BACKGROUND

Weather index insurance promises financial resilience to farmers struck by harsh weather conditions with swift compensation at an affordable premium as well as minimal moral hazard. Despite these advantages, traditional weather index insurance does not adequately address sustainable farming practices. For example, if a farmer needs to determine a production strategy given a particular irrigation restriction, the farmer is likely to choose to grow one crop and irrigate that crop at the restricted level of irrigation. However, such a monoculture crop may not provide the farmer with the best yield or net profit floor given the irrigation restriction and may not amount to sustainable production practices given the growing problems caused by resource restrictions/constraints and increasing climate uncertainty. Thus, the ability to objectively determine an optimal combination of production and/or financial strategies for a particular location and given a particular set of restrictions is a technical problem in the field of agriculture.

Thus, there is a need in the art for methods, apparatuses, systems, computing devices, and/or the like for completing a market in a manner that incentivizes sustainable production (e.g., farming) practices.

BRIEF SUMMARY

In general, embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for providing market completing combinations of production strategies (e.g., combinations of cropping strategies) and/or financial strategies (e.g., insurance policies). As used herein, the term "market completing" is used to mean that the combination(s) of production strategies and/or financial strategies provide for the achievement of desired outcomes in all specified states of the world. For example, an example embodiment provides for the generation of a market completing insurance policy pay-out table. In an example embodiment, an insurance policy that provides full insurance at a specified level of residual risk is determined for a particular production strategy (e.g., cropping strategy or a combination/set of cropping strategies). In an example embodiment, an insurance policy that provides full insurance at a specified level of residual risk is determined for an optimal production (e.g., cropping) and/or financial (e.g., insurance) strategy. In an example embodiment, an application is provided that is configured and/or programmed to provide a user with a personalized optimal and/or suggested combination of production strategies (e.g., cropping strategy and/or combination of cropping strategies) and/or financial strategies (e.g., insurance policy tailored to the optimal production strategies) via an improved interactive user interface.

According to one embodiment of the present invention, a method for completing a market comprising production and financial strategies is provided. In an example embodiment, the method comprises defining a set of production strategies (e.g., a plurality of cropping strategies and/or combinations of cropping strategies) by a computing entity comprising a processor and a memory storing parameters of one or more crop models; defining a set of specified states by the computing entity; and determining at least one estimate. The at least one estimate is one of a crop estimate, crop value estimate, crop yield estimate, or crop unit price estimate for each production strategy of the set of production strategies for each state of the set of specified states by the computing entity and based at least in part on the parameters of the one or more crop models. In an example embodiment, the method further comprises generating by the computing entity a state- dependent matrix based at least in part on determined estimates; determining by the computing entity an optimal allocation vector based at least in part on the inverse of the state-dependent matrix; and generating by the computing entity a market completing policy pay-out table based on the state-dependent matrix and optimal allocation vector.

According to another embodiment of the present invention, an apparatus is provided. In an example embodiment, the apparatus comprises at least one processor and at least one memory storing computer program code and parameters of one or more crop models. The at least one memory and the computer program code are configured to, with the processor, cause the apparatus to at least define a set of production strategies (e.g., a plurality of cropping strategies and/or combinations of cropping strategies); define a set of specified states; and determine at least one estimate. The at least one estimate is one of a crop estimate, crop value estimate, crop yield estimate, or crop unit price estimate for each production strategy of the set of production strategies for each state of the set of specified states based at least in part on the parameters of the one or more crop models and/or one or more financial strategies. In an example embodiment, the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to at least generate a state-dependent matrix based at least in part on determined estimates; determine an optimal allocation vector based at least in part on the inverse of the state- dependent matrix; and generate a market completing policy pay-out table based on the state-dependent matrix and optimal allocation vector.

According to yet another embodiment of the present invention, a computer program product is provided. In an example embodiment, the computer program product comprises at least one non-transitory computer-readable storage medium having computer-executable program code instructions stored therein. The computer-executable program code instructions comprise program code instructions configured to, when executed by a processor of a computing entity, cause the computing entity to define a set of production strategies (e.g., a plurality of cropping strategies and/or combinations of cropping strategies); define a set of specified states; and determine at least one estimate. The at least one estimate is one of a crop estimate, crop value estimate, crop yield estimate, or crop unit price estimate for each production strategy of the set of production strategies for each state of the set of specified states based at least in part on parameters of the one or more crop models. The parameters of the one or more crop models are stored by at least one of the non- transitory computer-readable storage medium or a memory of the computing entity. In an example embodiment, the computer-executable program code instructions further comprise program code instructions configured to, when executed by a processor of a computing entity, cause the computing entity to generate a state-dependent matrix based at least in part on determined estimates; determine an optimal allocation vector based at least in part on the inverse of the state-dependent matrix; and generate a market completing policy pay-out table based on the state-dependent matrix and optimal allocation vector.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S) Having thus described the invention in general terms, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:

Fig. 1 is an overview of a system that can be used to practice embodiments of the present invention.

Fig. 2 is an exemplary schematic diagram of an analysis computing entity according to one embodiment of the present invention.

Fig. 3 is an exemplary schematic diagram of a user computing entity according to one embodiment of the present invention.

Fig. 4 provides a flowchart illustrating operations and processes that can be used in accordance with various embodiments of the present invention. Fig. 5 is a plot showing an example of defining a set of states.

Fig. 6 illustrates some example components used to determine, generate, and/or the like a market completing insurance policy, in accordance with various embodiments of the present invention.

Fig. 7 illustrates collapsing a state-dependent matrix based on an optimal allocation vector, in accordance with various embodiments of the present invention.

Fig. 8 illustrates an example Pareto analysis of various cropping strategies, in accordance with various embodiments of the present invention.

Figs. 8A, 8B, and 8C illustrate a 3D Pareto analysis and projections of the 3D Pareto analysis into two different planes.

Fig. 9 illustrates an example view of a crop planning screen, in accordance with various embodiments of the present invention.

Fig. 10 illustrates an example view of a production strategy screen, in accordance with various embodiments of the present invention.

DETAILED DESCRIPTION

Various embodiments of the present invention now will be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the inventions are shown. Indeed, these inventions may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. The term "or" is used herein in both the alternative and conjunctive sense, unless otherwise indicated. The terms "illustrative" and "exemplary" are used to be examples with no indication of quality level. Like numbers refer to like elements throughout.

Example embodiments of the present invention relate to generating market completing production and/or financial strategies. For example, a production strategy may comprise an optimal allocation for a set and/or combination of cropping strategies. Each cropping strategy identifies a crop and, in an example embodiment, at least one of (a) an irrigation strategy, (b) a fertilization strategy, (c) a tillage strategy, or (d) a pesticide strategy for the crop. As used herein, a crop may be a produce crop (e.g., any cultivated plant, fungus, or alga that is harvested for food, clothing, livestock fodder, biofuel, medicine, or other uses, including but not limited to wheat, corn, potatoes, soybeans, sugarcane, etc.), livestock, timber (e.g., for lumber or pulp), a mineral crop (e.g., produced through mining), environmental performance (e.g., pollutants and/or greenhouse gas emissions such as, for example, CO _x and/or NO _x , soil erosion, nutrient leaching, salinity control), and/or other crops. Example embodiments are described herein using an agricultural market and produce crops as an example. However, it should be apparent that example embodiments of the present invention may be applicable to other markets and crops.

I. Computer Program Products, Methods, and Computing Entities

Embodiments of the present invention may be implemented in various ways, including as computer program products that comprise articles of manufacture. A computer program product may include a non-transitory computer-readable storage medium storing applications, programs, program modules, scripts, source code, program code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like (also referred to herein as executable instructions, instructions for execution, computer program products, program code, and/or similar terms used herein interchangeably). Such non-transitory computer-readable storage media include all computer-readable media (including volatile and non- volatile media).

In one embodiment, a non- volatile computer-readable storage medium may include a floppy disk, flexible disk, hard disk, solid-state storage (SSS) (e.g., a solid state drive (SSD), solid state card (SSC), solid state module (SSM), enterprise flash drive, magnetic tape, or any other non-transitory magnetic medium, and/or the like. A non-volatile computer-readable storage medium may also include a punch card, paper tape, optical mark sheet (or any other physical medium with patterns of holes or other optically recognizable indicia), compact disc read only memory (CD-ROM), compact disc- rewritable (CD-RW), digital versatile disc (DVD), Blu-ray disc (BD), any other non- transitory optical medium, and/or the like. Such a non- volatile computer-readable storage medium may also include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory (e.g., Serial, NAND, NOR, and/or the like), multimedia memory cards (MMC), secure digital (SD) memory cards, SmartMedia cards, CompactFlash (CF) cards, Memory Sticks, and/or the like. Further, a non-volatile computer-readable storage medium may also include conductive-bridging random access memory (CBRAM), phase-change random access memory (PRAM), ferroelectric random-access memory (FeRAM), non-volatile random-access memory (NVRAM), magnetoresistive random-access memory (MRAM), resistive random-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory (SONOS), floating junction gate random access memory (FJG RAM), Millipede memory, racetrack memory, and/or the like.

In one embodiment, a volatile computer-readable storage medium may include random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), fast page mode dynamic random access memory (FPM DRAM), extended data-out dynamic random access memory (EDO DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), double data rate type two synchronous dynamic random access memory (DDR2 SDRAM), double data rate type three synchronous dynamic random access memory (DDR3 SDRAM), Rambus dynamic random access memory (RDRAM), Twin Transistor RAM (TTRAM), Thyristor RAM (T-RAM), Zero-capacitor (Z-RAM), Rambus in-line memory module (RIMM), dual in-line memory module (DIMM), single in-line memory module (SIMM), video random access memory (VRAM), cache memory (including various levels), flash memory, register memory, and/or the like. It will be appreciated that where embodiments are described to use a computer-readable storage medium, other types of computer-readable storage media may be substituted for or used in addition to the computer-readable storage media described above.

As should be appreciated, various embodiments of the present invention may also be implemented as methods, apparatus, systems, computing devices, computing entities, and/or the like. As such, embodiments of the present invention may take the form of an apparatus, system, computing device, computing entity, and/or the like executing instructions stored on a computer-readable storage medium to perform certain steps or operations. Thus, embodiments of the present invention may also take the form of an entirely hardware embodiment, an entirely computer program product embodiment, and/or an embodiment that comprises combination of computer program products and hardware performing certain steps or operations.

Embodiments of the present invention are described below with reference to block diagrams and flowchart illustrations. Thus, it should be understood that each block of the block diagrams and flowchart illustrations may be implemented in the form of a computer program product, an entirely hardware embodiment, a combination of hardware and computer program products, and/or apparatus, systems, computing devices, computing entities, and/or the like carrying out instructions, operations, steps, and similar words used interchangeably (e.g., the executable instructions, instructions for execution, program code, and/or the like) on a computer-readable storage medium for execution. For example, retrieval, loading, and execution of code may be performed sequentially such that one instruction is retrieved, loaded, and executed at a time. In some exemplary embodiments, retrieval, loading, and/or execution may be performed in parallel such that multiple instructions are retrieved, loaded, and/or executed together. Thus, such embodiments can produce specifically-configured machines performing the steps or operations specified in the block diagrams and flowchart illustrations. Accordingly, the block diagrams and flowchart illustrations support various combinations of embodiments for performing the specified instructions, operations, or steps.

II. Exemplary System Architecture

Fig. 1 provides an illustration of an exemplary embodiment of the present invention. As shown in Fig. 1, this particular embodiment may include one or more analysis computing entities 10, one or more user computing entities 20, one or more sensors 30, one or more index information/data computing entities 40, one or more networks 50, and/or the like. Each of these components, entities, devices, systems, and similar words used herein interchangeably may be in direct or indirect communication with, for example, one another over the same or different wired or wireless networks. Additionally, while Fig. 1 illustrates the various system entities as separate, standalone entities, the various embodiments are not limited to this particular architecture.

1. Exemplary Analysis Computing Entity

Fig. 2 provides a schematic of an analysis computing entity 10 according to one embodiment of the present invention. In example embodiments, an analysis computing entity 10 may be operated by and/or behalf of a government or non-governmental organization or agency, an insurance provider, financial analyst, individual, and/or the like. In example embodiments, the analysis computing entity 10 may be configured to determine, calculate, compute, estimate, and/or the like a crop yield estimate, a crop value estimate, a premium for insuring a crop, a payout or settlement for damage to a crop, a payout table, generate a market completing insurance policy, and/or the like based on at least two indices. In example embodiments, the analysis computing entity 10 may be configured to analyze measurement information/data and/or index information/data to determine, compute, and/or the like two or more indices. In example embodiments, the analysis computing entity 10 may be configured to determine a look-up table for determining a premium for insuring a crop or a payout/settlement for a crop that is indexed by two or more indices.

In general, the terms computing entity, computer, entity, device, system, and/or similar words used herein interchangeably may refer to, for example, one or more computers, computing entities, desktops, mobile phones, tablets, phablets, notebooks, laptops, distributed systems, input terminals, servers or server networks, blades, gateways, switches, processing devices, processing entities, set-top boxes, relays, routers, network access points, base stations, the like, and/or any combination of devices or entities adapted to perform the functions, operations, and/or processes described herein. Such functions, operations, and/or processes may include, for example, transmitting, receiving, operating on, processing, displaying, storing, determining, creating/generating, monitoring, evaluating, comparing, and/or similar terms used herein interchangeably. In one embodiment, these functions, operations, and/or processes can be performed on data, content, information, and/or similar terms used herein interchangeably.

In one embodiment, the analysis computing entity 10 may also include one or more communications interfaces 120 for communicating with various computing entities, such as by communicating data, content, information, and/or similar terms used herein interchangeably that can be transmitted, received, operated on, processed, displayed, stored, and/or the like.

As shown in Fig. 2, in one embodiment, the analysis computing entity 10 may include or be in communication with one or more processing elements 105 (also referred to as processors, processing circuitry, and/or similar terms used herein interchangeably) that communicate with other elements within the analysis computing entity 10 via a bus, for example. As will be understood, the processing element 105 may be embodied in a number of different ways. For example, the processing element 105 may be embodied as one or more complex programmable logic devices (CPLDs), microprocessors, multi-core processors, co-processing entities, application-specific instruction-set processors (ASIPs), microcontrollers, and/or controllers. Further, the processing element 105 may be embodied as one or more other processing devices or circuitry. The term circuitry may refer to an entirely hardware embodiment or a combination of hardware and computer program products. Thus, the processing element 105 may be embodied as integrated circuits, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), hardware accelerators, other circuitry, and/or the like. As will therefore be understood, the processing element 105 may be configured for a particular use or configured to execute instructions stored in volatile or non- volatile media or otherwise accessible to the processing element 105. As such, whether configured by hardware or computer program products, or by a combination thereof, the processing element 105 may be capable of performing steps or operations according to embodiments of the present invention when configured accordingly.

In one embodiment, the analysis computing entity 10 may further include or be in communication with non-volatile media (also referred to as non- volatile storage, memory, memory storage, memory circuitry and/or similar terms used herein interchangeably). In one embodiment, the non-volatile storage or memory may include one or more nonvolatile storage or memory media 110, including but not limited to hard disks, ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM, Millipede memory, racetrack memory, and/or the like. As will be recognized, the non-volatile storage or memory media may store databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like. The term database, database instance, database management system, and/or similar terms used herein interchangeably may refer to a collection of records or data that is stored in a computer-readable storage medium using one or more database models, such as a hierarchical database model, network model, relational model, entity-relationship model, object model, document model, semantic model, graph model, and/or the like.

In one embodiment, the analysis computing entity 10 may further include or be in communication with volatile media (also referred to as volatile storage, memory, memory storage, memory circuitry and/or similar terms used herein interchangeably). In one embodiment, the volatile storage or memory may also include one or more volatile storage or memory media 115, including but not limited to RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM, cache memory, register memory, and/or the like. As will be recognized, the volatile storage or memory media may be used to store at least portions of the databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like being executed by, for example, the processing element 105. Thus, the databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like may be used to control certain aspects of the operation of the analysis computing entity 10 with the assistance of the processing element 105 and operating system.

As indicated, in one embodiment, the analysis computing entity 10 may also include one or more communications interfaces 120 for communicating with various computing entities, such as by communicating data, content, information, and/or similar terms used herein interchangeably that can be transmitted, received, operated on, processed, displayed, stored, and/or the like. Such communication may be executed using a wired data transmission protocol, such as fiber distributed data interface (FDDI), digital subscriber line (DSL), Ethernet, asynchronous transfer mode (ATM), frame relay, data over cable service interface specification (DOCSIS), or any other wired transmission protocol. Similarly, the analysis computing entity 10 may be configured to communicate via wireless external communication networks using any of a variety of protocols, such as general packet radio service (GPRS), Universal Mobile Telecommunications System (UMTS), Code Division Multiple Access 2000 (CDMA2000), CDMA2000 IX (lxRTT), Wideband Code Division Multiple Access (WCDMA), Global System for Mobile Communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), Time Division-Synchronous Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), Evolved Universal Terrestrial Radio Access Network (E-UTRAN), Evolution-Data Optimized (EVDO), High Speed Packet Access (HSPA), High-Speed Downlink Packet Access (HSDPA), IEEE 802.11 (Wi-Fi), Wi-Fi Direct, 802.16 (WiMAX), ultra-wideband (UWB), infrared (IR) protocols, near field communication (NFC) protocols, Wibree, Bluetooth protocols, wireless universal serial bus (USB) protocols, and/or any other wireless protocol.

Although not shown, the analysis computing entity 10 may include or be in communication with one or more input elements, such as a keyboard input, a mouse input, a touch screen/display input, motion input, movement input, audio input, pointing device input, joystick input, keypad input, and/or the like. The analysis computing entity 10 may also include or be in communication with one or more output elements (not shown), such as audio output, video output, screen/display output, motion output, movement output, a printer for providing printed output, and/or the like. As will be appreciated, one or more of the analysis computing entity' s 10 components may be located remotely from other analysis computing entity 10 components, such as in a distributed system. Furthermore, one or more of the components may be combined and additional components performing functions described herein may be included in the analysis computing entity 10. Thus, the analysis computing entity 10 can be adapted to accommodate a variety of needs and circumstances. As will be recognized, these architectures and descriptions are provided for exemplary purposes only and are not limiting to the various embodiments. 2. Exemplary User Computing Entity

A user may be an individual, a family, a company, an organization, an entity, a department within an organization, a representative of an organization and/or person, and/or the like. For example, a user may be a farmer, representative of a farm, an officer of government or non-governmental organization or agency, and/or the like. A user may operate a user computing entity 20 that includes one or more components that are functionally similar to those of the analysis computing entity 10. Fig. 3 provides an illustrative schematic representative of a user computing entity 20 that can be used in conjunction with embodiments of the present invention. In general, the terms device, system, computing entity, entity, and/or similar words used herein interchangeably may refer to, for example, one or more computers, computing entities, desktops, mobile phones, tablets, phablets, notebooks, laptops, distributed systems, gaming consoles (e.g., Xbox, Play Station, Wii), watches, glasses, key fobs, RFID tags, ear pieces, scanners, cameras, wristbands, kiosks, input terminals, servers or server networks, blades, gateways, switches, processing devices, processing entities, set-top boxes, relays, routers, network access points, base stations, the like, and/or any combination of devices or entities adapted to perform the functions, operations, and/or processes described herein. As shown in Fig. 3, the user computing entity 20 can include an antenna 212, a transmitter 204 (e.g., radio), a receiver 206 (e.g., radio), and a processing element 208 (e.g., CPLDs, microprocessors, multi-core processors, co-processing entities, ASIPs, microcontrollers, and/or controllers) that provides signals to and receives signals from the transmitter 204 and receiver 206, respectively.

The signals provided to and received from the transmitter 204 and the receiver 206, respectively, may include signaling information/data in accordance with air interface standards of applicable wireless systems. In this regard, the user computing entity 20 may be capable of operating with one or more air interface standards, communication protocols, modulation types, and access types. More particularly, the user computing entity 20 may operate in accordance with any of a number of wireless communication standards and protocols, such as those described above with regard to the analysis computing entity 10. In a particular embodiment, the user computing entity 20 may operate in accordance with multiple wireless communication standards and protocols, such as UMTS, CDMA2000, lxRTT, WCDMA, GSM< EDGE, TD-SCDMA, LTE, E- UTRAN, EVDO, HSPA, HSDPA, Wi-Fi, Wi-Fi Direct, WiMAX, UWB, IR, NFC, Bluetooth, USB, Bluetooth low energy, ZigBee, near field communication, infrared, ultra- wideband, and/or the like. Similarly, the user computing entity 20 may operate in accordance with multiple wired communication standards and protocols, such as those described above with regard to the analysis computing entity 10 via a network interface 220.

Via these communication standards and protocols, the user computing entity 20 can communicate with various other entities using concepts such as Unstructured Supplementary Service Data (USSD), Short Message Service (SMS), Multimedia Messaging Service (MMS), Dual-Tone Multi-Frequency Signaling (DTMF), and/or Subscriber Identity Module Dialer (SIM dialer). The user computing entity 20 can also download changes, add-ons, and updates, for instance, to its firmware, software (e.g., including executable instructions, applications, program modules), and operating system.

According to one embodiment, the user computing entity 20 may include location determining aspects, devices, modules, functionalities, and/or similar words used herein interchangeably. For example, the user computing entity 20 may include outdoor positioning aspects, such as a location module adapted to acquire, for example, latitude, longitude, altitude, geocode, course, direction, heading, speed, universal time (UTC), date, and/or various other information/data. In one embodiment, the location module can acquire data, sometimes known as ephemeris data, by identifying the number of satellites in view and the relative positions of those satellites (e.g., using global positioning systems (GPS)). The satellites may be a variety of different satellites, including Low Earth Orbit (LEO) satellite systems, Department of Defense (DOD) satellite systems, the European Union Galileo positioning systems, the Chinese Compass navigation systems, Indian Regional Navigational satellite systems, and/or the like. This data can be collected using a variety of coordinate systems, such as the Decimal Degrees (DD); Degrees, Minutes, Seconds (DMS); Universal Transverse Mercator (UTM); Universal Polar Stereographic (UPS) coordinate systems; and/or the like. Alternatively, the location information/data can be determined by triangulating the user computing entity's 20 position in connection with a variety of other systems, including wireless towers, Wi-Fi access points, and/or the like. Similarly, the user computing entity 20 may include indoor positioning aspects, such as a location module adapted to acquire, for example, latitude, longitude, altitude, geocode, course, direction, heading, speed, time, date, and/or various other information/data. Some of the indoor systems may use various position or location technologies including RFID tags, indoor beacons or transmitters, Wi-Fi access points, wireless towers, nearby computing devices (e.g., smartphones, laptops) and/or the like. For instance, such technologies may include the iBeacons, Gimbal proximity beacons, Bluetooth Low Energy (BLE) transmitters, NFC transmitters, infrared transmitters, ZigBee transmitters, ultra-wideband transmitters, and/or the like. These indoor positioning aspects can be used in a variety of settings to determine the location of someone or something to within inches or centimeters.

The user computing entity 20 may also comprise a user interface (that can include a display 216 coupled to a processing element 208) and/or a user input interface (coupled to a processing element 208). For example, the user interface may be a user application, browser, user interface, and/or similar words used herein interchangeably executing on and/or accessible via the user computing entity 20 to interact with and/or cause display of information/data from the analysis computing entity 10, as described herein. The user input interface can comprise any of a number of devices or interfaces allowing the user computing entity 20 to receive data, such as a keypad 218 (hard or soft), a touch display, voice/speech or motion interfaces, or other input device. In embodiments including a keypad 218, the keypad 218 can include (or cause display of) the conventional numeric (0- 9) and related keys (#, *), and other keys used for operating the user computing entity 20 and may include a full set of alphabetic keys or set of keys that may be activated to provide a full set of alphanumeric keys. In addition to providing input, the user input interface can be used, for example, to activate or deactivate certain functions, such as screen savers and/or sleep modes.

The user computing entity 20 can also include volatile storage or memory 222 and/or non- volatile storage or memory 224, which can be embedded and/or may be removable. For example, the non-volatile memory may be ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM, Millipede memory, racetrack memory, and/or the like. The volatile memory may be RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM, cache memory, register memory, and/or the like. The volatile and non-volatile storage or memory can store databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like to implement the functions of the user computing entity 20. As indicated, this may include a user application that is resident on the entity or accessible through a browser or other user interface for communicating with the analysis computing entity 10 and/or various other computing entities.

In another embodiment, the user computing entity 20 may include one or more components or functionality that are the same or similar to those of the analysis computing entity 10, as described in greater detail above. As will be recognized, these architectures and descriptions are provided for exemplary purposes only and are not limiting to the various embodiments.

3. Exemplary Sensor

In various embodiments, one or more sensors 30 may be configured to measure and/or collect measurement information/data that may be used to determine index information/data. In example embodiments, the one or more sensors 30 may be configured to measure and/or collect measurement information/data relating to weather and/or environmental conditions. For example, in one embodiment, the one or more sensors 30 may comprise one or more rain gauges, thermometers, light meters, rain gauges, barometers, hygrometers, soil moisture content meters, instruments for measuring wind speed and/or direction, instruments for measuring rainfall intensity, and/or the like. In one embodiment, the one or more sensors 30 may be configured to provide information regarding the intensity and frequency of rainfall at a particular location.

In example embodiments, the measurement information/data measured and/or collected by the one or more sensors 30 may be associated with a sensor location corresponding to the location where the measurement information/data was measured and/or collected. For example embodiments, the one or more sensors 30 may be in communication with and/or comprise a GPS sensor and/or other location determining aspects, devices, modules, functionalities, and/or similar words used herein interchangeably, as described above. The one or more sensors 30 may be configured to provide the location of the one or more sensors 30, as determined by the GPS sensor and/or other location determining aspect, when the one or more sensors 30 provide the measurement information/data. In example embodiments, the one or more sensors 30 may be stationary and may be configured to provide a known location when the measurement information/data is provided without the use of a GPS sensor and/or other location determining aspect. In some embodiments, the one or more sensors 30 may provide a sensor identifier configured to uniquely identify the sensor when the one or more sensors 30 provide the measurement information/data. The sensor identifier may then be used to determine a known location of the sensor identified thereby. In another embodiment, the one or more sensors 30 may provide the measurement information/data via a display and/or the like such that a user may enter the measurement information/data through a user interface of the user computing entity 20. The user interface may comprise a field for the user to provide information/data identifying the location of the one or more sensors 30 which measured and/or collected the measurement information/data being entered and/or the user computing entity 20 access the location determining embodiment of the user computing entity 20 to determine a current physical location of the user computing entity 20 and tag the measurement information/data with the current physical location of the user computing entity 20.

As indicated above, the one or more sensors 30 may be configured to provide measurement information/data. For example, the one or more sensors may be configured to provide measurement information/data to an analysis computing entity 10, a user computing entity 20, an index information/data computing entity 40, and/or the like. In another example, the one or more sensors 30 may be configured to provide a visual and/or audio indication (e.g., via a display device, speakers, and/or the like) of the measurement information/data and a user may enter the measurement information/data into an interface of, for example, the user computing entity 20. In example embodiments, the one or more sensors 30 may be configured to provide the measurement information/data through one or more wired or wireless networks 50. For example, such communication may be executed using a wired data transmission protocol, such as FDDI, DSL, Ethernet, ATM, frame relay, DOCSIS, or any other wired transmission protocol. Similarly, at least one of the one or more sensors 30 may be configured to communicate via wireless external communication networks using any of a variety of protocols, such as GPRS, UMTS, CDMA2000, lxRTT, WCDMA, GSM, EDGE, TD-SCDMA, LTE, E-UTRAN, EVDO, HSPA, HSDPA, Wi-Fi, Wi-Fi Direct, WiMAX, UWB, IR protocols, NFC protocols, Wibree, Bluetooth protocols, wireless USB protocols, and/or any other wireless protocol.

In example embodiments, the one or more sensors may be configured to measure and/or collect measurement information/data routinely, periodically, in response to one or more triggers, and/or the like. For example, the one or more sensors may be configured to measure and/or collect measurement information/data every fifteen minutes, every hour, every few hours, once a day, cumulatively throughout the day, and/or the like. In example embodiments, the one or more sensors 30 may be configured to provide the measurement information/data daily. However, the measurement information/data may be provided more frequently (e.g., every few hours) or less frequently (e.g., once a week), as appropriate for the application, data transmission requirements, amount of information/data to be transmitted, and/or the like.

In example embodiments, the one or more sensors 30 may be in communication with, operated by, and/or the like a sensor control unit. The sensor control unit may comprise a processor, memory, communication interface, user interface, and/or the like as described above with respect to the analysis computing entity 10 and/or the user computing entity 20. For example, the sensor control unit may be configured to cause at least one of the one or more sensors 30 to measure and/or collect measurement information/data; operate, communicate with, and/or comprise a location determining aspect; store measured and/or collected measurement information/data; cause display of measurement information/data; provide measurement information/data; and/or the like. In one embodiment, at least one sensor 30 is not in communication with, operated by, and/or the like a sensor control unit. For example, in one embodiment, the one or more sensors 30 comprises a rain gauge that consists of a rain receptacle for collecting rain and a scale that a user may use to determine how much rain was collected in the rain receptacle. The user may then enter the determined amount of rain into a user interface provided by the user computing entity 20.

4. Exemplary Index Information/Data Computing Entity

In various embodiments, the index information/data computing entity 40 may be configured to receive, store, and/or provide measurement information/data, information/data linking a sensor 30 to a corresponding location of the sensor 30 (e.g., based on the sensor identifier), index information/data, and/or other information/data that may be requested by any of a variety of computing entities. For example, the index information/data computing entity 40 may be configured to determine, compute and/or the like one or more crop models based on one or more indices; receive, store, determine, and/or provide one or more crop futures and/or crop value estimates, and/or the like. In example embodiments, the index information/data computing entity 40 may be operated by and/or on behalf of a government or non-governmental organization or agency, financial institution, insurance company, and/or the like.

In one embodiment, an index information/data computing entity 40 may include one or more components that are functionally similar to those of the analysis computing entity 10, user computing entity 20, and/or the like. For example, in one embodiment, each index information/data computing entity 40 may include one or more processing elements (e.g., CPLDs, microprocessors, multi-core processors, co-processing entities, ASIPs, microcontrollers, and/or controllers), one or more display device/input devices (e.g., including user interfaces), volatile and non- volatile storage or memory, and/or one or more communications interfaces. For example, the user interface may be a user application, browser, user interface, and/or similar words used herein interchangeably executing on and/or accessible via the index information/data computing entity 40 to interact with and/or cause display of information/data from various other entities.

III. Exemplary System Operation

Various example embodiments of the present invention provide a market completing insurance policy. Various example embodiments of the present invention provide an optimal allocation for production strategies (e.g., cropping strategy and/or combination of cropping strategies) and/or financial strategies (e.g., insurance coverage). For example, a market completing insurance policy may encourage and/or incentivize agricultural diversity, water use efficiency, water use reduction, irrigation development, sustainable agriculture, and/or the like while providing financial security for farmers participating in the agricultural market. Various embodiments provide an optimal allocation for a plurality of cropping strategies and/or insurance coverage given a water use restriction, and/or the like.

Here, diversified cropping systems (e.g., as provided by the optimal production strategy (e.g., optimal combination of cropping strategies)) include not only crop diversification, but also diverse irrigation levels and management practices. For example, even with the same crop, one can exploit nonlinearity of crop response to water availability to improve yield under water limitation by diversifying irrigation levels: with an irrigation restriction, irrigating all fields uniformly with the same constrained rate produces inferior yield to a combination of rain-fed yields from some fields and applying the available amount of irrigation at the inflection point rate in the crop response curve in the remaining fields. (The inflection point corresponds to the highest water use efficiency of a monoculture; after this point the crop response curves usually turn concave with increasing amount of water applied.) With diverse crops and management practices, optimal production strategies can result in a variety of profits at rain-fed and inflection points which, when combined, can produce greater profits than simply growing monocultures at an irrigation level (e.g., using a single cropping strategy).

Various embodiments provide production and/or financial strategies that incorporate both (tradeable and non-tradeable) financial contracts and diverse cropping strategies, resulting in one single insurance policy for a portfolio of diverse cropping strategies. These products enable the insured to determine (1) how much resources (e.g., land, nutrients, water, etc.) should be allocated to different farm strategies, and (2) the extent to which financial instruments, such as multiple index rainfall insurance and savings to accompany such allocation, can provide adequate financial protection and achieve the desired level of income/wealth. In various embodiments, the multiple index rainfall insurance may be similar to that described in PCT/US2017/054335, filed September 29, 2017, the contents of which are incorporated herein by reference. Importantly, this method can be used further as a mechanism by which subsidy (e.g., for insurance premiums and tiered pricing for crops) is targeted to incentivize growers to adopt practices whose combined resource use (e.g., water extraction) does not exceed a given resource restriction.

Consider an empirical example from the Central Great Plains, U.S.A. The irrigated land in Central Great Plains grows mostly corn irrigated using water extracted from the Ogallala aquifer, which could run out soon. Considering the net profit estimate of growing corn and sunflower, for an available irrigation water of 127 mm (level 1) on the same acreage, growing sunflowers yields the best profit. However, if the available irrigation water is doubled (level 2), growing corn yields the best profit. At a different level of available irrigation water, a combination of corn and sunflowers at different irrigation levels may provide the best profit. Though the optimal combination of crops and irrigation levels could yield the better profit under the limited available water, adding an insurance strategy to the production strategy can ensure that even a worst case scenario is unlikely to bankrupt the farm. Various embodiments of the present invention may be used to achieve a desired level of wealth (e.g., a net profit floor), W, in all or most possible weather states k.

Fig. 4 provides a flowchart illustrating example processes, procedures, and/or operations for generating a market completing insurance policy. Starting at step/operation 402, a set of production strategies are identified, defined, and/or the like. For example, a set of production strategies may comprise plurality of cropping strategies and/or combinations of cropping strategies. In an example embodiment, a cropping strategy is defined by at least one crop with or without an irrigation strategy and/or other criteria. For example, an example cropping strategy may be growing corn and watering every other day. An example production strategy may be growing corn and summer squash, watering every third day, and applying a particular fertilizer during watering. As should be understood, a variety of production strategies may be identified, determined, and/or defined. In an example embodiment, the identified, determined, and/or defined production strategies may depend on the location and/or market relevant to the market completing insurance policy. For example, a market completing insurance policy may only be relevant and/or available for crops grown in a particular geographical region (e.g., country, state, county, provinces, territories, and/or other geographical region defined based on political, climate, and/or geographical boundaries). For example, the crop of a cropping strategy is appropriate for the geographical region corresponding to the market completing insurance policy. In an example embodiment, the set of production strategies may be automatically defined by analysis computing entity 10 (e.g., based on information/data corresponding to crops and/or irrigation practices associated with the geographical region corresponding to the market completing insurance policy, and/or the like), or defined by user input received by the analysis computing entity 10 (e.g., through user input via a user interface of the analysis computing entity 10, through a communication interface based on user input received via a user interface of a user computing entity 20, and/or the like). In an example embodiment, the set of production strategies may only comprise one cropping strategy. For example, a farmer may have previously decided upon a production strategy (e.g., cropping strategy or a combination of cropping strategies) and is seeking coverage based on the already decided upon production strategy.

At step/operation 404, a set of specified states are defined. In an example embodiment, a set of specified states comprises a plurality of specified states (also referred to herein as states). In an example embodiment, each state corresponds to a set of indices. For example, a state may be defined by one or more weather indices. For example, the one or more weather indices may comprise the average or mean rainfall over a period of time (e.g., the growing season for the crop, a period of time before the growing season for the crop, a combination thereof, and/or the like), the fraction of days during the period of time that rainfall was experienced, the average high, low, or other temperature during the period of time, and/or the like. For example, Fig. 5 illustrates a two weather index plot 500 wherein the two weather indices are mean daily rainfall during a period of time and the frequency in days that rainfall is experienced during the period of time. A possible state 505 may be a specific point or node in the plot 500 (e.g., defined by particular index values), an area of the plot 500 (e.g., defined by a range of index values), and/or the like. For example, a possible state 505 may be described by a set of index information corresponding to the index values and/or range of index values that define the possible state 505. For example, in various embodiments, the market completing insurance policy is determined, defined, generated, and/or the like at a time to, the specified states are specified states of the world that may be experienced during at least a portion of the time between time to and time ti, wherein time ti is the time when a crop is harvested, sold, and/or the like. In an example embodiment, the set of specified states may not include all of the possible states of the world. For example, the set of specified states may include only relatively good states for crop production, only relatively poor states for crop production, relatively poor and relatively good states but not in between states, only in between states, and/or the like. Time to may be before the planting of one or more crops, before the growing season for one or more crops, after planting time for one or more crops but before the halfway point of the growing season is reached, before the planting of the one or more crops, and/or the like. In an example embodiment, the specified states relate to weather experienced during at least a portion of the time between time to and time ti as described by one or more weather indices. In an example embodiment, the set of specified states may be automatically defined by analysis computing entity 10 (e.g., based on information/data corresponding to historical weather information/data associated with the geographical region corresponding to the market completing insurance policy, and/or the like), or defined by user input received by the analysis computing entity 10 (e.g., through user input via a user interface of the analysis computing entity 10, through a communication interface based on user input received via a user interface of a user computing entity 20, and/or the like). In an example embodiment, the set of specified states comprises more elements than the set of production strategies. For example, the number of specified states defined is more than the number of production strategies defined, in an example embodiment. In an example embodiment, the states may refer to two or more time periods. For example if the production strategies include two or more crops that may have back-to-back or overlapping growing seasons, the state may refer to weather that occurs before and/or during the growing season of each crop.

Continuing with Fig. 4, at step/operation 406, a crop estimate for each production strategy of the set of production strategies for each possible state of the set of specified states is determined. For example, the analysis computing entity 10 may determine a crop estimate for each production strategy of the set of production strategies for each possible state of the set of specified states. In an example embodiment, a crop estimate may be a crop yield estimate, the mean or a selected quantile of a distribution of crop yield and/or price of a crop yield model or empirical data, and/or the like. In an example embodiment, the selected quantile may be determined based on the risk preference of the farmer, firm, and/or the like that the insurance policy is being designed for. In example embodiments, the crop yield model may provide a probability distribution of the crop yield. In some embodiments in which the crop yield model provides a probability distribution, the crop estimate may be based on a selected quantile of the probability distribution. For example, a crop yield model may be evaluated based on index information/data corresponding to a possible state. In example embodiments, the crop yield model may be a stochastic crop yield model having one or more weather indices (e.g., rainfall parameters such as average daily rainfall for a period of time and frequency of rainfall over the period of time, average high, low, or other temperature during the period of time, and/or the like). In example embodiments, the crop yield model evaluated may be based, selected, optimized, and/or the like at least in part on crop information/data corresponding to the production strategy. For example, a crop yield model may be evaluated based on average amount of rainfall for rainy days and the fraction and/or number of rainy days during a particular portion of the crop preparation, growing, season, vegetative period, reproductive period, and/or the like. The crop yield model may be evaluated based at least in part on the index information/data to determine a crop estimate. For example, the mean or selected quantile of the crop yield model may be selected as the crop estimate. In example embodiments, the crop yield model may be based and/or evaluated based on a location of the crop. In example embodiments, the crop yield model may include one or more factors configured to account for heterogeneities between various crop locations, crop production practices, geographical differences between the crop location and the location where the measurement information/data was captured, and/or the like. In an example embodiment, the crop estimate may be determined based on empirical information/data and/or a combination of empirical information/data and a crop yield model.

While many different crop yield models may be used depending on the type of crop, other crop information/data, indices to be considered, and/or the like. The following example crop yield model is provided herein as an illustrative example. For example, the indices may be average rainfall on rainy days (e.g., days on which rain is experienced at the location of the crop) and fraction or number of days during which rainfall occurred during a time period. These indices may be used to estimate the soil moisture during the growing season, vegetative period, reproductive period, and/or the like for the crop. The soil moisture influences rain- fed crop production through its control on carbon assimilation and nutrient availability. For example, a rainfall intensity and frequency driven yield model may be defined as ≤ s ^* , wherein Y(t) is the crop yield at

time t, gy(s(t)) is the soil moisture-dependent grain- filling rate, and a represents the sensitivity of crop yield to drought (a higher a means greater sensitivity), s* is the soil moisture level at which incipient stomatal level occurs, and s _w is the soil moisture level at which the stomata are fully closed. As should be understood, gy.max, s*, s _w , and a may be selected, optimized, and/or the like based on the crop type, other crop information/data, location, other measurement information/data (e.g., average daily temperature), and/or the like. This model may then be used to determine the mean yield for the crop μ _γ = g _Y )T _Y , where Τγ is the period of grain filling from mobilization of carbon assimilates accumulated during the vegetative period and those synthesized during the last part of the growing season. By applying a probability density function of soil moisture, one can derive (g _Y ) = g _{Y mm} [p ' {s' )+ z (p (s ), a )\, wherein P'(s*) is the non-stressed proportion of time (e.g., the time when s > s*) during which crops achieve the maximum grain filling rate of g max and Z is the stress function that captures the non-linear response of the grain filling rate to soil moisture deficit (e.g., when s _w ≤ s≤ s*), which is a function of the probability density function of the soil moisture p(s) and the drought sensitivity of the crop a. Thus, based on the index information/data, the crop yield model may be evaluated. In example embodiments, various crop yield models may be evaluated to determine a crop estimate, as appropriate for the application and the crop. In an example embodiment, the crop estimate may be determined based on empirical data in addition to and/or in place of the crop yield model.

At step/operation 408, an estimated crop value for each production strategy of the set of production strategies for each possible state of the set of specified states and subsequent net profit is determined. In an example embodiment, the net profit floor is determined. For example, the net profit floor may be selected to be the net profit in a state in which no insurance pay-out would be provided (e.g., a state in which crop production is at least reasonably good). In an example embodiment, a crop value estimate may be determined for each production strategy of the set of production strategies for each state of the set of specified states. For example, the analysis computing entity 10 may determine a crop value estimate for each production strategy of the set of production strategies for each possible state of the set of specified states. In example embodiments, the crop value estimate may be a crop value estimate determined based on the crop yield estimate and/or crop estimate and one or more unit prices for the crop. In an example embodiment, an average unit price may be used to determine the crop value estimate, wherein the average unit price is an average or weighted average of two or more unit prices determined by different methods. For example, a price per unit estimate of the crop may be determined and/or received (e.g., from the index information/data computing entity 40 and/or the like) and used to determine a crop value estimate corresponding to a production strategy (and/or a cropping strategy thereof) and possible state based at least in part on the crop estimate for the corresponding production strategy and possible state. For example, the crop value estimate and/or unit price for the crop may be determined based on one or more crop futures and/or forwards; global, country, regional, or local crop yield models, expected and/or estimated values, or predictions; one or more supply and demand models; one or more financial models, and/or the like. In an example embodiment, the crop value estimate may be determined based on a location of the crop.

At step/operation 410, the state-dependent matrix is generated and inverted. For example, the analysis computing entity 10 may generate an invertible state-dependent matrix and determine, calculate, and/or the like the inverse of the state-dependent matrix. In an example embodiment, the state-dependent matrix comprises a pay-out amount, a policy per unit cost, and/or the like for each state. Fig. 6 shows an example state- dependent matrix 605 wherein the set of specified states comprises six states (e.g., states 1, 2, 3, 4, 5, and 6) and the set of production strategies comprises two cropping strategies (e.g., C2 corresponding to corn irrigated at irrigation level 2 and SF1 corresponding to sunflower irrigated at irrigation level 1). As noted above, the state-dependent matrix is invertible. Thus, the state-dependent matrix is a square matrix (e.g., has the same number of columns and rows). As noted above, in various embodiments, the set of specified states comprises more elements than the set of production strategies. Each row of the state- dependent matrix corresponds to a state of the set of specified states and each column of the state-dependent matrix corresponds to a production or cropping strategy of the set of production strategies or a pay-out strategy. For example, a pay-out strategy may be an example pay-out strategy for a weather-based index insurance policy. For example, pay- out strategy 1 corresponds to an example pay-out strategy wherein if the weather for at least a portion of the time between to and ti corresponds to state 6, the insurance policy provides a pay-out of 236 dollars per unit of insurance policy. The choice of 236 dollars here is based on the desired wealth vector W, which has, in this case, been determined based on the crop value estimate in the no insurance pay-out states (states 1 and 4) and various pay-out values may be used as appropriate for the application. In an example embodiment, a pay-out value corresponds to the corresponding element (e.g., corresponds to the same possible state) of the desired wealth vector W, described below. In another example, pay-out strategy 2 corresponds to an example pay-out strategy wherein if the weather for at least a portion of the time between to and ti corresponds to state 6 or state 5 , the insurance policy provides a pay-out of 236 dollars per unit of insurance policy. For example, the pay-out strategy columns may be organized such that the left most column of the state-dependent matrix comprises a pay-out only in one state (corresponding to the bottom row as shown in the illustrative example of Figure 6), the second left most column comprises a pay-out only in two states (corresponding to the bottom two rows as shown in the illustrative example of Figure 6), and so on, such that the state-dependent matrix X is invertible. The right most columns of the state-dependent matrix correspond to the cropping strategies corresponding to the set of production strategies. Once the state- dependent matrix is conditioned to be invertible by adding the financial pay-out strategies (e.g., insurance pay-out strategies) to the production strategies (e.g., cropping strategies) to complete the market, the state-dependent matrix is inverted. For example, the analysis computing entity 10 may determine, calculate, generate, and/or the like the inverse matrix X ^"1 of the state-dependent matrix X. The state-dependent matrix is related to the optimal allocation vector as shown in equation 625 of Fig. 6, wherein N is the optimal allocation vector, X is the state-dependent matrix, and W is the desired wealth vector. The desired wealth vector W corresponds to the desired wealth to be achieved at time ti for each state of the set of specified states. In an example embodiment, the desired wealth vector W is a net profit floor vector. For example, desired wealth vector W may be derived from profit of production strategies as provided in the state-dependent matrix X. As shown by example desired wealth vector W 615, the desired wealth at time ti may be the same for each state. For example, a farmer may wish to accomplish a wealth of 236 dollars (per unit of field) at time ti regardless of the weather that occurs between time to and time ti. Based on the equation 625, the example desired wealth vector W 615 and the example state-dependent matrix 605 are used to determine the example optimal allocation vector N 610. For example, continuing with Fig. 4, at step/operation 412, the optimal allocation strategy is determined. For example, the analysis computing entity 10 may determine the optimal allocation strategy. For example, the optimal allocation strategy vector N may be computed by determining, calculating, and/or the like X ^"1 x W, based on the determined inverse matrix X ^"1 of the state-dependent matrix X. The elements of the optimal allocation strategy vector N may each correspond to a production strategy (e.g., cropping strategy and/or combination of cropping strategies) and/or financial strategy (e.g., insurance pay-out strategy). For example, the optimal allocation strategy vector N may comprise negative elements. For example, the last two elements may correspond to cropping strategies C2 and SF1, indicating that a farmer should use cropping strategy C2 for 50.5% of their land and cropping strategy SF1 for 49.5% of their land, and/or the like.

In various embodiments, the desired wealth vector W, may be different for different scenarios or the same for all scenarios. For example, in some embodiments, each row of the desired wealth vector W may be the same value or one or more rows of the desired wealth vector W may have different values. The state-dependent matrix, X, may contain both production strategies and financial strategies. The uncertain rainfall, which when combined with cropping strategies, determines any eventual drought/flood damages. Both uncontrollable and manageable effects are captured in the rows and columns of X, respectively. The rows capture uncertainties by representing various specified states of the world (e.g., various rainfall patterns that farmers might face in a given season). The columns capture strategies to deal with those uncertainties through adopting various practices, including, for example, production strategies (e.g., combinations of irrigation levels, tillage and/or no/semi tillage practices, fertilization strategies, pesticide strategies, and/or the like) and financial strategies (e.g., multiple rainfall index insurance), to achieve desirable outcomes.

At step/operation 414, of Fig. 4, the state-dependent matrix X is collapsed based on the optimal allocation strategy vector N to generate the market completing insurance policy. For example, a pay-out table, such as example pay-out table 635 shown in Fig. 6 may be determined by collapsing the state-dependent matrix X based on the optimal allocation strategy vector N. Fig. 7 illustrates how the state-dependent matrix X is collapsed based on the optimal allocation strategy vector N for the example wherein the state-dependent matrix X corresponds to a set of specified states that corresponds to six rows and a set of cropping strategies that corresponds to two columns. In an example embodiment, the pay-out may be for a determined amount of land (e.g., per unit of field). For example, a unit of the market completing insurance policy may insure one hectare, half a hectare, and/or the like. In an example embodiment, a price vector P corresponding to a cost for each production strategy and pay-out strategy may also be determined. For example, the value of an element of the example price vector P 620 shown in Fig. 6 may be determined based on the cost of an insurance policy that provides the corresponding pay-out and/or the cost for the corresponding production strategy (e.g., cropping strategy and/or combination of cropping strategies) in a consistent manner with entries in the state- dependent matrix X (e.g., cost for seeds, irrigation, planting, labor, and/or the like). In an example embodiment, the entries in P that correspond to production/cropping strategies are set as 0 since the state-dependent matrix X already incorporates the cost of the production/cropping strategies. For example, the state-dependent matrix X reflects the net profit for the production/cropping strategies. In an example embodiment, the state- dependent matrix X may be collapsed, based on the optimal allocation strategy vector N, into one or more insurance policies. Similarly, in an example embodiment, an insurance policy may be determined and/or defined that relates only to a single state.

For example, the price for a market completing insurance policy may be determined based on the positive payout calculated by the method presented here. For example, in Fig 6, if each state has the same probability of occurrence, this portfolio of growing corn at irrigation level 2 at 50.5% and sunflower at irrigation level 1 at 49.5% can achieve the desired wealth of $236 per unit land with insurance payout $68 in state 6. If the actuarial pricing is used at the loading factor of 1.07, the premium can be calculated as 68*(1/6)*1.07. Knowing the needed insurance payout to complete the market, the insurance policy could also be priced through other pricing methodologies. As should be understood, the market completing insurance policy may cover a production strategy that comprises one or more cropping strategies each identifying a crop and, in an example embodiment, at least one of at least one of (a) an irrigation strategy, (b) a fertilization strategy, (c) a tillage strategy, or (d) a pesticide strategy, and/or the like. Thus, rather than having to purchase multiple insurance policies to cover each individual crop, irrigation strategy, and/or the like that a farm may employ, the farm may merely purchase a single insurance policy that is particular to the particular production strategy (e.g., combination of cropping strategies).

As illustrated by example pay-out table 635, the pay-out provided by the market completing insurance policy which allows incorporation of various financial and production strategies may not be monotonic. For example, if 1 through 6 are adjacent states along a normal gradient of a particular weather index. For example, state 1 may be the hottest (e.g., average temperature of 90° F), state 2 may be the second hottest (e.g., average temperature of 88° F), state 3 may be the third hottest (e.g., average temperature of 86° F), state 4 may be the third coolest (e.g., average temperature of 84° F), state 5 may be the second coolest(e.g., average temperature of 82° F), and state 6 may be coolest (e.g., average temperature of 80° F). In another example, state 1 may be the wettest, state 6 may be the driest, and states 2-5 may be adjacent and/or consecutive states there between. Traditional insurance pay-outs are generally structured to increase or decrease along normal gradients of a weather index. However, as shown in the example of Fig. 6, the payout of the market completing insurance policy may not be monotonic. For example, in the example shown in Fig. 6, the pay-out stays the same between states 1 and 2, increases between states 2 and 3, decreases between states 3 and 4, increases between states 4 and 5, and then decreases between states 5 and 6. Thus, the pay-out provided by the market completing insurance policy does not increase or decrease monotonically across the set of specified states as you move between adjacent and/or consecutive states along a normal gradient of a weather index. As shown in Fig. 6, the pay-out table 635 may be provided as a list of pay-outs corresponding to states. In an example embodiment, the payout table 635 may comprise negative numbers which indicates that the farm achieves a surplus of the desired wealth in that state (as indicated by the desired wealth vector W) and thus achieves a savings in that state of the world. Upon determination of a pay-out table, the analysis computing entity 10 may provide the pay-out table. For example, the analysis computing entity 10 may provide (e.g., transmit) the pay-out table such that the pay-out table is received by a user computing entity 20 (e.g., for display via a display device 216 of the user computing entity 20) or may cause the pay-out table to be provided to a user/individual via a display, printed by a printer, provided as audible output and/or the like of another user interface of and/or in communication with the analysis computing entity 10. In an example embodiment, the optimal allocation for each production strategy of the set of production strategies may also be provided. In an example embodiment, the optimal allocation and description of each cropping strategy (e.g., crop, irrigation strategy, and/or other parameters that define the cropping strategy) of the optimal production strategy may be provided.

In example scenarios, a production strategy may be influenced by a water use restriction. Various embodiments provide an optimal production strategy (e.g., combination of cropping strategies) for scenarios in which a water use restriction must be taken into account. In various embodiments, the optimal production strategy is the production strategy corresponding to the greatest net profit floor. For example, a Pareto analysis of a plurality of production and/or cropping strategies may be performed to identify a Pareto frontier based on water use. The optimal production strategy for a particular water use restriction is the production strategy located at the Pareto frontier at a point corresponding to the particular water use restriction. For example, Fig. 8 illustrates a Pareto analysis of a plurality of production strategies (e.g., each comprising a combination of cropping strategies). In particular, Fig. 8 provides a graph illustrating the final profit, expected and/or estimated profit, and/or net profit floor on the y-axis and irrigation level and/or water usage on the x-axis. Each data point represents an example production strategy (e.g., combination of cropping strategies). The black stars represent production strategies (e.g., combinations of cropping strategies) located on the Pareto frontier. If a farmer must take into account a water restriction of irrigation level 1.5 in selecting a production strategy, the optimal production strategy for that farmer is given by the black star corresponding to irrigation level 1.5. In various scenarios, the optimal production strategy may comprise multiple crops and/or multiple irrigation levels (e.g., a combination of cropping strategies). For example, if the water use restriction requires a farm to use no more than irrigation level 1 per hectare of field, the optimal production strategy may include using a first calculated part or fraction of the field space for a first crop that performs well within minimal irrigation and irrigating the first crop at a minimal irrigation level and using a second calculated part or fraction of the field space for a second crop that requires moderate to significant irrigation and irrigating the second crop at a moderate irrigation level such that the total irrigation level per hectare is in accordance with the water use restriction. As described above, an optimal production strategy may also be accompanied by one or more financial strategies (e.g., insurance policies) for the crops. As should be understood, various other restrictions may be considered when determining an optimal production strategy. For example, restrictions regarding pesticide use and/or other restrictions may be considered via a similar analysis.

Figs. 8A, 8B, and 8C provide an example three-dimensional (3D) Pareto analysis of a set of production strategies and projections of the 3D Pareto analysis into different planes. Fig. 8B illustrates the 3D Pareto analysis, Fig. 8A illustrates the projection of the 3D Pareto analysis in the mean profit v. irrigation level plane, and Fig. 8C illustrates the projection of the 3D Pareto analysis in the value at risk (e.g., net profit floor) v. irrigation level plane. Such a 3D Pareto analysis may be used to identify an optimal production strategy and/or optimal production and financial strategies based on a plurality of restrictions and/or considerations.

In various embodiments, application program code may be provided to a user computing entity 20 (e.g., received via the network interface 220 and stored in memory 222, 224). The application program code, when executed by processing element 208, may be configured to provide an interactive user interface via a user interface (e.g., comprising display 216, a user input device such as keyboard 218, for example, and/or the like) of the user computing entity 20. For example, the interactive user interface may provide a user with a crop planning screen 900, as shown in Fig. 9. For example, the crop planning screen 900 may be configured for requesting and receiving (e.g., through user input via one or more user input devices) information/data that may be used to determine an optimal cropping strategy. For example, the crop planning screen 900 may comprise a location field 905 where a user may type a street address, a city and state, a county and state, a geolocation (e.g., latitude and longitude), and/or the like (e.g., via using the keyboard 218) to provide a location of the farm. In an example embodiment, the execution of the application program code by the processing element 208 may cause a location determining device (e.g., a global navigation satellite system or global positioning system sensor) to determine a location of the user computing entity 20 and provide that location as the location of the farm in the location field 905. In another example embodiment, a user may be provided with a map and be asked to select the location of the farm on the map. The crop planning screen 900 may further comprise one or more restrictions and considerations fields 910. For example, the crop planning screen 900 may comprise restrictions and considerations fields 910 for a user to enter information/data corresponding to water usage restrictions, pesticide usage restrictions, considerations particular to the farm (e.g., shade level of the field, soil type, infrastructure considerations, preferred crops, undesired crops, and/or the like), and/or other restrictions and/or considerations. For example, if a farm has a particular piece of equipment that is used in planting/harvesting a particular crop, the particular crop may be a preferred crop. Similarly, if the farm does not have a particular piece of equipment for planting/harvesting a particular crop, that crop may be an undesired crop. The crop planning screen 900 may further comprise a selectable submission element 915. When the user selects the selectable submission element 915, the user computing entity 20 may use the information/data provided via the location field 905 and the restrictions and considerations fields 910 to determine an optimal production strategy and/or a suggested production strategy based on any considerations provided in restrictions and considerations fields 910. As used herein a restriction may be necessary to comply with (e.g., a government imposed water restriction) and a consideration may be any other factors to be taken into account. For example, financial products and/or incentives available in the area may affect the choice of productions strategies. In an example embodiment, a user may be able to provide input via the crop planning screen 900 regarding a risk preference (e.g., a level of residual risk and/or the like) that the user is willing to have present in the optimal and/or suggested production strategy.

In various embodiments, the optimal and/or suggested production strategy may be determined (e.g., by an analysis computing entity 10 and/or a user computing entity 20) in response to user selection of the selectable submission element 915 of the interactive user interface. The optimal and/or suggested production strategy may be determined based on the optimal allocation vector N, one or more predetermined market completing insurance policies and/or market completing insurance policies that are determined approximately simultaneously (e.g., within real time and/or near real time) with the determination of the optimal and/or suggested production strategy, a Pareto analysis and identification of the Pareto frontier, and/or the like as described above. In an example embodiment, the determination of the optimal and/or suggested production strategy is performed responsive to the selection of the selectable submission element 915 via the execution of the application program code by processing element 208. Responsive to determining the optimal and/or suggested production strategy, the application program code (executed by processing element 208) causes a production strategy screen 1000 to be provided to the user as an interactive user interface provided via the user interface (e.g., display 216) of the user computing entity 20. An example production strategy screen 1000 is illustrated in Fig. 10. For example, the production strategy screen 1000 may provide the user with information/data regarding the optimal and/or suggested production strategy for the user's farm. For example, the production strategy screen 1000 may display information/data regarding one, two, or more crops of the optimal and/or suggested production strategy; irrigation level information/data, pesticide use information/data, field allocation information/data and/or the like for each crop of the optimal and/or suggested production strategy; information/data regarding a financial (e.g., insurance) strategy; information/data regarding an initial investment and/or net profit floor for the optimal and/or suggested production strategy; and/or the like. In various embodiments, a portion of the information/data regarding the optimal and/or suggested production strategy may be provided as a graphical representation. For example, the field allocation per crop may be provided as an infographic, pie chart, and/or the like. Thus, user interaction with the selectable submission element 915 of the crop planning screen 900 launches a function of the application (e.g., via the execution of the application program code by the processing element 208) which results in the user being provided with simplified access (e.g., via the display 216) to a personalized optimal and/or suggested cropping strategy, as determined via the application.

As should be understood, if the optimal and/or suggested production and/or financial strategies is determined by the analysis computing entity 10, the user computing entity 20 provides a request to the analysis computing entity 10 (e.g., responsive to the user selecting the selectable submission element 915 and based on the location information/data provided in the location field 905 and the restrictions and/or considerations information/data provided in the restrictions and considerations fields 910), the analysis computing entity 10 receives the request and responsive to processing the request determines the optimal and/or suggested production and/or financial strategies and then provides the optimal and/or suggested production and/or financial strategies such that the user computing entity 20 receives the optimal and/or suggest production and/or financial strategies. In an example embodiment, the functions provided via the application program code described herein may be provided via a web portal, Internet browser, a website, and/or the like provided by an analysis computing entity 10 and accessed via the user interface of the user computing entity 20.

IV. Technical Advantages

Various embodiments provide technological improvements in the field of agriculture. For example, it is difficult to objectively determine an optimal production strategy and/or optimal combination of production and financial strategies, especially when various restrictions need to be taken into account. For example, if a farmer needs to determine a production strategy given a particular irrigation restriction, the farmer is likely to choose to grow one crop and irrigate that crop at the restricted level of irrigation (when water is relatively cheap). However, a better production strategy, that can achieve a much higher net profit floor, may be to use a combination of cropping strategies such as growing a first crop irrigated at a first level and growing a second crop irrigated at a second level such that the total irrigation is in accordance with the restricted level of irrigation. Thus, the ability to objectively determine an optimal production strategy and/or optimal combination of production and financial strategies for a particular location and given a particular set of restrictions is a technical problem in the field of agriculture. Various embodiments provide a technical solution to this technical problem. For example, various embodiments provide improvements in objective production strategy planning that takes into account the location of the farm and restrictions that are relevant to the farm. Various embodiments of the present invention provide improvements to user interfaces and the ability of a user computing entity to efficiently and easily provide (e.g., via a display) various information/data that is determined based at least in part on user input. Various embodiments provide for efficient and automated determination of pay-out information and/or premium information/data for one or more financial instruments that are based on multiple weather indices. Various embodiments promote the planting of a variety of crops, thus preventing issues caused by mono-cultures. Moreover, various embodiments enable farmers to reduce overall water usage while still producing profit making crops. Thus, various embodiments provide improvements to agricultural technology and promote sustainable farming practices.

V. Conclusion Many modifications and other embodiments of the inventions set forth herein will come to mind to one skilled in the art to which these inventions pertain having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the inventions are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.