fields, varieties, and overall number of observations
  • multiyear on-farm data can be used to populate predictive models to help farmers mitigate risks and improve profitability.
  • crop year commodity, soil type, soil texture, crop variety or hybrid
  • planting date row configuration, plant population, row spacing, irrigation type, field boundaries, seed treatments, tillage practice, crop protection, plant canopy temperature, atmospheric temperature, rainfall, pesticide applications, fungicide applications, and/or fertilizer applications.
  • the grower's data is analyzed against the cohort to produce an optimal bundle of crop varieties, as described first in a general manner, and then more specifically below.
  • the data is analyzed to determine the means and covariances.
  • Means for the cohort are calculated in the traditional way, that is, the averages are calculated or estimated in the statistical sense.
  • the covariances are calculated for the entire system (or subset of data)as well as for each pair of variables specified in the cohort regime.
  • the means and covariances are then used to determine the bundle of products that minimizes covariance risk for a given level of output (e.g. yield, returns, profits, etc.).
  • a yield goal can be specified by the user for the analytics in this step to optimize a bundle of crop varieties to meet this yield goal with the minimal amount of covariance risk.
  • An extension of the yield goal option is to evaluate the optimal bundle of crop varieties for a sequence of output levels that form the "efficient frontier.”
  • an analysis using mathematical optimization techniques calculates the optimal bundle of crop varieties.
  • a database is populated with information concerning various crop varieties, yield rates, and explanatory variables such as environmental characteristics and management techniques, as further described above.
  • inputs are received from the user, such as a designated field or soil type, crop type, intended yield goal, and other information as described below.
  • a cohort is created for use in the analysis.
  • the composition of X includes the variable-of-interest, such as the crop variety or management decision that the user would select, plus all the explanatory variables which comprise other input use (e.g., fertilizer, fungicides, insecticides, irrigation), cultural practices (e.g., tillage, planting date), and environmental factors (e.g., soils, weather).
  • explanatory variables which comprise other input use
  • cultural practices e.g., tillage, planting date
  • environmental factors e.g., soils, weather.
  • soils may be removed from or included in the list of explanatory variables.
  • Other variables such as presence of fungicide applied, planting date, cumulative rainfall in specific time intervals, etc., may be added.
  • the particular specification of the regression model (that is, the composition of the X matrix and ⁇ vector) is dynamic and potentially differs for each user-initiated run of the risk analysis tool.
  • the calculated mean and covariances are then entered into an optimization analysis that determines the bundle of products that minimize risk for a given level of output, such as a stated yield goal.
  • the user's basic characteristics are populated into the analytics based on a priori information derived from field information (e.g., soils, irrigation types, cropping systems, etc.) while further characteristics and management scenarios are populated by user's input during interaction with user-interface, as described above.
  • field information e.g., soils, irrigation types, cropping systems, etc.
  • further characteristics and management scenarios are populated by user's input during interaction with user-interface, as described above.
  • a multitude of optimization techniques can be used such as linear programming, quadratic programming, mixed integer programming and so forth, without departing from the scope of this invention.
  • the optimization analysis may take the form of identifying the Sharpe ratio of the resulting design curve when assuming that the risk free rate of return (or risk free asset) is zero, and then determining the correlating yield and risk for that Sharpe
  • the optimization analysis may first produce an "unconstrained" run, which includes all the data for the subset of the master database with no constraints (or only nominal constraints, such as the type of crop and the field being planted) being imposed by the user.
  • constraints such as deselecting available products or vendors, specifying a maximum number of products to apply, a yield goal, etc.
  • the result of this unconstrained run is displayed 206 to the user.
  • the display to the user may show the efficient frontier 8 with an expected yield 10 and variance 12.
  • the type and amount of crop varieties 16 may also be displayed.
  • a risk tolerance indicator 14 shows the user the level of risk associated with meeting the originally inputted yield amount.
  • Other information (such as useful crop management technique or environmental characteristics needed to attain the yield amount, recommended substitute crop varieties, second- or third-best mixes, or other information a grower may find useful for planting) could also be displayed.
  • the user may perform additional model runs after viewing the unconstrained run output and, in 208, select or deselect a plurality of environmental characteristics, management techniques, crop constraints, or other input constraints.
  • a new run of the model is conducted 210 and displayed to the user 212, who may then further revise the input constraints.
  • Such user-specified characteristics are received from the user in the form of parameters that will define the matrix and vectors in the regression model as well as instruct the regression analytics how to interpret each run.
  • a minimum acreage "floor” may be provided, such that if a product is to be included in a portfolio produced for a user, the product will be used in an amount at least as large as the floor acreage.
  • This is a variation from traditional financial portfolio analysis, which allows a portfolio to include arbitrarily small investments in particular stocks, bonds, securities and the like. Rather, in agriculture, it is preferred that a manageable amount of a particular product (or a manageable area sown with the seed of that product) be used. Accordingly, a floor is included to provide a cut-off amount, below which additional products will not be included in the portfolio. In some embodiments, this floor is set at 40 acres.
  • Another agriculture-specific optimization tool in some embodiments includes the ability to spread seed allocations across a variety of maturity groups in order to reduce risk.
  • a grower typically has multiple fields and cannot sow all fields at the same time given limited equipment and other resources.
  • weather conditions may delay planting or harvesting. This contrasts with typical financial products, which may be bought and sold at any given time or simultaneously, and for which the only resource restriction is the amount of money available for making purchases.
  • growers must plant and harvest over a period of days and weeks, the risk-averse grower will allocate seed according to maturity groups, which are staged sets of seeds and crops planted at roughly the same time.
  • the portfolio risk analysis will distribute selected products or cultivars across maturity groups to provide the optimal risk allocation.
  • the method and analysis herein can also be applied to benchmarking, giving a grower (or a consultant or other entity working with a grower) the ability to see how his or her agricultural production compares with others generally or with those that are similarly situated.
  • This in turn, can be used as an educational tool, or as a trailing indicator in order to improve future decisions.
  • Data can also be used to benchmark farmers within peer groups to help improve efficiency and profitability. Such benchmarking capabilities are depicted in Figures 8A and 8B.
  • Historical and current data are therefore used to populate predictive models, to assist growers and others in the agricultural production industries.
  • feeder database 30 having data related to or identifying some or all of the following information: (a) observational data, which may include grower personal information; (field location, size, and environmental characteristics (e.g., soil regime, climate conditions, weather conditions, and the like); management techniques (e.g., tillage, irrigation type and amount, fertilizers applied, insecticides applied, and the like); plant variety and yield data; and plant status characteristics (e.g., planting dates, canopy temperatures, insect infestations, and the like); (b) vendor or retailer sales and research information, and results of field trials; and (c) publicly available information, such results of university field trials and government published reports and databases.
  • Feeder databases 30 may be associated with particular retailers, crop consultants, vendors, publicly available data, and other source types.
  • the feeder databases 30 are linked with a master database 36 in such a manner that a subset of information is periodically exported from the feeder databases into the master database.
  • This subset may include the various types of information included in the feeder databases, but it should not include personal information, which either is not exported into the master database or is replaced by indexing values, aliases, or other information not considered personal. In this manner, the confidentiality concerns discussed with respect to the processes above are addressed by the system.
  • the exported data populates the master database.
  • the system further includes a software application or other data entry tool 32 to input data and information into at least one feeder database 30.
  • Data may be entered, for example, by importing databases associated with retailers, crop consultants, vendors, growers, universities, research institutions, or the government. Additionally, a contributor may directly enter information via a computer, cell phone, smart phone, tablet computer, or other device linkable via a telecommunications network to the feeder database. Also, data-gathering devices 38 in the field that measure status characteristics (such as rainfall amounts, atmospheric temperature, canopy temperature, and the like) may periodically and automatically upload information into the feeder database.
  • the feeder databases 30 are accessed through the software interface 32 allowing a particular user (such as a retailer 34a, crop consultant 34b, landowner 34c, grower 34d, or other data contributor 34e) to enter and/or update information provided by the user to a particular feeder database 30.
  • a processor 40 is connected to and receives data from the master database for the purpose of conducting the regression analysis to produce reports and analyses concerning historical trends, comparative analysis, and predictive growth models.
  • a monitor, smartphone, cell phone, tablet PC, or other display device 42 is provided for communicating to the requestor the reports and analyses conducted by the processor.
  • the display device 42 may be integrated into the software application or data entry tool 32, or it may be viewable independently from the data entry site.
  • a grower 34d may be able to enter data through the software application 32, but the results of the analysis performed by the processor 40 may only be displayed on a display device 42 accesible by the grower's crop consultant 34b.
  • any given elements of the disclosed embodiments of the invention may be embodied in a single structure, a single step, a single substance, or the like.
  • a given element of the disclosed embodiment may be embodied in multiple structures, steps, substances, or the like.

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Abstract

L'invention concerne un système et un procédé conçus pour mettre en application une analyse des risques dans le but d'identifier des variétés de cultures optimales à utiliser dans un champ précis et avec des caractéristiques environnementales précises et des techniques précises quant aux sols. Ledit procédé adapte des procédés d'analyse de portefeuille financier afin de les utiliser dans le cadre de la production agricole et prendre en compte la variabilité du sol, de l'environnement et de la gestion des cultures. Un système permettant d'utiliser ce procédé est également décrit.
PCT/US2013/049979 2012-07-10 2013-07-10 Système et procédé pour la gestion des risques agricoles WO2014011810A1 (fr)

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US201261670103P 2012-07-10 2012-07-10
US61/670,103 2012-07-10
US13/833,792 2013-03-15
US13/833,792 US20140278731A1 (en) 2013-03-15 2013-03-15 System and Method for Agricultural Risk Management

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