Patent Publication Number: US-2020288626-A1

Title: Methods and systems for managing agricultural activities

Description:
BENEFIT CLAIM 
     This application claims the benefit under 35 U.S.C. § 120 as a Continuation of application Ser. No. 14/846,422, Sep. 4, 2015, which claims the benefit under 35 U.S.C. § 119(e) of provisional application 62/049,898, filed Sep. 12, 2014, the entire contents of which are hereby incorporated by reference as if fully set forth herein. 
    
    
     COPYRIGHT NOTICE 
     A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright or rights whatsoever. ©2020 The Climate Corporation. 
     BACKGROUND 
     The embodiments described herein relate generally to agricultural activities and, more particularly, systems and methods for managing and recommending agricultural activities at the field level based on crop-related data and field-condition data. 
     Agricultural production requires significant strategy and analysis. In many cases, agricultural growers (e.g., farmers or others involved in agricultural cultivation) are required to analyze a variety of data to make strategic decisions months in advance of the period of crop cultivation (i.e., growing season). In making such strategic decisions, growers must consider at least some of the following decision constraints: fuel and resource costs, historical and projected weather trends, soil conditions, projected risks posed by pests, disease and weather events, and projected market values of agricultural commodities (i.e., crops). Analyzing these decision constraints may help a grower to predict key agricultural outcomes including crop yield, energy usage, cost and resource utilization, and farm profitability. Such analysis may inform a grower&#39;s strategic decisions of determining crop cultivation types, methods, and timing. 
     Despite its importance, such analysis and strategy is difficult to accomplish for a variety of reasons. First, obtaining reliable information for the various considerations of the grower is often difficult. Second, aggregating such information into a usable manner is a time consuming task. Third, where data is available, it may not be precise enough to be useful to determine strategy. For example, weather data (historical or projected) is often generalized for a large region such as a county or a state. In reality, weather may vary significantly at a much more granular level, such as an individual field. In addition, terrain features may cause weather data to vary significantly in even small regions. 
     Additionally, growers often must regularly make decisions during growing season. Such decisions may include adjusting when to harvest, providing supplemental fertilizer, and how to mitigate risks posed by pests, disease and weather. As a result, growers must continually monitor various aspects of their crops during the growing season including weather, soil, and crop conditions. Accurately monitoring all such aspects at a granular level is difficult and time consuming. Accordingly, methods and systems for analyzing crop-related data and providing field condition data and strategic recommendations for maximizing crop yield are desirable. 
     BRIEF DESCRIPTION OF THE DISCLOSURE 
     In one aspect, a computer-implemented method for recommending agricultural activities is provided. The method is implemented by an agricultural intelligence computer system in communication with a memory. The method includes receiving a plurality of field definition data, retrieving a plurality of input data from a plurality of data networks, determining a field region based on the field definition data, identifying a subset of the plurality of input data associated with the field region, determining a plurality of field condition data based on the subset of the plurality of input data, identifying a plurality of field activity options, determining a recommendation score for each of the plurality of field activity options based at least in part on the plurality of field condition data, and providing a recommended field activity option from the plurality of field activity options based on the plurality of recommendation scores. 
     In another aspect, a networked agricultural intelligence system for recommending agricultural activities is provided. The networked agricultural intelligence system includes a user device, a plurality of data networks computer systems, an agricultural intelligence computer system comprising a processor and a memory in communication with the processor. The processor is configured to receive a plurality of field definition data from the user device, retrieve a plurality of input data from a plurality of data networks, determine a field region based on the field definition data, identify a subset of the plurality of input data associated with the field region, determine a plurality of field condition data based on the subset of the plurality of input data, identify a plurality of field activity options, determine a recommendation score for each of the plurality of field activity options based at least in part on the plurality of field condition data, and provide a recommended field activity option from the plurality of field activity options based on the plurality of recommendation scores. 
     In a further aspect, computer-readable storage media for recommending agricultural activities is provided. The computer-readable storage media has computer-executable instructions embodied thereon. When executed by at least one processor, the computer-executable instructions cause a processor to receive a plurality of field definition data from the user device, retrieve a plurality of input data from a plurality of data networks, determine a field region based on the field definition data, identify a subset of the plurality of input data associated with the field region, determine a plurality of field condition data based on the subset of the plurality of input data, identify a plurality of field activity options, determine a recommendation score for each of the plurality of field activity options based at least in part on the plurality of field condition data, and provide a recommended field activity option from the plurality of field activity options based on the plurality of recommendation scores. 
     In an additional aspect, an agricultural intelligence computer system is provided. The agricultural intelligence computer system includes a processor and a memory in communication with the processor. The processor is configured to receive a plurality of field definition data from the user device, retrieve a plurality of input data from the plurality of data networks, determine a field region based on the field definition data, identify a subset of the plurality of input data associated with the field region, determine a plurality of field condition data based on the subset of the plurality of input data, and provide the plurality of field condition data to the user device. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram depicting an example agricultural environment including a plurality of fields that are monitored and managed with an agricultural intelligence computer system that is used to manage and recommend agricultural activities; 
         FIG. 2  is a block diagram of a user computing device, used for managing and recommending agricultural activities, as shown in the agricultural environment of  FIG. 1 ; 
         FIG. 3  is a block diagram of a computing device, used for managing and recommending agricultural activities, as shown in the agricultural environment of  FIG. 1 ; 
         FIG. 4  is an example data flowchart of managing and recommending agricultural activities using the computing devices of  FIGS. 1, 2, and 3  in the agricultural environment shown in  FIG. 1 ; 
         FIG. 5  is an example method for managing agricultural activities in the agricultural environment of  FIG. 1 ; 
         FIG. 6  is an example method for recommending agricultural activities in the agricultural environment of  FIG. 1 ; 
         FIG. 7  is a diagram of an example computing device used in the agricultural environment of  FIG. 1  to recommend and manage agricultural activities; and 
         FIGS. 8-30  are example illustrations of information provided by the agricultural intelligence computer system of  FIG. 3  to the user device of  FIG. 2  to facilitate the management and recommendation of agricultural activities. 
     
    
    
     Although specific features of various embodiments may be shown in some drawings and not in others, this is for convenience only. Any feature of any drawing may be referenced and/or claimed in combination with any feature of any other drawing. 
     DETAILED DESCRIPTION OF THE DISCLOSURE 
     The following detailed description of the embodiments of the disclosure refers to the accompanying drawings. The same reference numbers in different drawings may identify the same or similar elements. Also, the following detailed description does not limit the claims. 
     The subject matter described herein relates generally to managing and recommending agricultural activities for a user such as a grower or a farmer. Specifically, a first embodiment of the methods and systems described herein includes (i) receiving a plurality of field definition data, (ii) retrieving a plurality of input data from a plurality of data networks, (iii) determining a field region based on the field definition data, (iv) identifying a subset of the plurality of input data associated with the field region, (v) determining a plurality of field condition data based on the subset of the plurality of input data, and (vi) providing the plurality of field condition data to the user device. 
     A second embodiment of the methods and systems described herein includes (i) receiving a plurality of field definition data, (ii) retrieving a plurality of input data from a plurality of data networks, (iii) determining a field region based on the field definition data, (iv) identifying a subset of the plurality of input data associated with the field region, (v) determining a plurality of field condition data based on the subset of the plurality of input data, (vi) identifying a plurality of field activity options, (vii) determining a recommendation score for each of the plurality of field activity options based at least in part on the plurality of field condition data, and (viii) providing a recommended field activity option from the plurality of field activity options based on the plurality of recommendation scores. 
     In at least some agricultural environments (e.g., farms, groups of farms, and other agricultural cultivation environments), agricultural growers employ significant strategy and analysis to make decisions on agricultural cultivation. In many cases, growers analyze a variety of data to make strategic decisions months in advance of the period of crop cultivation (i.e., growing season). In making such strategic decisions, growers must consider at least some of the following decision constraints: fuel and resource costs, historical and projected weather trends, soil conditions, projected risks posed by pests, disease and weather events, and projected market values of agricultural commodities (i.e., crops). Analyzing these decision constraints may help a grower to predict key agricultural outcomes including crop yield, energy usage, cost and resource utilization, and farm profitability. Such analysis may inform a grower&#39;s strategic decisions of determining crop cultivation types, methods, and timing. Despite its importance, such analysis and strategy is difficult to accomplish for a variety of reasons. First, obtaining reliable information for the various considerations of the grower is often difficult. Second, aggregating such information into a usable manner is a time consuming task. Third, where data is available, it may not be precise enough to be useful to determine strategy. For example, weather data (historical or projected) is often generalized for a large region such as a county or a state. In reality, weather may vary significantly at a much more granular level, such as an individual field. Terrain features may cause weather data to vary significantly in even small regions. 
     Additionally, growers often must regularly make decisions during growing season. Such decisions may include adjusting when to harvest, providing supplemental fertilizer, and how to mitigate risks posed by pests, disease and weather. As a result, growers must continually monitor various aspects of their crops during the growing season including weather, soil, and crop conditions. Accurately monitoring all such aspects at a granular level is difficult and time consuming. Accordingly, methods and systems for analyzing crop-related data, and providing field condition data and strategic recommendations for maximizing crop yield are desirable. Accordingly, the systems and methods described herein facilitate the management and recommendation of agricultural activities to growers. 
     As used herein, the term “agricultural intelligence services” refers to a plurality of data providers used to aid a user (e.g., a farmer, agronomist or consultant) in managing agricultural services and to provide the user with recommendations of agricultural services. As used herein, the terms “agricultural intelligence service”, “data network”, “data service”, “data provider”, and “data source” are used interchangeably herein unless otherwise specified. In some embodiments, the agricultural intelligence service may be an external data network (e.g., a third-party system). As used herein, data provided by any such “agricultural intelligence services” or “data networks” may be referred to as “input data”, or “source data.” 
     As used herein, the term “agricultural intelligence computer system” refers to a computer system configured to carry out the methods described herein. The agricultural intelligence computer system is in networked connectivity with a “user device” (e.g., desktop computer, laptop computer, smartphone, personal digital assistant, tablet or other computing device) and a plurality of data sources. In the example embodiment, the agricultural intelligence computer system provides the agricultural intelligence services using a cloud-based software as a service (SaaS) model. Therefore, the agricultural intelligence computer system may be implemented using a variety of distinct computing devices. The user device may interact with the agricultural intelligence computer system using any suitable network. 
     In an example embodiment, an agricultural machine (e.g., combine, tractor, cultivator, plow, subsoiler, sprayer or other machinery used on a farm to help with farming) may be coupled to a computing device (“agricultural machine computing device”) that interacts with the agricultural intelligence computer system in a similar manner as the user device. In some examples, the agricultural machine computing device could be a planter monitor, planter controller or a yield monitor. The agricultural machine and agricultural machine computing device may provide the agricultural intelligence computer system with field definition data and field-specific data. 
     The term “field definition data” refers to field identifiers, geographic identifiers, boundary identifiers, crop identifiers, and any other suitable data that may be used to identify farm land, such as a common land unit (CLU), lot and block number, a parcel number, geographic coordinates and boundaries, Farm Serial Number (FSN), farm number, tract number, field number, section, township, and/or range. According to the United States Department of Agriculture (USDA) Farm Service Agency, a CLU is the smallest unit of land that has a permanent, contiguous boundary, a common land cover and land management, a common owner and a common producer in agricultural land associated with USDA farm programs. CLU boundaries are delineated from relatively permanent features such as fence lines, roads, and/or waterways. The USDA Farm Service Agency maintains a Geographic Information Systems (GIS) database containing CLUs for farms in the United States. 
     When field definition and field-specific data is not provided directly to the agricultural intelligence computer system via one or more agricultural machines or agricultural machine devices that interacts with the agricultural intelligence computer system, the user may be prompted via one or more user interfaces on the user device (served by the agricultural intelligence computer system) to input such information. In an example embodiment, the user may identify field definition data by accessing a map on the user device (served by the agricultural intelligence computer system) and selecting specific CLUs that have been graphically shown on the map. In an alternative embodiment, the user may identify field definition data by accessing a map on the user device (served by the agricultural intelligence computer system) and drawing boundaries of the field over the map. Such CLU selection or map drawings represent geographic identifiers. In alternative embodiments, the user may identify field definition data by accessing field definition data (provided as shape files or in a similar format) from the U.S. Department of Agriculture Farm Service Agency or other source via the user device and providing such field definition data to the agricultural intelligence computer system. The land identified by “field definition data” may be referred to as a “field” or “land tract.” As used herein, the land farmed, or “land tract”, is contained in a region that may be referred to as a “field region.” Such a “field region” may be coextensive with, for example, temperature grids or precipitation grids, as used and defined below. 
     The term “field-specific data” refers to (a) field data (e.g., field name, soil type, acreage, tilling status, irrigation status), (b) harvest data (e.g., crop type, crop variety, crop rotation, whether the crop is grown organically, harvest date, Actual Production History (APH), expected yield, yield, crop price, crop revenue, grain moisture, tillage practice, weather information (e.g., temperature, rainfall) to the extent maintained or accessible by the user, previous growing season information), (c) soil composition (e.g., pH, organic matter (OM), cation exchange capacity (CEC)), (d) planting data (e.g., planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population), (e) nitrogen data (e.g., application date, amount, source), (f) pesticide data (e.g., pesticide, herbicide, fungicide, other substance or mixture of substances intended for use as a plant regulator, defoliant, or desiccant), (g) irrigation data (e.g., application date, amount, source), and (h) scouting observations (photos, videos, free form notes, voice recordings, voice transcriptions, weather conditions (temperature, precipitation (current and over time), soil moisture, crop growth stage, wind velocity, relative humidity, dew point, black layer)). If field-specific data is not provided via one or more agricultural machines or agricultural machine devices that interacts with the agricultural intelligence computer system in a similar manner as the user device, a user may provide such data via the user device to the agricultural intelligence computer system. In other words, the user accesses the agricultural intelligence computer system via the user device and provides the field-specific data. 
     The agricultural intelligence computer system also utilizes environmental data to provide agricultural intelligence services. The term “environmental data” refers to environmental information related to farming activities such as weather information, vegetation and crop growth information, seed information, pest and disease information and soil information. Environmental data may be obtained from external data sources accessible by the agricultural intelligence computer system. Environmental data may also be obtained from internal data sources integrated within the agricultural intelligence computer system. Data sources for environmental data may include weather radar sources, satellite-based precipitation sources, meteorological data sources (e.g., weather stations), satellite imagery sources, aerial imagery sources (e.g., airplanes, unmanned aerial vehicles), terrestrial imagery sources (e.g., agricultural machine, unmanned terrestrial vehicle), soil sources and databases, seed databases, crop phenology sources and databases, and pest and disease reporting and prediction sources and databases. For example, a soil database may relate soil types and soil locations to soil data including pH levels, organic matter makeups, and cation exchange capacities. Although in many examples, the user may access data from data sources indirectly via the agricultural intelligence computer system, in other examples, the user may directly access the data sources via any suitable network connection. 
     The agricultural intelligence computer system processes the plurality of field definition data, field-specific data and environmental data from a plurality of data sources to provide a user with the plurality of field condition data for the field or field region identified by the field definition data. The term “field condition data” refers to characteristics and conditions of a field that may be used by the agricultural intelligence computer system to manage and recommend agricultural activities. Field condition data may include, for example, and without limitation, field weather conditions, field workability conditions, growth stage conditions, soil moisture, and precipitation conditions. Field condition data is presented to the user using the user device. 
     The agricultural intelligence computer system also provides a user with a plurality of agricultural intelligence services for the land tract or field region identified by the field definition data. Such agricultural intelligence services may be used to recommend courses of action for the user to undertake. In an example embodiment, the recommendation services include a planting advisor, a nitrogen application advisor, a pest advisor, a field health advisor, a harvest advisor, and a revenue advisor. Each is discussed herein. 
     System Architecture 
     As noted above, the agricultural intelligence computer system may be implemented using a variety of distinct computing devices using any suitable network. In an example embodiment, the agricultural intelligence computer system uses a client-server architecture configured for exchanging data over a network (e.g., the Internet). One or more user devices may communicate via a network with a user application or an application platform. The application platform represents an application available on user devices that may be used to communicate with agricultural intelligence computer system. Other example embodiments may include other network architectures, such as peer-to-peer or distributed network environment. 
     The application platform may provide server-side functionality, via the network to one or more user devices. Accordingly, the application platform may include client side software stored locally at the user device as well as server side software stored at the agricultural intelligence computer system. In an example embodiment, the user device may access the application platform via a web client or a programmatic client. The user device may transmit data to, and receive data from one or more front-end servers. In an example embodiment, the data may take the form of requests and user information input, such as field-specific data, into the user device. One or more front-end servers may process the user device requests and user information and determine whether the requests are service requests or content requests, among other things. Content requests may be transmitted to one or more content management servers for processing. Application requests may be transmitted to one or more application servers. In an example embodiment, application requests may take the form of a request to provide field condition data and/or agricultural intelligence services for one or more fields. 
     In an example embodiment, the application platform may include one or more servers in communication with each other. For example, the agricultural intelligence computer system may include front-end servers, application servers, content management servers, account servers, modeling servers, environmental data servers, and corresponding databases. As noted above, environmental data may be obtained from external data sources accessible by the agricultural intelligence computer system or it may be obtained from internal data sources integrated within the agricultural intelligence computer system. 
     In an example embodiment, external data sources may include third-party hosted servers that provide services to the agricultural intelligence computer system via Application Program Interface (API) requests and responses. The frequency at which the agricultural intelligence computer system may consume data published or made available by these third-party hosted servers may vary based on the type of data. In an example embodiment, a notification may be sent to the agricultural intelligence computer system when new data is available by a data source. The agricultural intelligence computer system may transmit an API call via the network to the agricultural intelligence computer system hosting the data and receive the new data in response to the call. To the extent needed, the agricultural intelligence computer system may process the data to enable components of the application platform to handle the data. For example, processing data may involve extracting data from a stream or a data feed and mapping the data to a data structure, such as an XML data structure. Data received and/or processed by the agricultural intelligence computer system may be transmitted to the application platform and stored in an appropriate database. 
     When an application request is made, the one or more application servers communicate with the content management servers, account servers, modeling servers, environmental data servers, and corresponding databases. In one example, modeling servers may generate a predetermined number of simulations (e.g., 10,000 simulations) using, in part, field-specific data and environmental data for one or more fields identified based on field definition data and user information. Depending on the type of application request, the field-specific data and environmental data for one or more fields may be located in the content management servers, account servers, environmental data servers, the corresponding databases, and, in some instances, archived in the modeling servers and/or application servers. Based on the simulations generated by the modeling servers, field condition data and/or agricultural intelligence services for one or more fields is provided to the application servers for transmission to the requesting user device via the network. More specifically, the user may use the user device to access a plurality of windows or displays showing field condition data and/or agricultural intelligence services, as described below. 
     Although the aforementioned application platform has been configured with various example embodiments above, one skilled in the art will appreciate that any configuration of servers may be possible and that example embodiments of the present disclosure need not be limited to the configurations disclosed herein. 
     Field Condition Data 
     Field Weather and Temperature Conditions 
     As part of the field condition data provided, the agricultural intelligence computer system tracks field weather conditions for each field identified by the user. The agricultural intelligence computer system determines current weather conditions including field temperature, wind, humidity, and dew point. The agricultural intelligence computer system also determines forecasted weather conditions including field temperature, wind, humidity, and dew point for hourly projected intervals, daily projected intervals, or any interval specified by the user. The forecasted weather conditions are also used to forecast field precipitation, field workability, and field growth stage. Near-term forecasts are determined using a meteorological model (e.g., the Microcast model) while long-term projections are determined using historical analog simulations. 
     The agricultural intelligence computer system uses grid temperatures to determine temperature values. Known research shows that using grid techniques provides more accurate temperature measurements than point-based temperature reporting. Temperature grids are typically square physical regions, typically 2.5 miles by 2.5 miles. The agricultural intelligence computer system associates the field with a temperature grid that contains the field. The agricultural intelligence computer system identifies a plurality of weather stations that are proximate to the temperature grid. The agricultural intelligence computer system receives temperature data from the plurality of weather stations. The temperatures reported by the plurality of weather stations are weighted based on their relative proximity to the grid such that more proximate weather stations have higher weights than less proximate weather stations. Further, the relative elevation of the temperature grid is compared to the elevation of the plurality of weather stations. Temperature values reported by the plurality of weather stations are adjusted in response to the relative difference in elevation. In some examples, the temperature grid includes or is adjacent to a body of water. Bodies of water are known to cause a reduction in the temperature of an area. Accordingly, when a particular field is proximate to a body of water as compared to the weather station providing the temperature reading, the reported temperature for the field is adjusted downwards to account for the closer proximity to the body of water. 
     Precipitation values are similarly determined using precipitation grids that utilize meteorological radar data. Precipitation grids have similar purposes and characteristics as temperature grids. Specifically, the agricultural intelligence computer system uses available data sources such as the National Weather Service&#39;s NEXRAD Doppler radar data, rain gauge networks, and weather stations across the U.S. The agricultural intelligence computer system further validates and calibrates reported data with ground station and satellite data. In the example embodiment, the Doppler radar data is obtained for the precipitation grid. The Doppler radar data is used to determine an estimate of precipitation for the precipitation grid. The estimated precipitation is adjusted based on other data sources such as other weather radar sources, ground weather stations (e.g., rain gauges), satellite precipitation sources (e.g., the National Oceanic and Atmospheric Administration&#39;s Satellite Applications and Research), and meteorological sources. By utilizing multiple distinct data sources, more accurate precipitation tracking may be accomplished. 
     Current weather conditions and forecasted weather conditions (hourly, daily, or as specified by the user) are displayed on the user device graphically along with applicable information regarding the specific field, such as field name, crop, acreage, field precipitation, field workability, field growth stage, soil moisture, and any other field definition data or field-specific data that the user may specify. Such information may be displayed on the user device in one or more combinations and level of detail as specified by the user. 
     In an example embodiment, temperature can be displayed as high temperatures, average temperatures and low temperatures over time. Temperature can be shown during a specific time and/or date range and/or harvest year and compared against prior times, years, including a 5 year average, a 15 year average, a 30 year average or as specified by the user. 
     In an example embodiment, precipitation can be displayed as the amount of precipitation and/or accumulated precipitation over time. Precipitation can be shown during a specific time period and/or date range and/or harvest year and compared against prior times, years, including a 5 year average, a 15 year average, a 30 year average or as specified by the user. Precipitation can also be displayed as past and future radar data. In an example embodiment, past radar may be displayed over the last 1.5 hours or as specified by the user. Future radar may be displayed over the next 6 hours or as specified by the user. Radar may be displayed as an overlay of an aerial image map showing the user&#39;s one or more fields where the user has the ability to zoom in and out of the map. Radar can be displayed as static at intervals selected by the user or continuously over intervals selected by the user. The underlying radar data received and/or processed by the agricultural intelligence computer system may be in the form of Gridded Binary (GRIB) files that includes forecast reflectivity files, precipitation type, and precipitation-typed reflectivity values. 
     Field Workability Conditions Data 
     As part of the field condition data, the agricultural intelligence computer system provides field workability conditions, which indicate the degree to which a field or section of a field (associated with the field definition data) may be worked for a given time of year using machinery or other implements. In an example embodiment, the agricultural intelligence computer system retrieves field historical precipitation data over a predetermined period of time, field predicted precipitation over a predetermined period of time, and field temperatures over a predetermined period of time. The retrieved data is used to determine one or more workability index. 
     In an example embodiment, the workability index may be used to derive three values of workability for particular farm activities. The value of “Good” workability indicates high likelihood that field conditions are acceptable for use of machinery or a specified activity during an upcoming time interval. The value of “Check” workability indicates that field conditions may not be ideal for the use of machinery or a specified activity during an upcoming time interval. The value of “Stop” workability indicates that field conditions are not suitable for work or a specified activity during an upcoming time interval. 
     Determined values of workability may vary depending upon the farm activity. For example, planting and tilling typically require a low level of muddiness and may require a higher workability index to achieve a value of “Good” than activities that allow for a higher level of muddiness. In some embodiments, workability indices are distinctly calculated for each activity based on a distinct set of factors. For example, a workability index for planting may correlate to predicted temperature over the next 60 hours while a workability index for harvesting may be correlated to precipitation alone. In some examples, user may be prompted at the user device to answer questions regarding field activities if such information has not already been provided to the agricultural intelligence computer system. For example, a user may be asked what field activities are currently in use. Depending upon the response, the agricultural intelligence computer system may adjust its calculations of the workability index because of the user&#39;s activities, thereby incorporating the feedback of the user into the calculation of the workability index. Alternately, the agricultural intelligence computer system may adjust the recommendations made to the user for activities. In a further example, the agricultural intelligence computer system may recommend that the user stop such activities based on the responses. 
     Field Growth Stage Conditions 
     [As part of the field condition data provided, the agricultural intelligence computer system provides field growth stage conditions (e.g., for corn, vegetative (VE-VT) and reproductive (R1-R6) growth stages) for the crops being grown in each listed field. Vegetative growth stages for corn typically are described as follows. The “VE” stage indicates emergence, the “V1” stage indicates a first fully expanded leaf with a leaf collar; the “V2” stage indicates a second fully expanded leaf with the leaf collar; the “V3” stage indicates a third fully expanded leaf with the leaf collar; any “V(n)” stage indicates an nth fully expanded leaf with the leaf collar; and the “VT” stage indicates that the tassel of the corn is fully emerged. In the reproductive growth stage model described, “R1” indicates a silking period in which pollination and fertilization processes take place; the “R2” or blister stage (occurring 10-14 days after R1) indicates that the kernel of corn is visible and resembles a blister; the “R3” or milk stage (occurring 18-22 days after R1) indicates that the kernel is yellow outside and contains milky white fluid; the “R4” or dough stage (occurring 24-28 days after R1) indicates that the interior of the kernel has thickened to a dough-like consistency; the “R5” or dent stage (occurring 35-42 days after R1) indicates that the kernels are indented at the top and beginning drydown; and the “R6” or physiological maturity stage (occurring 55-65 days after R1) indicates that kernels have reached maximum dry matter accumulation. Field growth stage conditions may be used to determine timing of key farming decisions. The agricultural intelligence computer system computes crop progression for each crop through stages of growth (agronomic stages) by tracking the impact of weather (both historic and forecasted) on the phenomenological development of the crop from planting through harvest. 
     In the example embodiment, the agricultural intelligence computer system uses the planting date entered by the user device to determine field growth stage conditions. In other words, the user may enter the planting date into the user device, which communicates the planting date to the agricultural intelligence computer system. Alternately, the agricultural intelligence computer system may estimate the planting date using a system algorithm. Specifically, the planting date may be estimated based on agronomic stage data and planting practices in the region associated with the field definition data. The planting practices may be received from a data service such as a university data network that monitors typical planting techniques for a region. The agricultural intelligence computer system further uses data regarding the user&#39;s farming practices within the current season and for historical seasons, thereby facilitating historical analysis. In other words, the agricultural intelligence computer system is configured to use historical practices of each particular grower on a subject field or to alternately use historical practices for the corresponding region to predict the planting date of a crop when the actual planting date is not provided by the grower. The agricultural intelligence computer system determines a relative maturity value of the crops based on expected heat units over the growing season in light of the planting date, the user&#39;s farming practices, and field-specific data. As heat is a proxy for energy received by crops, the agricultural intelligence computer system calculates expected heat units for crops and determines a development of maturity of the crops. In the example embodiment, maximum temperatures and low temperatures are used to estimate heat units. 
     Soil Moisture 
     As part of the field condition data, the agricultural intelligence computer system determines and provides soil moisture data via a display showing a client application on the user device. Soil moisture indicates the percent of total water capacity available to the crop that is present in the soil of the field. Soil moisture values are initialized at the beginning of the growing season based on environmental data in the agricultural intelligence computer system at that time, such as data from the North American Land Data Assimilation System, and field-specific data. In another embodiment, a soil analysis computing device may analyze soil samples from a plurality of fields for a grower wherein the plurality of fields includes a selected field. Once analyzed, the results may be directly provided from the soil analysis computing device to the agricultural intelligence computer system so that the soil analysis results may be provided to the grower. Further, data from the soil analysis may be inputted into the agricultural intelligence computer system for use in determining field condition data and agricultural intelligence services. 
     Soil moisture values are then adjusted, at least daily, during the growing season by tracking moisture entering the soil via precipitation and moisture leaving the soil via evapotranspiration (ET). 
     In some examples, water that is received in an area as precipitation does not enter the soil because it is lost as run off. Accordingly, in one example, a gross and net precipitation value is calculated. Gross precipitation indicates a total precipitation value. Net precipitation excludes a calculated amount of water that never enters the soil because it is lost as runoff. A runoff value is determined based on the precipitation amount over time and a curve determined by the USDA classification of soil type. The systems account for a user&#39;s specific field-specific data related to soil to determine runoff and the runoff curve for the specific field. Soil input data, described above, may alternately be provided via the soil analysis computing device. Lighter, sandier soils allow greater precipitation water infiltration and experience less runoff during heavy precipitation events than heavier, more compact soils. Heavier or denser soil types have lower precipitation infiltration rates and lose more precipitation to runoff on days with large precipitation events. 
     Daily evapotranspiration associated with a user&#39;s specific field is calculated based on a version of the standard Penman-Monteith ET model. The total amount of water that is calculated as leaving the soil through evapotranspiration on a given day is based on the following:
     Maximum and minimum temperatures for the day: Warmer temperatures result in greater evapotranspiration values than cooler temperatures.   Latitude: During much of the corn growing season, fields at more northern latitudes experience greater solar radiation than fields at more southern latitudes due to longer days. But fields at more northern latitudes also get reduced radiation due to earth tilting. Areas with greater net solar radiation values will have relatively higher evapotranspiration values than areas with lower net solar radiation values.   Estimated crop growth stage: Growth stages around pollination provide the highest potential daily evapotranspiration values while growth stages around planting and late in grain fill result in relatively lower daily evapotranspiration values, because the crop uses less water in these stages of growth.   Current soil moisture: The agricultural intelligence computer system&#39;s model accounts for the fact that crops conserve and use less water when less water is available in the soil. The reported soil moisture values reported that are above a certain percentage, determined by crop type, provide the highest potential evapotranspiration values and potential evapotranspiration values decrease as soil moisture values approach 0%. As soil moisture values fall below this percentage, corn will start conserving water and using soil moisture at less than optimal rates. This water conservation by the plant increases as soil moisture values decrease, leading to lower and lower daily evapotranspiration values.   Wind: Evapotranspiration takes into account wind; however, evapotranspiration is not as sensitive to wind as to the other conditions. In an example embodiment, a set wind speed of 2 meters per second is used for all evapotranspiration calculations.   

     Alerts and Reporting 
     The agricultural intelligence computer system is additionally configured to provide alerts based on weather and field-related information. Specifically, the user may define a plurality of thresholds for each of a plurality of alert categories. When field condition data indicates that the thresholds have been exceeded, the user device will receive alerts. Alerts may be provided via the application (e.g., notification upon login, push notification), email, text messages, or any other suitable method. Alerts may be defined for crop cultivation monitoring, for example, hail size, rainfall, overall precipitation, soil moisture, crop scouting, wind conditions, field image, pest reports or disease reports. Alternately, alerts may be provided for crop growth strategy. For example, alerts may be provided based on commodity prices, grain prices, workability indexes, growth stages, and crop moisture content. In some examples, an alert may indicate a recommended course of action. For example, the alert may recommend that field activities (e.g., planting, nitrogen application, pest and disease treatment, irrigation application, scouting, or harvesting) occur within a particular period of time. The agricultural intelligence computer system is also configured to receive information on farming activities from, for example, the user device, an agricultural machine and/or agricultural machine computing device, or any other source. Accordingly, alerts may also be provided based on logged farm activity such as planting, nitrogen application, spraying, irrigation, scouting, or harvesting. In some examples, alerts may be provided regardless of thresholds to indicate certain field conditions. In one example, a daily precipitation, growth stage, field image or temperature alert may be provided to the user device. 
     The agricultural intelligence computer system is further configured to generate a plurality of reports based on field condition data. Such reports may be used by the user to improve strategy and decision-making in farming. The reports may include reports on crop growth stage, temperature, humidity, soil moisture, precipitation, workability, pest risk, and disease risk. The reports may also include one or more field definition data, environmental data, field-specific data, scouting and logging events, field condition data, summary of agricultural intelligence services or FSA Form 578. 
     Scouting and Notes 
     The agricultural intelligence computer system is also configured to receive supplemental information from the user device. For example, a user may provide logging or scouting events regarding the fields associated with the field definition data. The user may access a logging application at the user device and update the agricultural intelligence computer system. In one embodiment, the user accesses the agricultural intelligence computer system via a user device while being physically located in a field to enter field-specific data. The agricultural intelligence computer system might automatically display and transmit the date and time and field definition data associated with the field-specific data, such as geographic coordinates and boundaries. The user may provide general data for activities including field, location, date, time, crop, images, and notes. The user may also provide data specific to particular activities such as planting, nitrogen application, pesticide application, harvesting, scouting, and current weather observations. Such supplemental information may be associated with the other data networks and used by the user for analysis. 
     The agricultural intelligence computer system is additionally configured to display scouting and logging events related to the receipt of field-specific data from the user via one or more agricultural machines or agricultural machine devices that interacts with the agricultural intelligence computer system or via the user device. Such information can be displayed as specified by the user. In one example, the information is displayed on a calendar on the user device, wherein the user can obtain further details regarding the information as necessary. In another example, the information is displayed in a table on the user device, wherein the user can select the specific categories of information that the user would like displayed. 
     The agricultural intelligence computer system also includes (or is in data communication with) a plurality of modules configured to analyze field condition data and other data available to the agricultural intelligence computer system and to recommend certain agricultural actions (or activities) to be performed relative to the fields being analyzed in order to maximize yield and/or revenue for the particular fields. In other words, such modules review field condition data and other data to recommend how to effectively enhance output and performance of the particular fields. The modules may be variously referred to as agricultural intelligence modules or, alternately as recommendation advisor components or agricultural intelligence services. As used herein, such agricultural intelligence modules may include, but are not limited to a) planting advisor module, b) nitrogen application advisor module, c) pest advisor module, d) field health advisor module, e) harvest advisor module, and f) revenue advisor module. 
     Agricultural Intelligence Services 
     Planting Advisor Module 
     The agricultural intelligence computer system is additionally configured to provide agricultural intelligence services related to planting. In one example embodiment, a planting advisor module provides planting date recommendations. The recommendations are specific to the location of the field and adapt to the current field condition data, along with weather predicted to be experienced by the specific fields. 
     In one embodiment, the planting advisor module receives one or more of the following data points for each field identified by the user (as determined from field definition data) in order to determine and provide such planting date recommendations:
     1. A first set of data points is seed characteristic data. Seed characteristic data may include any relevant information related to seeds that are planted or will be planted. Seed characteristic data may include, for example, seed company data, seed cost data, seed population data, seed hybrid data, seed maturity level data, seed disease resistance data, and any other suitable seed data. Seed company data may refer to the manufacturer or provider of seeds. Seed cost data may refer to the price of seeds for a given quantity, weight, or volume of seeds. Seed population data may include the amount of seeds planted (or intended to be planted) or the density of seeds planted (or intended to be planted). Seed hybrid data may include any information related to the biological makeup of the seeds (i.e., which plants have been hybridized to form a given seed.) Seed maturity level data may include, for example, a relative maturity level of a given seed (e.g., a comparative relative maturity (“CRM”) value or a silk comparative relative maturity (“silk CRM”)), growing degree units (“GDUs”) until a given stage such as silking, mid-pollination, black layer, or flowering, and a relative maturity level of a given seed at physiological maturity (“Phy. CRM”). Disease resistance data may include any information related to the resistance of seeds to particular diseases. In the example embodiment, disease resistance data includes data related to the resistance to Gray Leaf Spot, Northern Leaf Blight, Anthracnose Stalk Rot, Goss&#39;s Wilt, Southern Corn Leaf Blight, Eyespot, Common Rust, Anthracnose Leaf Blight, Southern Rust, Southern Virus Complex, Stewart&#39;s Leaf Blight, Corn Lethal Necrosis, Headsmut, Diplodia Ear Rot, and Fusarium Crown Rot. Other suitable seed data may include, for example, data related to, grain drydown, stalk strength, root strength, stress emergence, staygreen, drought tolerance, ear flex, test eight, plant height, ear height, mid-season brittle stalk, plant vigor, fungicide response, growth regulators sensitivity, pigment inhibitors, sensitivity, sulfonylureas sensitivity, harvest timing, kernel texture, emergence, harvest appearance, harvest population, seedling growth, cob color, and husk cover.   2. A second set of data points is field-specific data related to soil composition. Such field-specific data may include measurements of the acidity or basicity of soil (e.g., pH levels), soil organic matter levels (“OM” levels), and cation exchange capacity levels (“CEC” levels).   3. A third set of data points is field-specific data related to field data. Such field-specific data may include field names and identifiers, soil types or classifications, tilling status, irrigation status.   4. A fourth set of data points is field-specific data related to historical harvest data. Such field-specific data may include crop type or classification, harvest date, actual production history (“APH”), yield, grain moisture, and tillage practice.
 
In some examples, users may be prompted at the user device to provide a fifth set of data points by answering questions regarding desired planting population (e.g., total crop volume and total crop density for a particular field) and/or seed cost, expected yield, and indication of risk preference (e.g., general or specific: user is willing to risk a specific number of bushels per acre to increase the chance of producing a specific larger number of bushels per acre) if such information has not already been provided to the agricultural intelligence computer system.
   

     The planting advisor module receives and processes the sets of data points to simulate possible yield potentials. Possible yield potentials are calculated for various planting dates. The planting advisor module additionally utilizes additional data to generate such simulations. The additional data may include simulated weather between the planting data and harvesting date, field workability, seasonal freeze risk, drought risk, heat risk, excess moisture risk, estimated soil temperature, and/or risk tolerance. The likely harvesting date may be estimated based upon the provided relative maturity (e.g., to generate an earliest recommended harvesting date) and may further be adjusted based upon predicted weather and workability. Risk tolerance may be calculated based for a high profit/high risk scenario, a low risk scenario, a balanced risk/profit scenario, and a user defined scenario. The planting advisor module generates such simulations for each planting date and displays a planting date recommendation for the user on the user device. The recommendation includes the recommended planting date, projected yield, relative maturity, and graphs the projected yield against planting date. In some examples, the planting advisor module also graphs planting dates against the projected yield loss resulting from spring freeze risk, fall freeze risk, drought risk, heat risk, excess moisture risk, and estimated soil temperature. In some examples, such graphs are generated based on the predicted temperatures and/or precipitation between each planting date and a likely or earliest recommended harvest date for the selected relative maturity. The planting advisor module provides the option of modeling and displaying alternative yield scenarios for planting data and projected yield by modifying one or more data points associated with seed characteristic data, field-specific data, desired planting population and/or seed cost, expected yield, and/or indication of risk preference. The alternative yield scenarios may be displayed and graphed on the user device along with the original recommendation. 
     In some examples, the planting advisor module recommends or excludes planting dates based on predicted workability. For example, dates at which a predicted planting-specific workability value is “Stop” may either be excluded or not recommended. In some examples, the planting advisor recommends or excludes planting dates based upon predicted weather events (e.g., temperature or precipitation). For examples, planting dates may be recommended after which which likelihood of freezing is lower than associated threshold values. 
     In some examples, the planting advisor recommends seed characteristics or graphs estimated yield against planting date for various seed characteristics. For example, a graph of estimated yield against planting date may be generated for both the seed characteristic and a recommended seed characteristic. The recommended seed characteristic may be recommended based on any of the maximum yield at any planting date, the maximum average yield across a set of planting dates, or the earliest possible harvesting date (e.g., where a later harvesting date is not desired due to predicted weather, a relative maturity may be selected in order to enable a desired harvesting date). 
     Nitrogen Application Advisor Module 
     The agricultural intelligence computer system is additionally configured to provide agricultural intelligence services related to soil. The nitrogen application advisor module determines potential needs for nitrogen in the soil and recommends nitrogen application practices to a user. More specifically, the nitrogen application advisor module is configured to identify conditions when crop needs cannot be met by nitrogen present in the soil. In one example embodiment, a nitrogen application advisor module provides recommendations for sidedressing or spraying, such as date and rate, specific to the location of the field and adapted to the current field condition data. In one embodiment, the nitrogen application advisor module is configured to receive one or more of the following data points for each field identified by the user (as determined from field definition data):
     1. A first set of data points includes environmental information. Environmental information may include information related to weather, precipitation, meteorology, soil and crop phenology.   2. A second set of data points includes field-specific data related to field data. Such field-specific data may include field names and identifiers, soil types or classifications, tilling status, irrigation status.   3. A third set of data points includes field-specific data related to historical harvest data. Such field-specific data may include crop type or classification, harvest date, actual production history (“APH”), yield, grain moisture, and tillage practice.   4. A fourth set of data points is field-specific data related to soil composition. Such field-specific data may include measurements of the acidity or basicity of soil (e.g., pH levels), soil organic matter levels (“OM” levels), and cation exchange capacity levels (“CEC” levels).   5. A fifth set of data points is field-specific data related to planting data. Such field-specific data may include planting date, seed type or types, relative maturity (RM) levels of planted seed(s), and seed population. In some examples, the planting data is transmitted from a planter monitor to the agricultural intelligence computer system  150 , e.g., via a cellular modem or other data communication device of the planter monitor.   6. A sixth set of data points is field-specific data related to nitrogen data. Such field-specific data may include nitrogen application dates, nitrogen application amounts, and nitrogen application sources.   7. A seventh set of data points is field-specific data related to irrigation data. Such field-specific data may include irrigation application dates, irrigation amounts, and irrigation sources.   

     Based on the sets of data points, the nitrogen application advisor module determines a nitrogen application recommendation. As described below, the recommendation includes a list of fields with adequate nitrogen, a list of fields with inadequate nitrogen, and a recommended nitrogen application for the fields with inadequate nitrogen. 
     In some examples, users may be prompted at the user device to answer questions regarding nitrogen application (e.g., side-dressing, spraying) practices and costs, such as type of nitrogen (e.g., Anhydrous Ammonia, Urea, UAN (Urea Ammonium Nitrate) 28%, 30% or 32%, Ammonium Nitrate, Ammonium Sulphate, Calcium Ammonium Sulphate), nitrogen costs, latest growth stage of crop at which nitrogen can be applied, application equipment, labor costs, expected crop price, tillage practice (e.g., type (conventional, no till, reduced, strip) and amount of surface of the field that has been tilled), residue (the amount of surface of the field covered by residue), related farming practices (e.g., manure application, nitrogen stabilizers, cover crops) as well as prior crop data (e.g., crop type, harvest date, Actual Production History (APH), yield, tillage practice), current crop data (e.g., planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population), soil characteristics (pH, OM, CEC) if such information has not already been provided to the agricultural intelligence computer system. For certain questions, such as latest growth stage of crop at which nitrogen can be applied, application equipment, labor costs, the user has the option to provide a plurality of alternative responses to that the agricultural intelligence computer system can optimize the nitrogen application advisor recommendation. 
     Using the environmental information, field-specific data, nitrogen application practices and costs, prior crop data, current crop data, and/or soil characteristics, the agricultural intelligence computer system identifies the available nitrogen in each field and simulates possible nitrogen application practices, dates, rates, and next date on which workability for a nitrogen application is “Green” taking into account predicted workability and nitrogen loss through leaching, denitrification and volatilization. The nitrogen application advisor module generates and displays on the user device a nitrogen application recommendation for the user. The recommendation includes:
     1. The list of fields having enough nitrogen, including for each field the available nitrogen, last application data, and the last nitrogen rate applied.   2. The list of fields where nitrogen application is recommended, including for each field the available nitrogen, recommended application practice, recommended application dates, recommended application rate, and next data on which workability for the nitrogen application is “Green.”   

     The user has the option of modeling (i.e., running a model) and displaying nitrogen lost (total and divided into losses resulting from volatilization, denitrification, and leaching) and crop use (“uptake”) of nitrogen over a specified time period (predefined or as defined by the user) for the recommended nitrogen application versus one or more alternative scenarios based on a custom application practice, date and rate entered by the user. The user has the option of modeling and displaying estimated return on investment for the recommended nitrogen application versus one or more alternative scenarios based on a custom application practice, date and rate entered by the user. The alternative nitrogen application scenarios may be displayed and graphed on the user device along with the original recommendation. The user has the further option of modeling and displaying estimated yield benefit (minimum, average, and maximum) for the recommended nitrogen application versus one or more alternative scenarios based on a custom application practice, date and rate entered by the user. The user has the further option of modeling and displaying estimated available nitrogen over any time period specified by the user for the recommended nitrogen application versus one or more alternative scenarios based on a custom application practice, date and rate entered by the user. The user has the further option of running the nitrogen application advisor (using the nitrogen application advisor) for one or more sub-fields or management zones within a field. 
     Pest Advisor Module (or Pest and Disease Advisor Module) 
     The agricultural intelligence computer system is additionally configured to provide agricultural intelligence services related to pest and disease. The pest and disease advisor module is configured to identify risks posed to crops by pest damage and/or disease damage. In an example embodiment, the pest and disease advisor module identifies risks caused by the pests that cause that the most economic damage to crops in the U.S. Such pests include, for example, corn rootworm, corn earworm, soybean aphid, western bean cutworm, European corn borer, armyworm, bean leaf beetle, Japanese beetle, and twospotted spider mite. In some examples, the pest and disease advisor provides supplemental analysis for each pest segmented by growth stages (e.g., larval and adult stages). The pest and disease advisor module also identifies disease risks caused by the diseases that cause that the most economic damage to crops in the U.S. Such diseases include, for example, Gray Leaf Spot, Northern Leaf Blight, Anthracnose Stalk Rot, Goss&#39;s Wilt, Southern Corn Leaf Blight, Eyespot, Common Rust, Anthracnose Leaf Blight, Southern Rust, Southern Virus Complex, Stewart&#39;s Leaf Blight, Corn Lethal Necrosis, Headsmut, Diplodia Ear Rot, Fusarium Crown Rot. The pest advisor is also configured to recommend scouting practices and treatment methods to respond to such pest and disease risks. The pest advisor is also configured to provide alerts based on observations of pests in regions proximate to the user&#39;s fields. 
     In one embodiment, the pest and disease advisor may receive one or more of the following sets of data for each field identified by the user (as determined from field definition data):
     1. A first set of data points is environmental information. Environmental information includes information related to weather, precipitation, meteorology, crop phenology and pest and disease reporting.   2. A second set of data points is seed characteristic data. Seed characteristic data may include any relevant information related to seeds that are planted or will be planted. Seed characteristic data may include, for example, seed company data, seed cost data, seed population data, seed hybrid data, seed maturity level data, seed disease resistance data, and any other suitable seed data. Seed company data may refer to the manufacturer or provider of seeds. Seed cost data may refer to the price of seeds for a given quantity, weight, or volume of seeds. Seed population data may include the amount of seeds planted (or intended to be planted) or the density of seeds planted (or intended to be planted). Seed hybrid data may include any information related to the biological makeup of the seeds (i.e., which plants have been hybridized to form a given seed.) Seed maturity level data may include, for example, a relative maturity level of a given seed (e.g., a comparative relative maturity (“CRM”) value or a silk comparative relative maturity (“silk CRM”)), growing degree units (“GDUs”) until a given stage such as silking, mid-pollination, black layer, or flowering, and a relative maturity level of a given seed at physiological maturity (“Phy. CRM”). Disease resistance data may include any information related to the resistance of seeds to particular diseases. In the example embodiment, disease resistance data includes data related to the resistance to Gray Leaf Spot, Northern Leaf Blight, Anthracnose Stalk Rot, Goss&#39;s Wilt, Southern Corn Leaf Blight, Eyespot, Common Rust, Anthracnose Leaf Blight, Southern Rust, Southern Virus Complex, Stewart&#39;s Leaf Blight, Corn Lethal Necrosis, Headsmut, Diplodia Ear Rot, and Fusarium Crown Rot. Other suitable seed data may include, for example, data related to, grain drydown, stalk strength, root strength, stress emergence, staygreen, drought tolerance, ear flex, test eight, plant height, ear height, mid-season brittle stalk, plant vigor, fungicide response, growth regulators sensitivity, pigment inhibitors, sensitivity, sulfonylureas sensitivity, harvest timing, kernel texture, emergence, harvest appearance, harvest population, seedling growth, cob color, and husk cover.   3. A third set of data points is field-specific data related to planting data. Such field-specific data may include, for example, planting dates, seed type, relative maturity (RM) of planted seed, and seed population.   4. A fourth set of data points is field-specific data related to pesticide data. Such field-specific data may include, for example, pesticide application date, pesticide product type (specified by, e.g., EPA registration number), pesticide formulation, pesticide usage rate, pesticide acres tested, pesticide amount sprayed, and pesticide source.   

     In some examples, users may be prompted at the user device to answer questions regarding pesticide application practices and costs, such as type of product type, application date, formulation, rate, acres tested, amount, source, costs, latest growth stage of crop at which pesticide can be applied, application equipment, labor costs, expected crop price as well as current crop data (e.g., planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population) if such information has not already been provided to the agricultural intelligence computer system. Accordingly, the pest and disease advisor module receives such data from user devices. For certain questions, such as latest growth stage of crop at which pesticide can be applied, application equipment, labor costs, the user has the option to provide a plurality of alternative responses to that the agricultural intelligence computer system can optimize the pest and disease advisor recommendation. 
     The pest and disease advisor module is configured to receive and process all such sets of data points and received user data and simulate possible pesticide application practices. The simulation of possible pesticide practices includes, dates, rates, and next date on which workability for a pesticide application is “Green” taking into account predicted workability. The pest and disease advisor module generates and displays on the user device a scouting and treatment recommendation for the user. The scouting recommendation includes daily (or as specified by the user) times to scout for specific pests and diseases. The user has the option of displaying a specific subset of pests and diseases as well as additional information regarding a specific pest or disease. The treatment recommendation includes the list of fields where a pesticide application is recommended, including for each field the recommended application practice, recommended application dates, recommended application rate, and next data on which workability for the pesticide application is “Green.” The user has the option of modeling and displaying estimated return on investment for the recommended pesticide application versus one or more alternative scenarios based on a custom application practice, date and rate entered by the user. The alternative pesticide application scenarios may be displayed and graphed on the user device along with the original recommendation. The user has the further option of modeling and displaying estimated yield benefit (minimum, average, and maximum) for the recommended pesticide application versus one or more alternative scenarios based on a custom application practice, date and rate entered by the user. 
     Field Health Advisor Module 
     The field health advisor module identifies crop health quality over the course of the season and uses such crop health determinations to recommend scouting or investigation in areas of poor field health. More specifically, the field health advisor module receives and processes field image data to determine, identify, and provide index values of biomass health. The index values of biomass health may range from zero (indicating no biomass) to 1 (indicating the maximum amount of biomass). In an example embodiment, the index value has a specific color scheme, so that every image has a color-coded biomass health scheme (e.g., brown areas show the areas in the field with the lowest relative biomass health). In one embodiment, the field health advisor module may receive one or more of the following data points for each field identified by the user (as determined from field definition data):
     1. A first set of data points includes environmental information. Such environmental information includes information related to satellite imagery, aerial imagery, terrestrial imagery and crop phenology.   2. A second set of data points includes field-specific data related to field data. Such field-specific data may include field and soil identifiers such as field names, and soil types.   3. A third set of data points includes field-specific data related to soil composition data. Such field-specific data may include measurements of the acidity or basicity of soil (e.g., pH levels), soil organic matter levels (“OM” levels), and cation exchange capacity levels (“CEC” levels).   4. A fourth set of data points includes field-specific data related to planting data. Such field-specific data may include, for example, planting dates, seed type, relative maturity (RM) of planted seed, and seed population.   

     The field health advisor module receives and processes all such data points (along with field image data) to determine and identify a crop health index for each location in each field identified by the user each time a new field image is available. In an example embodiment, the field health advisor module determines a crop health index as a normalized difference vegetation index (“NDVI”) based on at least one near-infrared (“NIR”) reflectance value and at least one visible spectrum reflectance value at each raster location in the field. In another example embodiment, the crop health index is a NDVI based on multispectral reflectance. 
     The field health advisor module generates and displays on the user device the health index map as an overlay on an aerial map for each field identified by the user. In an example embodiment, for each field, the field health advisor module will display field image date, growth stage of crop at that time, soil moisture at that time, and health index map as an overlay on an aerial map for the field. In an example embodiment, the field image resolution is between 5 m and 0.25 cm. The user has the option of modeling and displaying a list of fields based on field image date and/or crop health index (e.g., field with lowest overall health index values to field with highest overall health index values, field with highest overall health index values to field with lowest overall health index values, lowest health index value variability within field, highest health index value variability within field, or as specified by the user). The user also has the option of modeling and displaying a comparison of crop health index for a field over time (e.g., side-by-side comparison, overlay comparison). In an example embodiment, the field health advisor module provides the user with the ability to select a location on a field to get more information about the health index, soil type or elevation at a particular location. In an example embodiment, the field health advisor module provides the user with the ability to save a selected location, the related information, and a short note so that the user can retrieve the same information on the user device while in the field. 
     A technical effect of the systems and methods described herein include at least one of (a) improved utilization of agricultural fields through improved field condition monitoring; (b) improved selection of time and method of fertilization; (c) improved selection of time and method of pest control; (d) improved selection of seeds planted for the given location of soil; (e) improved field condition data for at a micro-local level; and (f) improved selection of time of harvest. 
     More specifically, the technical effects can be achieved by performing at least one of the following steps: (a) receiving a plurality of field definition data, retrieving a plurality of input data from a plurality of data networks, determining a field region based on the field definition data, identifying a subset of the plurality of input data associated with the field region, determining a plurality of field condition data based on the subset of the plurality of input data, and providing the plurality of field condition data to the user device; (b) defining a precipitation analysis period, retrieving a set of recent precipitation data, a set of predicted precipitation data, and a set of temperature data associated with the precipitation analysis period from the subset of the plurality of input data, determining a workability index based on the set of recent precipitation data, the set of predicted precipitation data, and the set of temperature data, and providing a workability value to the user device based on the workability index; (c) receiving a prospective field activity, and determining the workability index based partially on the prospective field activity; (d) determining an initial crop moisture level, receiving a plurality of daily high and low temperatures, receiving a plurality of crop water usage, and determining a soil moisture level; (e) receiving a plurality of alert preferences from the user device, identifying a plurality of alert thresholds associated with the plurality of alert preferences, monitoring the subset of the plurality of input data, and alerting the user device when at least one of the alert thresholds is exceeded; (f) receiving a plurality of field definition data from at least one of a user device and an agricultural machine device; (g) identifying a grid associated with the field region, identifying, from a plurality of weather stations associated with the grid, wherein each of the plurality of weather stations is associated with a weather station location, identifying an associated weight for each of the plurality of weather stations based on each associated weather station location, receiving a temperature reading from each of the plurality of weather stations, and identifying a temperature value for the field region based on the plurality of temperature readings and each associated weight; (h) receiving a plurality of field definition data, retrieving a plurality of input data from a plurality of data networks, determining a field region based on the field definition data, identifying a subset of the plurality of input data associated with the field region, determining a plurality of field condition data based on the subset of the plurality of input data, identifying a plurality of field activity options, determining a recommendation score for each of the plurality of field activity options based at least in part on the plurality of field condition data, and providing a recommended field activity option from the plurality of field activity options based on the plurality of recommendation scores; (i) defining a precipitation analysis period, retrieving a set of recent precipitation data, a set of predicted precipitation data, and a set of temperature data associated with the precipitation analysis period from the subset of the plurality of input data, determining a workability index based on the set of recent precipitation data, the set of predicted precipitation data, and the set of temperature data, and identifying a recommended agricultural activity based, at least in part, on the workability index; (j) determining an initial crop moisture level, receiving a plurality of daily high and low temperatures, receiving a plurality of crop water usage, determining a soil moisture level for the field region, and identifying a plurality of crops to recommend based on the determined soil moisture level; (k) determining an expected heat unit value for the field region based on the input data, receiving a plurality of crop options considered for planting, wherein each of the plurality of crop options includes crop data, determining a relative maturity for each of the plurality of crop options based on the expected heat unit value and the crop data, and recommending a selected crop from the plurality of crop options based on the relative maturity for each of the plurality of crop options; (l) receiving a plurality of pest risk data wherein each of the plurality of pest risk data includes a pest identifier and a pest location, receiving a plurality of crop identifiers associated with a plurality of crops, receiving a plurality of pest spray information associated with the crop identifiers, determining a pest risk assessment associated with each of the plurality of crops, and recommending a spray strategy based on the plurality of pest risk assessments; (m) receiving a plurality of historical agricultural activities associated with each of the field region from a user device, and providing a recommended field activity option based at least in part on the plurality of historical agricultural activities; and (n) utilizing a grid-based model to obtain localized field condition data. 
     As used herein, a processor may include any programmable system including systems using micro-controllers, reduced instruction set circuits (RISC), application specific integrated circuits (ASICs), logic circuits, and any other circuit or processor capable of executing the functions described herein. The above examples are example only, and are thus not intended to limit in any way the definition and/or meaning of the term “processor.” 
     As used herein, the term “database” may refer to either a body of data, a relational database management system (RDBMS), or to both. As used herein, a database may include any collection of data including hierarchical databases, relational databases, flat file databases, object-relational databases, object oriented databases, and any other structured collection of records or data that is stored in a computer system. The above examples are example only, and thus are not intended to limit in any way the definition and/or meaning of the term database. Examples of RDBMS&#39;s include, but are not limited to including, Oracle® Database, MySQL, IBM® DB2, Microsoft® SQL Server, Sybase®, and PostgreSQL. However, any database may be used that enables the systems and methods described herein. (Oracle is a registered trademark of Oracle Corporation, Redwood Shores, Calif.; IBM is a registered trademark of International Business Machines Corporation, Armonk, N.Y.; Microsoft is a registered trademark of Microsoft Corporation, Redmond, Wash.; and Sybase is a registered trademark of Sybase, Dublin, Calif.) 
     In one embodiment, a computer program is provided, and the program is embodied on a computer readable medium. In an example embodiment, the system is executed on a single computer system, without requiring a connection to a sever computer. In a further embodiment, the system is being run in a Windows® environment (Windows is a registered trademark of Microsoft Corporation, Redmond, Wash.). In yet another embodiment, the system is run on a mainframe environment and a UNIX® server environment (UNIX is a registered trademark of X/Open Company Limited located in Reading, Berkshire, United Kingdom). The application is flexible and designed to run in various different environments without compromising any major functionality. In some embodiments, the system includes multiple components distributed among a plurality of computing devices. One or more components may be in the form of computer-executable instructions embodied in a computer-readable medium. 
     As used herein, an element or step recited in the singular and proceeded with the word “a” or “an” should be understood as not excluding plural elements or steps, unless such exclusion is explicitly recited. Furthermore, references to “example embodiment” or “one embodiment” of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. 
     As used herein, the terms “software” and “firmware” are interchangeable, and include any computer program stored in memory for execution by a processor, including RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory. The above memory types are example only, and are thus not limiting as to the types of memory usable for storage of a computer program. 
     The systems and processes are not limited to the specific embodiments described herein. In addition, components of each system and each process can be practiced independent and separate from other components and processes described herein. Each component and process also can be used in combination with other assembly packages and processes. 
     The following detailed description illustrates embodiments of the disclosure by way of example and not by way of limitation. It is contemplated that the disclosure has general application to the management and recommendation of agricultural activities. 
       FIG. 1  is a diagram depicting an example agricultural environment  100  including a plurality of fields that are monitored and managed using an agricultural intelligence computer system. Example agricultural environment  100  includes grower  110  cultivating a plurality of fields  120  including a first field  122  and a second field  124 . Grower  110  interacts with agricultural intelligence computer system  150  to effectively manage fields  120  and receive recommendations for agricultural activities to effectively utilize fields  120 . Agricultural intelligence computer system  150  utilizes a plurality of computer systems  112 ,  114 ,  116 ,  118 ,  130 A,  130 B, and  140  to provide such services. Computer systems  112 ,  114 ,  116 ,  118 ,  130 A,  130 B,  140 , and  150  and all associated sub-systems may be referred to as a “networked agricultural intelligence system.” Although only one grower  110  and only two fields  120  are shown, it should be understood that multiple growers  110  having multiple fields  120  may utilize agricultural intelligence computer system  150 . 
     In the example embodiment, grower  110  utilizes user devices  112 ,  114 ,  116 , and/or  118  to interact with agricultural intelligence computer system  150 . In one example, user device  112  is a smart watch, computer-enabled glasses, smart phone, PDA, or “phablet” computing device capable of transmitting and receiving information such as described herein. Alternately, grower  110  may utilize tablet computing device  114 , or laptop  116  to interact with agricultural intelligence computer system  150 . As user devices  112  and  114  are “mobile devices” with specific types and ranges of inputs and outputs, in at least some examples user devices  112  and  114  utilize specialty software (sometimes referred to as “apps”) to interact with agricultural intelligence computer system  150 . 
     In an example embodiment, agricultural machine  117  (e.g., combine, tractor, cultivator, plow, subsoiler, sprayer or other machinery used on a farm to help with farming) may be coupled to a computing device  118  (“agricultural machine computing device”) that interacts with agricultural intelligence computer system  150  in a similar manner as user devices  112 ,  114 , and  116 . In some examples, agricultural machine computing device  118  could be a planter monitor, planter controller or a yield monitor. In some examples, the agricultural machine computing device  118  could be a planter monitor as disclosed in U.S. Pat. No. 8,738,243, incorporated herein by reference, or in International Patent Application No. PCT/US2013/054506, incorporated herein by reference. In some examples, the agricultural machine computing device  118  could be a yield monitor as disclosed in U.S. patent application Ser. No. 14/237,844, incorporated herein by reference. Agricultural machine  117  and agricultural machine computing device  118  may provide agricultural intelligence computer system  150  with field definition data  160  and field-specific data, as described below. 
     As described below and herein, grower (or user)  110  interacts with user devices  112 ,  114 ,  116 , and/or  118  to obtain information regarding the management of fields  120 . More specifically, grower  110  interacts with user devices  112 ,  114 ,  116 , and/or  118  in order to obtain recommendations, services, and information related to the management of fields  120 . Grower  110  provides field definition data  160  descriptive of the location, layout, geography, and topography of fields  120  via user devices  112 ,  114 ,  116 , and/or  118 . In an example embodiment, grower  110  may provide field definition data  160  to agricultural intelligence computer system  150  by accessing a map (served by agricultural intelligence computer system  150 ) on user device  112 ,  114 ,  116 , and/or  118  and selecting specific CLUs that have been graphically shown on the map. In an alternative embodiment, grower  110  may identify field definition data  160  by accessing a map (served by agricultural intelligence computer system  150 ) on user device  112 ,  114 ,  116 , and/or  118  and drawing boundaries of fields  120  (or, more specifically, field  122  and field  124 ) over the map. Such CLU selection or map drawings represent geographic identifiers. In alternative embodiments, the user may identify field definition data  160  by accessing field definition data  160  (provided as shape files or in a similar format) from the U.S. Department of Agriculture Farm Service Agency or other source via the user device and providing such field definition data  160  to the agricultural intelligence computer system. The land identified by “field definition data” may be referred to as a “field” or “land tract.” As used herein, the land farmed, or “land tract”, is contained in a region that may be referred to as a “field region.” Such a “field region” may be coextensive with, for example, temperature grids or precipitation grids, as used and defined below. 
     Specifically, field definition data  160  defines the location of fields  122  and  124 . As described herein, accurate locations of fields  122  and  124  are useful in order to identify field-specific &amp; environmental data  170  and/or field condition data  180 . Significant variations may exist in field conditions over small distances including variances in, for example, soil quality, soil composition, soil moisture levels, nitrogen levels, relative maturity of crops, precipitation, wind, temperature, solar exposure, other meteorological conditions, and workability of the field. As such, agricultural intelligence computer system  150  identifies a location for each of fields  122  and  124  based on field definition data  160  and identifies a field region for each of fields  122  and  124 . As described above, in one embodiment agricultural intelligence computer system  150  utilizes a “grid” architectural model that subdivides land into grid sections that are 2.5 miles by 2.5 miles in dimension. 
     Accordingly, agricultural intelligence computer system  150  utilizes field definition data  160  to identify which field conditions and field data to process and determine for a particular field. In the example, data networks  130 A and  130 B represent data sources associated with fields  124  and  122 , respectively, because the grid associated with field  122  is monitored by external data source  130 B and the grid associated with field  124  is monitored by data network  130 A. Each of data networks  130 A and  130 B may each have associated subsystems  131 A,  132 A,  133 A,  134 A (associated with data network  130 A) and  131 B,  132 B,  133 B, and  134 B (associated with external data source  130 B). Accordingly, field definition data  160  associates field  122  with data network  130 A and field  124  with data network  130 B. Such a distinction of regions covered by an data network  130 A and  130 B is provided for illustrative purposes. In operation, data networks  130 A and  130 B may be associated with a plurality of grids and be able to provide field-specific &amp; environmental data  170  for a particular grid based on field definition data  160 . 
     Data networks  130 A and  130 B, as described herein, receive a plurality of information to determine field-specific &amp; environmental data  170 . Data networks  130 A and  130 B may receive feeds of meteorological data from other external services or be associated with meteorological devices such as anemometer  135  and rain gauge  136 . Accordingly, based on such devices  135  and  136  and other accessible data, data networks  130 A and  130 B provide field-specific &amp; environmental data  170  to agricultural intelligence computer system  150 . 
     Further, agricultural intelligence computer system may receive additional information from other data networks  140  to determine field-specific &amp; environmental data  170  and field condition data  180 . In the example, other data networks  140  receive inputs from aerial monitoring system  145  and satellite device  146 . Such inputs  145  and  146  may provide field-specific &amp; environmental data for a plurality of fields  120 . 
     Using field-specific &amp; environmental data  170  associated with each field  122  and  124  (as defined by field definition data  160 ), agricultural intelligence computer system determines field condition data  180  and/or at least one recommended agricultural activity  190 , as described herein. Field condition data  180  substantially represents a response to a request from grower  110  for information related to field conditions of fields  120  including field weather conditions, field workability conditions, growth stage conditions, soil moisture, and precipitation conditions. Recommended agricultural activity  190  includes outputs from any of the plurality of services described herein including planting advisor, a nitrogen application advisor, a pest advisor, a field health advisor, a harvest advisor, and a revenue advisor. Accordingly, recommended agricultural activity  190  may include, for example, suggestions on planting, nitrogen application, pest response, field health remediation, harvesting, and sales and marketing of crops. 
     Agricultural intelligence computer system  150  may be implemented using a variety of distinct computing devices such as agricultural intelligence computing devices  151 ,  152 ,  153 , and  154  using any suitable network. In an example embodiment, agricultural intelligence computer system  150  uses a client-server architecture configured for exchanging data over a network (e.g., the Internet) with other computer systems including systems  112 ,  114 ,  116 ,  118 ,  130 A,  130 B, and  140 . One or more user devices  112 ,  114 ,  116 , and/or  118  may communicate via a network using a suitable method of interaction including a user application (or application platform) stored on user devices  112 ,  114 ,  116 , and/or  118  or using a separate application utilizing (or calling) an application platform interface. Other example embodiments may include other network architectures, such as peer-to-peer or distributed network environment. 
     The user application may provide server-side functionality, via the network to one or more user devices  112 ,  114 ,  116 , and/or  118 . In an example embodiment, user device  112 ,  114 ,  116 , and/or  118  may access the user application via a web client or a programmatic client. User devices  112 ,  114 ,  116 , and/or  118  may transmit data to, and receive data from, from one or more front-end servers. In an example embodiment, the data may take the form of requests and user information input, such as field-specific data, into the user device. One or more front-end servers may process the user device requests and user information and determine whether the requests are service requests or content requests, among other things. Content requests may be transmitted to one or more content management servers for processing. Application requests may be transmitted to one or more application servers. In an example embodiment, application requests may take the form of a request to provide field condition data and/or agricultural intelligence services for one or more fields  120 . 
     In an example embodiment, agricultural intelligence computer system  150  may comprise one or more servers  151 ,  152 ,  153 , and  154  in communication with each other. For example, agricultural intelligence computer system  150  may comprise front-end servers  151 , application servers  152 , content management servers  153 , account servers  154 , modeling servers  155 , environmental data servers  156 , and corresponding databases  157 . As noted above, environmental data may be obtained from data networks  130 A,  130 B, and  140 , accessible by agricultural intelligence computer system  150  or such environmental data may be obtained from internal data sources or databases integrated within agricultural intelligence computer system  150 . 
     In an example embodiment, data networks  130 A,  130 B, and  140  may comprise third-party hosted servers that provide services to agricultural intelligence computer system  150  via Application Program Interface (API) requests and responses. The frequency at which agricultural intelligence computer system  150  may consume data published or made available by these third-party hosted servers  130 A,  130 B, and  140  may vary based on the type of data. In an example embodiment, a notification may be sent to the agricultural intelligence computer system when new data is available by a data source. Agricultural intelligence computer system  150  may transmit an API call via the network to servers  130 A,  130 B, and  140  hosting the data and receive the new data in response to the call. To the extent needed, agricultural intelligence computer system  150  may process the data to enable components of the agricultural intelligence computer system and user application to handle the data. For example, processing data may involve extracting data from a stream or a data feed and mapping the data to a data structure, such as an XML data structure. Data received and/or processed by agricultural intelligence computer system  150  may be transmitted to the application platform and stored in an appropriate database. 
     When an application request is made, one or more front end servers  151  communicate with applications servers  151 , content management servers  153 , account servers  154 , modeling servers  155 , environmental data servers  156 , and corresponding databases  157 . In one example, modeling servers  155  may generate a predetermined number of simulations (e.g., 10,000 simulations) using, in part, field specific data and environmental data for one or more fields identified based on field definition data and user information. Depending on the type of application request, the field-specific data and environmental data for one or more fields may be located in content management servers  153 , account servers  154 , environmental data servers  156 , corresponding databases  157 , and, in some instances, archived in modeling servers  155  and/or application servers  152 . Based on the simulations generated by modeling servers  155 , field condition data and/or agricultural intelligence services for one or more fields is provided to application servers  152  for transmission to the requesting user device  112 ,  114 ,  116 , and/or  118  via the network. More specifically, grower (or user)  110  may use user device  112 ,  114 ,  116 , and/or  118  to access a plurality of windows or displays showing field condition data and/or agricultural intelligence services, as described below. 
       FIG. 2  is a block diagram of a user computing device  202 , used for managing and recommending agricultural activities, as shown in the agricultural environment of  FIG. 1 . User computing device  202  may include, but is not limited to, smartphone  112 , tablet  114 , laptop  116 , and agricultural computing device  118  (all shown in  FIG. 1 ). Alternately, user computing device  202  may be any suitable device used by user  110 . In the example embodiment, user system  202  includes a processor  205  for executing instructions. In some embodiments, executable instructions are stored in a memory area  210 . Processor  205  may include one or more processing units, for example, a multi-core configuration. Memory area  210  is any device allowing information such as executable instructions and/or written works to be stored and retrieved. Memory area  210  may include one or more computer readable media. 
     User system  202  also includes at least one media output component  215  for presenting information to user  201 . Media output component  215  is any component capable of conveying information to user  201 . In some embodiments, media output component  215  includes an output adapter such as a video adapter and/or an audio adapter. An output adapter is operatively coupled to processor  205  and operatively coupled to an output device such as a display device, a liquid crystal display (LCD), organic light emitting diode (OLED) display, or “electronic ink” display, or an audio output device, a speaker or headphones. 
     In some embodiments, user system  202  includes an input device  220  for receiving input from user  201 . Input device  220  may include, for example, a keyboard, a pointing device, a mouse, a stylus, a touch sensitive panel, a touch pad, a touch screen, a gyroscope, an accelerometer, a position detector, or an audio input device. A single component such as a touch screen may function as both an output device of media output component  215  and input device  220 . User system  202  may also include a communication interface  225 , which is communicatively coupled to a remote device such as agricultural intelligence computer system  150 . Communication interface  225  may include, for example, a wired or wireless network adapter or a wireless data transceiver for use with a mobile phone network, Global System for Mobile communications (GSM), 3G, or other mobile data network or Worldwide Interoperability for Microwave Access (WIMAX). 
     Stored in memory area  210  are, for example, computer readable instructions for providing a user interface to user  201  via media output component  215  and, optionally, receiving and processing input from input device  220 . A user interface may include, among other possibilities, a web browser and client application. Web browsers enable users, such as user  201 , to display and interact with media and other information typically embedded on a web page or a website from agricultural intelligence computer system  150 . A client application allows user  201  to interact with a server application from agricultural intelligence computer system  150 . 
     As described herein, user system  202  may be associated with a variety of device characteristics. For example device characteristics may vary in terms of the operating system used by user device  202  in the initiating of the first transaction, the browser operating system used by user device  202  in the initiating of the first transaction, a plurality of hardware characteristics associated with user device  202  in the initiating of the first transaction, the internet protocol address associated with user device  202  in the initiating of the first transaction, the internet service provider associated with user device  202  in the initiating of the first transaction, display attributes and characteristics used by a browser used by user device  202  in the initiating of the first transaction, configuration attributes used by a browser used by user device  202  in the initiating of the first transaction, and software components used by user device  202  in the initiating of the first transaction. As further described herein, agricultural intelligence computer system  150  (shown in  FIG. 1 ) is capable of receiving device characteristic data related to user system  202  and analyzing such data as described herein. 
       FIG. 3  is a block diagram of a computing device, used for managing and recommending agricultural activities, as shown in the agricultural environment of  FIG. 1 . Server system  301  may include, but is not limited to, data network systems  130 A,  130 B, and  140  and agricultural intelligence computer system  150 . In the example embodiment, server system  301  determines and analyzes characteristics of devices used in payment transactions, as described below. 
     Server system  301  includes a processor  305  for executing instructions. Instructions may be stored in a memory area  310 , for example. Processor  305  may include one or more processing units (e.g., in a multi-core configuration) for executing instructions. The instructions may be executed within a variety of different operating systems on the server system  301 , such as UNIX, LINUX, Microsoft Windows®, etc. It should also be appreciated that upon initiation of a computer-based method, various instructions may be executed during initialization. Some operations may be required in order to perform one or more processes described herein, while other operations may be more general and/or specific to a particular programming language (e.g., C, C#, C++, Java, Python, or other suitable programming languages, etc.). 
     Processor  305  is operatively coupled to a communication interface  315  such that server system  301  is capable of communicating with a remote device such as a user system or another server system  301 . For example, communication interface  315  may receive requests from user systems  112 ,  114 ,  116 , and  118  via the Internet, as illustrated in  FIGS. 2 and 3 . 
     Processor  305  may also be operatively coupled to a storage device  330 . Storage device  330  is any computer-operated hardware suitable for storing and/or retrieving data. In some embodiments, storage device  330  is integrated in server system  301 . For example, server system  301  may include one or more hard disk drives as storage device  330 . In other embodiments, storage device  330  is external to server system  301  and may be accessed by a plurality of server systems  301 . For example, storage device  330  may include multiple storage units such as hard disks or solid state disks in a redundant array of inexpensive disks (RAID) configuration. Storage device  330  may include a storage area network (SAN) and/or a network attached storage (NAS) system. 
     In some embodiments, processor  305  is operatively coupled to storage device  330  via a storage interface  320 . Storage interface  320  is any component capable of providing processor  305  with access to storage device  330 . Storage interface  320  may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and/or any component providing processor  305  with access to storage device  330 . 
     Memory area  310  may include, but are not limited to, random access memory (RAM) such as dynamic RAM (DRAM) or static RAM (SRAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and non-volatile RAM (NVRAM). The above memory types are exemplary only, and are thus not limiting as to the types of memory usable for storage of a computer program. 
       FIG. 4  is an example data flowchart of managing and recommending agricultural activities using computing devices of  FIGS. 1, 2, and 3  in the agricultural environment shown in  FIG. 1 . As described herein, grower  110  uses any suitable user device  112 ,  114 ,  116 , and/or  118  (shown in  FIG. 1 ) to specify grower request  401  which is transmitted to agricultural intelligence computer system  150 . As described, grower  110  uses user application or application platform, served on user device  114 , to interact with agricultural intelligence computer system  150  and make any suitable grower request  401 . As described herein, grower request  401  may include a request for field condition data  180  and/or a request for a recommended agricultural activity  190 . 
     The application platform (or user application) may provide server-side functionality, via the network to one or more user devices  114 . In an example embodiment, user device  114  may access the application platform via a web client or a programmatic client. User device  114  may transmit data to, and receive data, from one or more front-end servers such as front end server  151  (shown in  FIG. 1 ). In an example embodiment, the data may take the form of grower requests  401  and user information input  402 , such as field-specific &amp; environmental data  170  (provided by grower  110 ), into user device  114 . One or more front-end servers  151  may process grower requests  401  and user information input  402  and determine whether grower requests  401  are service requests (i.e., requests for recommended agricultural activities  190 ) or content requests (i.e., requests for field condition data  180 ), among other things. Content requests may be transmitted to one or more content management servers  153  (shown in  FIG. 1 ) for processing. Application requests may be transmitted to one or more application servers  152  (shown in  FIG. 1 ). In an example embodiment, application requests may take the form of a grower request  401  to provide field condition data  180  and/or agricultural intelligence services for one or more fields  120  (shown in  FIG. 1 ). 
     In an example embodiment, the application platform may comprise one or more servers  151 ,  152 ,  153 , and  154  (shown in  FIG. 1 ) in communication with each other. For example, agricultural intelligence computer system  150  may comprise front-end servers  151 , application servers  152 , content management servers  153 , account servers  154 , modeling servers  155 , environmental data servers  156 , and corresponding databases  157  (all shown in  FIG. 1 ). Further, agricultural intelligence computer system includes a plurality of agricultural intelligence modules  158  and  159 . In the example embodiment, agricultural intelligence modules  158  and  159  are harvest advisor module  158  and revenue advisor module  159 . In further examples, planting advisor module, nitrogen application advisor module, pest and disease advisor module, and field health advisor module may be represented in agricultural intelligence computer system  150 . As noted above, environmental data may be obtained from data networks  130  and  140  accessible by agricultural intelligence computer system  150  or it may be obtained from internal data sources integrated within agricultural intelligence computer system  150 . 
     In an example embodiment, data networks  130  and  140  may comprise third-party hosted servers that provide services to agricultural intelligence computer system  150  via Application Program Interface (API) requests and responses. The frequency at which agricultural intelligence computer system  150  may consume data published or made available by these third-party hosted servers  130  and  140  may vary based on the type of data. In an example embodiment, a notification may be sent to agricultural intelligence computer system  150  when new data is made available. Agricultural intelligence computer system  150  may alternately transmit an API call via the network to external data sources  130  hosting the data and receive the new data in response to the call. To the extent needed, agricultural intelligence computer system  150  may process the data to enable components of the application platform to handle the data. For example, processing data may involve extracting data from a stream or a data feed and mapping the data to a data structure, such as an XML data structure. Data received and/or processed by agricultural intelligence computer system  150  may be transmitted to the application platform and stored in an appropriate database. 
     When an application request is made, one or more application servers  152  communicate with content management servers  153 , account servers  154 , modeling servers  155 , environmental data servers  156 , and corresponding databases  157 . In one example, modeling servers  155  may generate a predetermined number of simulations (e.g., 10,000 simulations) using, in part, field-specific &amp; environmental data  170  for one or more fields  120  identified based on field definition data  160  and user input information  402 . Depending on the type of grower request  401 , field-specific &amp; environmental data  170  for one or more fields  120  may be located in content management servers  153 , account servers  154 , modeling servers  155 , environmental data servers  156 , and corresponding databases  157 , and, in some instances, archived in the application servers  152 . Based on the simulations generated by modeling servers  155 , field condition data  180  and/or agricultural intelligence services (i.e., recommended agricultural activities  190 ) for one or more fields  120  is provided to application servers  152  for transmission to requesting user device  114  via the network. More specifically, the user may use user device  114  to access a plurality of windows or displays showing field condition data  180  and/or recommended agricultural activities  190 , as described below. 
     Although the aforementioned application platform has been configured with various exemplary embodiments above, one skilled in the art will appreciate that any configuration of servers may be possible and that example embodiments of the present disclosure need not be limited to the configurations disclosed herein. 
     In order to provide field condition data  180 , agricultural intelligence computer system  150  runs a plurality of field condition data analysis modules  410 . Field condition analysis modules include field weather data module  411  which is configured to determine weather conditions for each field  120  identified by grower  110 . Agricultural intelligence computer system  150  uses field weather data module  411  to determine field temperature, wind, humidity, and dew point. Agricultural intelligence computer system  150  also uses field weather data module  411  to determine forecasted weather conditions including field temperature, wind, humidity, and dew point for hourly projected intervals, daily projected intervals, or any interval specified by grower  110 . Field precipitation module  415 , field workability module  412 , and field growth stage module  413  also receive and process the forecasted weather conditions. Near-term forecasts are determined using a meteorological model (e.g., the Microcast model) while long-term projections are determined using historical analog simulations. 
     Agricultural intelligence computer system  150  uses grid temperatures to determine temperature values. Known research shows that using grid techniques provides more accurate temperature measurements than point-based temperature reporting. Temperature grids are typically square physical regions, typically 2.5 miles by 2.5 miles. Agricultural intelligence computer system  150  associates fields (e.g., fields  122  or  124 ) with a temperature grid that contains the field. Agricultural intelligence computer system  150  identifies a plurality of weather stations that are proximate to the temperature grid. Agricultural intelligence computer system  150  receives temperature data from the plurality of weather stations. The temperatures reported by the plurality of weather stations are weighted based on their relative proximity to the grid such that more proximate weather stations have higher weights than less proximate weather stations. Further, the relative elevation of the temperature grid is compared to the elevation of the plurality of weather stations. Temperature values reported by the plurality of weather stations are adjusted in response to the relative difference in elevation. In some examples, the temperature grid includes or is adjacent to a body of water. Bodies of water are known to cause a reduction in the temperature of an area. Accordingly, when a particular field is proximate to a body of water as compared to the weather station providing the temperature reading, the reported temperature for the field is adjusted downwards to account for the closer proximity to the body of water. 
     Precipitation values are similarly determined using precipitation grids that utilize meteorological radar data. Precipitation grids have similar purposes and characteristics as temperature grids. Specifically, agricultural intelligence computer system  150  uses available data sources such as the National Weather Service&#39;s NEXRAD Doppler radar data. Agricultural intelligence computer system  150  further validates and calibrates reported data with ground station and satellite data. In the example embodiment, the Doppler radar data is obtained for the precipitation grid. The Doppler radar data is used to determine an estimate of precipitation for the precipitation grid. The estimated precipitation is adjusted based on other data sources such as other weather radar sources, ground weather stations (e.g., rain gauges), satellite precipitation sources (e.g., the National Oceanic and Atmospheric Administration&#39;s Satellite Applications and Research), and meteorological sources. By utilizing multiple distinct data sources, more accurate precipitation tracking may be accomplished. 
     Current weather conditions and forecasted weather conditions (hourly, daily, or as specified by the user) are displayed on the user device graphically along with applicable information regarding the specific field, such as field name, crop, acreage, field precipitation, field workability, field growth stage, soil moisture, and any other field definition data or field-specific &amp; environmental data  170  that the user may specify. Such information may be displayed on the user device in one or more combinations and level of detail as specified by the user. 
     In an example embodiment, temperature can be displayed as high temperatures, average temperatures and low temperatures over time. Temperature can be shown during a specific time and/or date range and/or harvest year and compared against prior times, years, including a 5 year average, a 15 year average, a 30 year average or as specified by the user. 
     In an example embodiment, field precipitation module  415  determines and provides the amount of precipitation and/or accumulated precipitation over time. Precipitation can be shown during a specific time period and/or date range and/or harvest year and compared against prior times, years, including a 5 year average, a 15 year average, a 30 year average or as specified by the user. Precipitation can also be displayed as past and future radar data. In an example embodiment, past radar may be displayed over the last 1.5 hours or as specified by the user. Future radar may be displayed over the next 6 hours or as specified by the user. Radar may be displayed as an overlay of an aerial image map showing the user&#39;s one or more fields where the user has the ability to zoom in and out of the map. Radar can be displayed as static at intervals selected by the user or continuously over intervals selected by the user. The underlying radar data received and/or processed by the agricultural intelligence computer system may be in the form of Gridded Binary (GRIB) files that includes forecast reflectivity files, precipitation type, and precipitation-typed reflectivity values. 
     As part of field condition data  180  provided, agricultural intelligence computer system  150  runs or executes field workability data module  412 , which processes field-specific &amp; environmental data  170  and user information output  402  to determine the degree to which a field or section of a field (associated with the field definition data) may be worked for a given time of year using machinery or other implements. In an example embodiment, agricultural intelligence computer system  150  retrieves field historical precipitation data over a predetermined period of time, field predicted precipitation over a predetermined period of time, and field temperatures over a predetermined period of time. The retrieved data is used to determine one or more workability index as determined by field workability data module  412 . 
     In an example embodiment, the workability index may be used to derive three values of workability for particular farm activities. The value of “Good” workability indicates high likelihood that field conditions are acceptable for use of machinery or a specified activity during an upcoming time interval. The value of “Check” workability indicates that field conditions may not be ideal for the use of machinery or a specified activity during an upcoming time interval. The value of “Stop” workability indicates that field conditions are not suitable for work or a specified activity during an upcoming time interval. 
     Determined values of workability may vary depending upon the farm activity. For example, planting and tilling typically require a low level of muddiness and may require a higher workability index to achieve a value of “Good” than activities that allow for a higher level of muddiness. In some embodiments, workability indices are distinctly calculated for each activity based on a distinct set of factors. For example, a workability index for planting may correlate to predicted temperature over the next 60 hours while a workability index for harvesting may be correlated to precipitation alone. In some examples, user may be prompted at the user device to answer questions regarding field activities if such information has not already been provided to agricultural intelligence computer system  150 . For example, a user may be asked what field activities are currently in use. Depending upon the response, agricultural intelligence computer system  150  may adjust its calculations of the workability index because of the user&#39;s activities, thereby incorporating the feedback of the user into the calculation of the workability index. Alternately, agricultural intelligence computer system  150  may adjust the recommendations made to the user for activities. In a further example, agricultural intelligence computer system  150  may recommend that the user stop such activities based on the responses. 
     As part of field condition data  180  provided, agricultural intelligence computer system  150  runs or executes field growth stage data module  413  (e.g., for corn, vegetative (VE-VT) and reproductive (R1-R6) growth stages). Field growth stage data module  413  receives and processes field-specific &amp; environmental data  170  and user information input  402  to determine timings of key farming decisions. Agricultural intelligence computer system  150  computes crop progression for each crop through stages of growth (agronomic stages) by tracking the impact of weather on the phenomenological development of the crop from planting through harvest. 
     In the example embodiment, agricultural intelligence computer system  150  uses the planting date entered by the user device. Alternately, agricultural intelligence computer system  150  may estimate the planting date using a system algorithm. Specifically, the planting date may be estimated based on agronomic stage data and planting practices in the region associated with the field definition data. The planting practices may be received from a data service such as a university data network that monitors typical planting techniques for a region. Agricultural intelligence computer system  150  further uses data regarding the user&#39;s farming practices within the current season and for historical seasons, thereby facilitating historical analysis. Agricultural intelligence computer system  150  determines a relative maturity value of the crops based on expected heat units over the growing season in light of the planting date, the user&#39;s farming practices, and field-specific &amp; environmental data  170 . As heat is a proxy for energy received by crops, agricultural intelligence computer system  150  calculates expected heat units for crops and determines a development of maturity of the crops. 
     As part of field condition data  180  provided, agricultural intelligence computer system  150  uses and executes soil moisture data module  414 . Soil moisture data module  414  is configured to determine the percent of total water capacity available to the crop that is present in the soil of the field. Soil moisture data module  414  initializes output at the beginning of the growing season based on environmental data in agricultural intelligence computer system  150  at that time, such as data from the North American Land Data Assimilation System, and field-specific &amp; environmental data  170 . 
     Soil moisture values are then adjusted, at least daily, during the growing season by tracking moisture entering the soil via precipitation and moisture leaving the soil via evapotranspiration (ET). Precipitation excludes a calculated amount of water that never enters the soil because it is lost as runoff. A runoff value is determined based on the precipitation amount over time and a curve determined by the USDA classification of soil type. The agricultural intelligence computer systems accounts for a user&#39;s specific field-specific &amp; environmental data  170  related to soil to determine runoff and the runoff curve for the specific field. Lighter, sandier soils allow greater precipitation water infiltration and experience less runoff during heavy precipitation events than heavier, more compact soils. Heavier or denser soil types have lower precipitation infiltration rates and lose more precipitation to runoff on days with large precipitation events. 
     Daily evapotranspiration associated with a user&#39;s specific field is calculated based on a version of the standard Penman-Monteith ET model. The total amount of water that is calculated as leaving the soil through evapotranspiration on a given day is based on the following:
     Maximum and minimum temperatures for the day: Warmer temperatures result in greater evapotranspiration values than cooler temperatures.   Latitude: During much of the corn growing season, fields at more northern latitudes experience greater solar radiation than fields at more southern latitudes due to longer days. But fields at more northern latitudes also get reduced radiation due to earth tilting. Areas with greater net solar radiation values will have relatively higher evapotranspiration values than areas with lower net solar radiation values.   Estimated crop growth stage: Growth stages around pollination provide the highest potential daily evapotranspiration values while growth stages around planting and late in grain fill result in relatively lower daily evapotranspiration values, because the crop uses less water in these stages of growth.   Current soil moisture: The agricultural intelligence computer system&#39;s model accounts for the fact that crops conserve and use less water when less water is available in the soil. The reported soil moisture values reported that are above a certain percentage, determined by crop type, provide the highest potential evapotranspiration values and potential evapotranspiration values decrease as soil moisture values approach 0%. As soil moisture values fall below this percentage, corn will start conserving water and using soil moisture at less than optimal rates. This water conservation by the plant increases as soil moisture values decrease, leading to lower and lower daily evapotranspiration values.   Wind: Evapotranspiration takes into account wind; however, evapotranspiration is not as sensitive to wind as to the other conditions. In an example embodiment, a set wind speed of 2 meters per second is used for all evapotranspiration calculations.   

     Agricultural intelligence computer system  150  is additionally configured to provide alerts based on weather and field-related information. Specifically, the user may define a plurality of thresholds for each of a plurality of alert categories. When field condition data indicates that the thresholds have been exceeded, the user device will receive alerts. Alerts may be provided via the application (e.g., notification upon login, push notification), email, text messages, or any other suitable method. Alerts may be defined for crop cultivation monitoring, for example, hail size, rainfall, overall precipitation, soil moisture, crop scouting, wind conditions, field image, pest reports or disease reports. Alternately, alerts may be provided for crop growth strategy. For example, alerts may be provided based on commodity prices, grain prices, workability indexes, growth stages, and crop moisture content. In some examples, an alert may indicate a recommended course of action. For example, the alert may recommend that field activities (e.g., planting, nitrogen application, pest and disease treatment, irrigation application, scouting, or harvesting) occur within a particular period of time. Agricultural intelligence computer system  150  is also configured to receive information on farming activities from, for example, the user device, an agricultural machine, or any other source. Accordingly, alerts may also be provided based on logged farm activity such as planting, nitrogen application, spraying, irrigation, scouting, or harvesting. In some examples, alerts may be provided regardless of thresholds to indicate certain field conditions. In one example, a daily precipitation, growth stage, field image or temperature alert may be provided to the user device. 
     Agricultural intelligence computer system  150  is further configured to generate a plurality of reports based on field condition data  180 . Such reports may be used by the user to improve strategy and decision-making in farming. The reports may include reports on crop growth stage, temperature, humidity, soil moisture, precipitation, workability, and pest risk. The reports may also include one or more field definition data  160 , field-specific &amp; environmental data  170 , scouting and logging events, field condition data  180 , summary of agricultural intelligence services (e.g., recommended agricultural activities  190 ) or FSA Form 578. 
     Agricultural intelligence computer system  150  is also configured to receive supplemental information from the user device. For example, a user may provide logging or scouting events regarding the fields associated with the field definition data. The user may access a logging application at the user device and update agricultural intelligence computer system  150 . In one embodiment, the user accesses agricultural intelligence computer system  150  via a user device while being physically located in a field to enter field-specific data. The agricultural intelligence computer system might automatically display and transmit the date and time and field definition data associated with the field-specific data, such as geographic coordinates and boundaries. The user may provide general data for activities including field, location, date, time, crop, images, and notes. The user may also provide data specific to particular activities such as planting, nitrogen application, pesticide application, harvesting, scouting, and current weather observations. Such supplemental information may be associated with the other data networks and used by the user for analysis. 
     Agricultural intelligence computer system  150  is additionally configured to display scouting and logging events related to the receipt of field-specific data from the user via one or more agricultural machines or agricultural machine devices that interacts with agricultural intelligence computer system  150  or via the user device. Such information can be displayed as specified by the user. In one example, the information is displayed on a calendar on the user device, wherein the user can obtain further details regarding the information as necessary. In another example, the information is displayed in a table on the user device, wherein the user can select the specific categories of information that the user would like displayed. 
     Agricultural Intelligence Modules  420   
     Planting Advisor Module  421   
     Agricultural intelligence computer system  150  is additionally configured to provide agricultural intelligence services related to planting. More specifically, agricultural intelligence computer system  150  includes a plurality of agricultural intelligence modules  420  (or agricultural activity modules) that may be used to determine recommended agricultural activities  190  which are provided to grower  110 . In at least some examples, agricultural intelligence modules  420  may be similar to agricultural intelligence modules  158  and  159  (shown in  FIG. 1 ). In at least some examples, planting advisor module  421  may be similar to agricultural intelligence modules  158  and  159  (shown in  FIG. 1 ). Such agricultural intelligence modules  420  may be referred to as agricultural intelligence services and may include planting advisor module  421 , nitrogen application advisor module  422 , pest advisor module  423 , field health advisor module  424 , and harvest advisor module  425 . In one example embodiment, planting advisor module  421  processes field-specific &amp; environmental data  170  and user information input  402  to determine and provide planting date recommendations. The recommendations are specific to the location of the field and adapt to the current field condition data. 
     In one embodiment, planting advisor module  421  receives one or more of the following data points for each field identified by the user (as determined from field definition data) in order to determine and provide such planting date recommendations:
     1. A first set of data points is seed characteristic data. Seed characteristic data may include any relevant information related to seeds that are planted or will be planted. Seed characteristic data may include, for example, seed company data, seed cost data, seed population data, seed hybrid data, seed maturity level data, seed disease resistance data, and any other suitable seed data. Seed company data may refer to the manufacturer or provider of seeds. Seed cost data may refer to the price of seeds for a given quantity, weight, or volume of seeds. Seed population data may include the amount of seeds planted (or intended to be planted) or the density of seeds planted (or intended to be planted). Seed hybrid data may include any information related to the biological makeup of the seeds (i.e., which plants have been hybridized to form a given seed.) Seed maturity level data may include, for example, a relative maturity level of a given seed (e.g., a comparative relative maturity (“CRM”) value or a silk comparative relative maturity (“silk CRM”)), growing degree units (“GDUs”) until a given stage such as silking, mid-pollination, black layer, or flowering, and a relative maturity level of a given seed at physiological maturity (“Phy. CRM”). Disease resistance data may include any information related to the resistance of seeds to particular diseases. In the example embodiment, disease resistance data includes data related to the resistance to Gray Leaf Spot, Northern Leaf Blight, Anthracnose Stalk Rot, Goss&#39;s Wilt, Southern Corn Leaf Blight, Eyespot, Common Rust, Anthracnose Leaf Blight, Southern Rust, Southern Virus Complex, Stewart&#39;s Leaf Blight, Corn Lethal Necrosis, Headsmut, Diplodia Ear Rot, and Fusarium Crown Rot. Other suitable seed data may include, for example, data related to, grain drydown, stalk strength, root strength, stress emergence, staygreen, drought tolerance, ear flex, test eight, plant height, ear height, mid-season brittle stalk, plant vigor, fungicide response, growth regulators sensitivity, pigment inhibitors, sensitivity, sulfonylureas sensitivity, harvest timing, kernel texture, emergence, harvest appearance, harvest population, seedling growth, cob color, and husk cover.   2. A second set of data points is field-specific data related to soil composition. Such field-specific data may include measurements of the acidity or basicity of soil (e.g., pH levels), soil organic matter levels (“OM” levels), and cation exchange capacity levels (“CEC” levels).   3. A third set of data points is field-specific data related to field data. Such field-specific data may include field names and identifiers, soil types or classifications, tilling status, irrigation status.   4. A fourth set of data points is field-specific data related to historical harvest data. Such field-specific data may include crop type or classification, harvest date, actual production history (“APH”), yield, grain moisture, and tillage practice. In some examples, users may be prompted at the user device to provide a fifth set of data points by answering questions regarding desired planting population (e.g., total crop volume and total crop density for a particular field) and/or seed cost, expected yield, and indication of risk preference (e.g., general or specific: user is willing to risk a specific number of bushels per acre to increase the chance of producing a specific larger number of bushels per acre) if such information has not already been provided to the agricultural intelligence computer system.   

     Planting advisor module  421  receives and processes the sets of data points to simulate possible yield potentials. Possible yield potentials are calculated for various planting dates. Planting advisor module  421  additionally utilizes additional data to generate such simulations. The additional data may include simulated weather between the planting data and harvesting date, field workability, seasonal freeze risk, drought risk, heat risk, excess moisture risk, estimated soil temperature, and/or risk tolerance. Risk tolerance may be calculated based for a high profit/high risk scenario, a low risk scenario, a balanced risk/profit scenario, and a user defined scenario. Planting advisor module  421  generates such simulations for each planting date and displays a planting date recommendation for the user on the user device. The recommendation includes the recommended planting date, projected yield, relative maturity, and graphs the projected yield against planting date. In some examples, the planting advisor module also graphs the projected yield against the planting date for spring freeze risk, the planting date for fall freeze risk, the planting date for drought risk, the planting date for heat risk, the planting date for excess moisture risk, the planting date for estimated soil temperature, and the planting date for the various risk tolerance levels. Planting advisor module  421  provides the option of modeling and displaying alternative yield scenarios for planting data and projected yield by modifying one or more data points associated with seed characteristic data, field-specific data, desired planting population and/or seed cost, expected yield, and/or indication of risk preference. The alternative yield scenarios may be displayed and graphed on the user device along with the original recommendation. 
     Nitrogen Application Advisor Module  422   
     Agricultural intelligence computer system  150  is additionally configured to provide agricultural intelligence services related to soil by using nitrogen application advisor module  422 . In at least some examples, nitrogen application advisor module  422  may be similar to agricultural intelligence modules  158  and  159  (shown in  FIG. 1 ). Nitrogen application advisor module  422  determines potential needs for nitrogen in the soil and recommends nitrogen application practices to a user. More specifically, nitrogen application advisor module  422  is configured to identify conditions when crop needs cannot be met by nitrogen present in the soil. In one example embodiment, nitrogen application advisor module  422  provides recommendations for sidedressing or spraying, such as date and rate, specific to the location of the field and adapt to the current field condition data. In one embodiment, nitrogen application advisor module  422  is configured to receive one or more of the following data points for each field identified by the user (as determined from field definition data):
     1. A first set of data points includes environmental information. Environmental information may include information related to weather, precipitation, meteorology, soil and crop phenology.   2. A second set of data points includes field-specific data related to field data. Such field-specific data may include field names and identifiers, soil types or classifications, tilling status, irrigation status.   3. A third set of data points includes field-specific data related to historical harvest data. Such field-specific data may include crop type or classification, harvest date, actual production history (“APH”), yield, grain moisture, and tillage practice.   4. A fourth set of data points is field-specific data related to soil composition. Such field-specific data may include measurements of the acidity or basicity of soil (e.g., pH levels), soil organic matter levels (“OM” levels), and cation exchange capacity levels (“CEC” levels).   5. A fifth set of data points is field-specific data related to planting data. Such field-specific data may include planting date, seed type or types, relative maturity (RM) levels of planted seed(s), and seed population.   6. A sixth set of data points is field-specific data related to nitrogen data. Such field-specific data may include nitrogen application dates, nitrogen application amounts, and nitrogen application sources.   7. A seventh set of data points is field-specific data related to irrigation data. Such field-specific data may include irrigation application dates, irrigation amounts, and irrigation sources.   

     Based on the sets of data points, nitrogen application advisor module  422  determines a nitrogen application recommendation. As described below, the recommendation includes a list of fields with adequate nitrogen, a list of fields with inadequate nitrogen, and a recommended nitrogen application for the fields with inadequate nitrogen. 
     In some examples, users may be prompted at the user device to answer questions regarding nitrogen application (e.g., side-dressing, spraying) practices and costs, such as type of nitrogen (e.g., Anhydrous Ammonia, Urea, UAN (Urea Ammonium Nitrate) 28%, 30% or 32%, Ammonium Nitrate, Ammonium Sulphate, Calcium Ammonium Sulphate), nitrogen costs, latest growth stage of crop at which nitrogen can be applied, application equipment, labor costs, expected crop price, tillage practice (e.g., type (conventional, no till, reduced, strip) and amount of surface of the field that has been tilled), residue (the amount of surface of the field covered by residue), related farming practices (e.g., manure application, nitrogen stabilizers, cover crops) as well as prior crop data (e.g., crop type, harvest date, Actual Production History (APH), yield, tillage practice), current crop data (e.g., planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population), soil characteristics (pH, OM, CEC) if such information has not already been provided to the agricultural intelligence computer system. For certain questions, such as latest growth stage of crop at which nitrogen can be applied, application equipment, labor costs, the user has the option to provide a plurality of alternative responses to that the agricultural intelligence computer system can optimize the nitrogen application advisor recommendation. 
     Using the environmental information, field-specific data, nitrogen application practices and costs, prior crop data, current crop data, and/or soil characteristics, nitrogen application advisor module  422  identifies the available nitrogen in each field and simulates possible nitrogen application practices, dates, rates, and next date on which workability for a nitrogen application is “Green” taking into account predicted workability and nitrogen loss through leaching, denitrification and volatilization. Nitrogen application advisor module  422  generates and displays on the user device a nitrogen application recommendation for the user. The recommendation includes:
     1. The list of fields having enough nitrogen, including for each field the available nitrogen, last application data, and the last nitrogen rate applied.   2. The list of fields where nitrogen application is recommended, including for each field the available nitrogen, recommended application practice, recommended application dates, recommended application rate, and next data on which workability for the nitrogen application is “Green.”   3. The recommended date of nitrogen application for each field. In some examples the recommended date may be optimized for either yield or return on investment. In some examples the recommended date may be the date at which minimum predicted nitrogen levels in the field will reach a threshold minimum value without intervening nitrogen application. In some examples recommended dates may be excluded or selected based upon available equipment as indicated by the user; for example, where no equipment for applying nitrogen is available past a given growth stage, dates are preferably recommended before the predicted date at which that growth stage will be reached.   4. The recommended rate of nitrogen application for each field for each possible or recommended application date. The recommended rate of nitrogen application may be optimized for either yield or return on investment.   

     The user has the option of modeling and displaying nitrogen lost (total and divided into losses resulting from volatilization, denitrification, and leaching) and crop use (“uptake”) of nitrogen over a specified time period (predefined or as defined by the user) for the recommended nitrogen application versus one or more alternative scenarios based on a custom application practice, date and rate entered by the user. The user has the option of modeling and displaying estimated return on investment for the recommended nitrogen application versus one or more alternative scenarios based on a custom application practice, date and rate entered by the user. The alternative nitrogen application scenarios may be displayed and graphed on the user device along with the original recommendation. The user has the further option of modeling and displaying estimated yield benefit (minimum, average, and maximum) for the recommended nitrogen application versus one or more alternative scenarios based on a custom application practice, date and rate entered by the user. The user has the further option of modeling and displaying estimated available nitrogen over any time period specified by the user for the recommended nitrogen application versus one or more alternative scenarios based on a custom application practice, date and rate entered by the user. The user has the further option of running the nitrogen application advisor (using the nitrogen application advisor) for one or more sub-fields or management zones within a field. 
     Pest Advisor Module (or Pest and Disease Advisor Module)  423   
     Agricultural intelligence computer system  150  is additionally configured to provide agricultural intelligence services related to pest and disease by using pest advisor module  423 . In at least some examples, pest advisor module  423  may be similar to agricultural intelligence modules  158  and  159  (shown in  FIG. 1 ). Pest advisor module  423  is configured to identify risks posed to crops by pest damage and/or disease damage. In an example embodiment, pest advisor module  423  identifies risks caused by the pests that cause that the most economic damage to crops in the U.S. Such pests include, for example, corn rootworm, corn earworm, soybean aphid, western bean cutworm, European corn borer, armyworm, bean leaf beetle, Japanese beetle, and twospotted spider mite. In some examples, the pest and disease advisor provides supplemental analysis for each pest segmented by growth stages (e.g., larval and adult stages). Pest advisor module  423  also identifies disease risks caused by the diseases that cause that the most economic damage to crops in the U.S. Such diseases include, for example, Gray Leaf Spot, Northern Leaf Blight, Anthracnose Stalk Rot, Goss&#39;s Wilt, Southern Corn Leaf Blight, Eyespot, Common Rust, Anthracnose Leaf Blight, Southern Rust, Southern Virus Complex, Stewart&#39;s Leaf Blight, Corn Lethal Necrosis, Headsmut, Diplodia Ear Rot, Fusarium Crown Rot. The pest advisor is also configured to recommend scouting practices and treatment methods to respond to such pest and disease risks. Pest advisor module  423  is also configured to provide alerts based on observations of pests in regions proximate to the user&#39;s fields. 
     In one embodiment, pest advisor module  423  may receive one or more of the following sets of data for each field identified by the user (as determined from field definition data):
     1. A first set of data points is environmental information. Environmental information includes information related to weather, precipitation, meteorology, crop phenology and pest and disease reporting. In some examples, pest and disease reports may be received from a third-party server or data source such as a university or governmental reporting service.   2. A second set of data points is seed characteristic data. Seed characteristic data may include any relevant information related to seeds that are planted or will be planted. Seed characteristic data may include, for example, seed company data, seed cost data, seed population data, seed hybrid data, seed maturity level data, seed disease resistance data, and any other suitable seed data. Seed company data may refer to the manufacturer or provider of seeds. Seed cost data may refer to the price of seeds for a given quantity, weight, or volume of seeds. Seed population data may include the amount of seeds planted (or intended to be planted) or the density of seeds planted (or intended to be planted). Seed hybrid data may include any information related to the biological makeup of the seeds (i.e., which plants have been hybridized to form a given seed.) Seed maturity level data may include, for example, a relative maturity level of a given seed (e.g., a comparative relative maturity (“CRM”) value or a silk comparative relative maturity (“silk CRM”)), growing degree units (“GDUs”) until a given stage such as silking, mid-pollination, black layer, or flowering, and a relative maturity level of a given seed at physiological maturity (“Phy. CRM”). Disease resistance data may include any information related to the resistance of seeds to particular diseases. In the example embodiment, disease resistance data includes data related to the resistance to Gray Leaf Spot, Northern Leaf Blight, Anthracnose Stalk Rot, Goss&#39;s Wilt, Southern Corn Leaf Blight, Eyespot, Common Rust, Anthracnose Leaf Blight, Southern Rust, Southern Virus Complex, Stewart&#39;s Leaf Blight, Corn Lethal Necrosis, Headsmut, Diplodia Ear Rot, and Fusarium Crown Rot. Other suitable seed data may include, for example, data related to, grain drydown, stalk strength, root strength, stress emergence, staygreen, drought tolerance, ear flex, test eight, plant height, ear height, mid-season brittle stalk, plant vigor, fungicide response, growth regulators sensitivity, pigment inhibitors, sensitivity, sulfonylureas sensitivity, harvest timing, kernel texture, emergence, harvest appearance, harvest population, seedling growth, cob color, and husk cover.   3. A third set of data points is field-specific data related to planting data. Such field-specific data may include, for example, planting dates, seed type, relative maturity (RM) of planted seed, and seed population.   4. A fourth set of data points is field-specific data related to pesticide data. Such field-specific data may include, for example, pesticide application date, pesticide product type (specified by, e.g., EPA registration number), pesticide formulation, pesticide usage rate, pesticide acres tested, pesticide amount sprayed, and pesticide source.   

     In some examples, users may be prompted at the user device to answer questions regarding pesticide application practices and costs, such as type of product type, application date, formulation, rate, acres tested, amount, source, costs, latest growth stage of crop at which pesticide can be applied, application equipment, labor costs, expected crop price as well as current crop data (e.g., planting date, seed(s) type, relative maturity (RM) of planted seed(s), seed population) if such information has not already been provided to the agricultural intelligence computer system. Accordingly, pest advisor module  423  receives such data from user devices. For certain questions, such as latest growth stage of crop at which pesticide can be applied, application equipment, labor costs, the user has the option to provide a plurality of alternative responses to that agricultural intelligence computer system  150  can optimize the pest and disease advisor recommendation. 
     Pest advisor module  423  is configured to receive and process all such sets of data points and received user data and simulate possible pesticide application practices. The simulation of possible pesticide practices includes, dates, rates, and next date on which workability for a pesticide application is “Green” taking into account predicted workability. Pest advisor module  423  generates and displays on the user device a scouting and treatment recommendation for the user. The scouting recommendation includes daily (or as specified by the user) times to scout for specific pests and diseases. The user has the option of displaying a specific subset of pests and diseases as well as additional information regarding a specific pest or disease. The treatment recommendation includes the list of fields where a pesticide application is recommended, including for each field the recommended application practice, recommended application dates, recommended application rate, and next data on which workability for the pesticide application is “Green.” The user has the option of modeling and displaying estimated return on investment for the recommended pesticide application versus one or more alternative scenarios based on a custom application practice, date and rate entered by the user. The alternative pesticide application scenarios may be displayed and graphed on the user device along with the original recommendation. The user has the further option of modeling and displaying estimated yield benefit (minimum, average, and maximum) for the recommended pesticide application versus one or more alternative scenarios based on a custom application practice, date and rate entered by the user. 
     Field Health Advisor Module  424   
     Agricultural intelligence computer system  150  is also configured to provide information regarding the health and quality of areas of fields  120 . In at least some examples, field health advisor module  424  may be similar to agricultural intelligence modules  158  and  159  (shown in  FIG. 1 ). Field health advisor module  424  identifies crop health quality over the course of the season and uses such crop health determinations to recommend scouting or investigation in areas of poor field health. More specifically, field health advisor module  424  receives and processes field image data to determine, identify, and provide index values of biomass health. The index values of biomass health may range from zero (indicating no biomass) to 1 (indicating the maximum amount of biomass). In an example embodiment, the index value has a specific color scheme, so that every image has a color-coded biomass health scheme (e.g., brown areas show the areas in the field with the lowest relative biomass health). In one embodiment, field health advisor module  424  may receive one or more of the following data points for each field identified by the user (as determined from field definition data):
     1. A first set of data points includes environmental information. Such environmental information includes information related to satellite imagery, aerial imagery, terrestrial imagery and crop phenology.   2. A second set of data points includes field-specific data related to field data. Such field-specific data may include field and soil identifiers such as field names, and soil types.   3. A third set of data points includes field-specific data related to soil composition data. Such field-specific data may include measurements of the acidity or basicity of soil (e.g., pH levels), soil organic matter levels (“OM” levels), and cation exchange capacity levels (“CEC” levels).   4. A fourth set of data points includes field-specific data related to planting data. Such field-specific data may include, for example, planting dates, seed type, relative maturity (RM) of planted seed, and seed population.   

     Field health advisor module  424  receives and processes all such data points (along with field image data) to determine and identify a crop health index for each location in each field identified by the user each time a new field image is available. In an example embodiment, field health advisor module  424  determines a crop health index as a normalized difference vegetation index (“NDVI”) based on at least one near-infrared (“NIR”) reflectance value and at least one visible spectrum reflectance value at each raster location in the field. In another example embodiment, the crop health index is a NDVI based on multispectral reflectance. 
     Field health advisor module  424  generates and displays on the user device the health index map as an overlay on an aerial map for each field identified by the user. In an example embodiment, for each field, the field health advisor module will display field image date, growth stage of crop at that time, soil moisture at that time, and health index map as an overlay on an aerial map for the field. In an example embodiment, the field image resolution is between 5 m and 0.25 cm. The user has the option of modeling and displaying a list of fields based on field image date and/or crop health index (e.g., field with lowest overall health index values to field with highest overall health index values, field with highest overall health index values to field with lowest overall health index values, lowest health index value variability within field, highest health index value variability within field, or as specified by the user). The user also has the option of modeling and displaying a comparison of crop health index for a field over time (e.g., side-by-side comparison, overlay comparison). In an example embodiment, the field health advisor module provides the user with the ability to select a location on a field to get more information about the health index, soil type or elevation at a particular location. In an example embodiment, the field health advisor module provides the user with the ability to save a selected location, the related information, and a short note so that the user can retrieve the same information on the user device while in the field. 
     Harvest Advisor Module  425   
     Agricultural intelligence computer system  150  is additionally configured to provide agricultural intelligence services related to timing and mechanisms of harvest using harvest advisor module  425 . In at least some examples, harvest advisor module  425  may be similar to agricultural intelligence modules  158  and  159  (shown in  FIG. 1 ) and more specifically to harvest advisor module  158 . 
     Harvest advisor computing module  425  is in data communication with agricultural intelligence computing system  150 . Agricultural intelligence computing system  150  captures and stores field definition data  160 , field-specific &amp; environmental data  170 , and field condition data  180  within its memory device. Harvest advisor computing module  425  receives and processes field definition data  160 , field-specific &amp; environmental data  170 , and field condition data  180  from agricultural intelligence computing system  150  to provide (i) grain moisture value predictions during drydown of a particular field prior to harvest, (ii) a projected date when the particular field will reach a target moisture value, and (iii) harvest recommendations and planning for one or more fields. More specifically, harvest advisor computing module  425  is configured to: (i) identify an initial date of a crop within a field (e.g., a black layer date); (ii) identifying an initial moisture value associated with the crop and the initial date; (iii) identify a target harvest moisture value associated with the crop; (iv) receive field condition data associated with the field; (v) compute a target harvest date for the crop based at least in part on the initial date, the initial moisture value, the field condition data, and the target harvest moisture value, wherein the target harvest date indicates a date at which the crop will have a present moisture value approximately equal to the target harvest moisture value; and (vi) display the target harvest date for the crop to the grower for harvest planning. The target harvest moisture value represents the value at which grower  110  desires the crop to be when harvested (e.g., at harvest date). Thus, the harvest advisor computing module  425  assists the grower in projecting approximately when a given field will be ready for harvest by projecting moisture values over time, and considering both past weather data and future weather predictions at the given field. 
     Revenue Advisor Module  426   
     Agricultural intelligence computer system  150  is additionally configured to provide agricultural intelligence services related to selling and marketing crops using revenue advisor module  426 . In at least some examples, revenue advisor module  426  may be similar to agricultural intelligence modules  158  and  159  (shown in  FIG. 1 ) and more specifically to revenue advisor module  159 . 
     Revenue advisor module  426  is in data communication with agricultural intelligence computing system  150 . Agricultural intelligence computing system  150  captures and stores field definition data  160 , field-specific &amp; environmental data  170 , and field condition data  180  within its memory device. Revenue advisor module  426  receives and processes field definition data  160  and field condition data  180  from agricultural intelligence computing system  150  to provide (i) daily yield projections at the national, farm, and field level, (ii) current crop prices at the national and local level, (iii) daily revenue projections at the farm and field level, and (iv) daily profit estimates by the field, farm, and acre. More specifically, revenue advisor module  426  is configured to: (i) receive field condition data  180  and field definition data  160  from agricultural intelligence computing system  150  for each field  120  of grower  110 , wherein the field condition data  180  includes growth stage conditions, field weather conditions, soil moisture, and precipitation conditions, and wherein field definition data includes field identifiers, geographic identifiers, boundary identifiers, and crop identifiers; (ii) receive cost data from grower  110 , wherein cost data includes costs related to an individual field  120  or all of the fields associated with grower  110 ; (iii) receive crop pricing data from local and national sources; (iv) process field condition data  180 , the crop pricing data, and the cost data to determine yield data, revenue data, and profit data for each field  120  of grower  110 ; and (v) output the yield data, revenue data and profit data to user device  112 ,  114 ,  116 , and/or  118 . The yield data, revenue data, and profit data relate to an individual field, and can further relate a plurality of additional fields associated with the grower. Yield data includes yield estimates for a high, low, and expected case for each field and at the national level. Revenue data includes revenue estimates based on national and local prices for each field. Profit data includes the expected profit for each field for the high, low, and expected cases. 
       FIG. 5  is an example method for managing agricultural activities in agricultural environment  100  (shown in  FIG. 1 ). Method  500  is implemented by agricultural intelligence computer system  150  (shown in  FIG. 1 ). Agricultural intelligence computer system  150  receives  510  a plurality of field definition data. Agricultural intelligence computer system  150  retrieves  520  a plurality of input data from a plurality of data networks  130 A,  130 B, and  140 . Agricultural intelligence computer system  150  determines  530  a field region based on the field definition data. Agricultural intelligence computer system  150  identifies  540  a subset of the plurality of input data associated with the field region. Agricultural intelligence computer system  150  determines  550  a plurality of field condition data based on the subset of the plurality of input data. Agricultural intelligence computer system  150  provides  560  the plurality of field condition data to the user device. 
       FIG. 6  is an example method for recommending agricultural activities in the agricultural environment of  FIG. 1 . Method  500  is implemented by agricultural intelligence computer system  150  (shown in  FIG. 1 ). Agricultural intelligence computer system  150  receives  610  a plurality of field definition data. Agricultural intelligence computer system  150  retrieves  620  a plurality of input data from a plurality of data networks  130 A,  130 B, and  140 . Agricultural intelligence computer system  150  determines  630  a field region based on the field definition data. Agricultural intelligence computer system  150  identifies  640  a subset of the plurality of input data associated with the field region. Agricultural intelligence computer system  150  determines  650  a plurality of field condition data based on the subset of the plurality of input data. Agricultural intelligence computer system  150  provides  660  the plurality of field condition data to the user device. Agricultural intelligence computer system  150  determines  670  a recommendation score for each of the plurality of field activity options based at least in part on the plurality of field condition data. Agricultural intelligence computer system  150  provides  680  a recommended field activity option from the plurality of field activity options based on the plurality of recommendation scores. 
       FIG. 7  is a diagram of components of one or more example computing devices that may be used in the environment shown in  FIG. 5 .  FIG. 7  further shows a configuration of databases including at least database  157  (shown in  FIG. 1 ). Database  157  is coupled to several separate components within fraud detection computer system  150 , which perform specific tasks. 
     Agricultural intelligence computer system  150  includes a first receiving component  701  for receiving a plurality of field definition data, a first retrieving component  702  for retrieving a plurality of input data from a plurality of data networks, a first determining component  703  for determining a field region based on the field definition data, a first identifying component  704  for identifying a subset of the plurality of input data associated with the field region, a second determining component  705  for determining a plurality of field condition data based on the subset of the plurality of input data, a first providing component  706  for providing the plurality of field condition data to the user device, a third determining component  707  for determining a recommendation score for each of the plurality of field activity options based at least in part on the plurality of field condition data, and a second providing component  708  for providing a recommended field activity option from the plurality of field activity options based on the plurality of recommendation scores. 
     In an example embodiment, database  157  is divided into a plurality of sections, including but not limited to, a meteorological analysis section  710 , a soil and crop analysis section  712 , and a market analysis section  714 . These sections within database  157  are interconnected to update and retrieve the information as required 
       FIGS. 8-30  are example illustrations of information provided by the agricultural intelligence computer system of  FIG. 3  to the user device of  FIG. 2  to facilitate the management and recommendation of agricultural activities. 
     Referring to  FIG. 8 , screenshot  800  illustrates a setup screen wherein grower  110  (shown in  FIG. 1 ) may provide user information input  402  (shown in  FIG. 4 ) to define basic attributes associated with their account. 
     Referring to  FIGS. 9-11 , screenshots  900 ,  1000 , and  1100  illustrate options allowing for grower  110  (shown in  FIG. 1 ) to view field condition data  180  (shown in  FIG. 1 ). As is indicated in screenshot  900 , grower  110  may select particular dates for field condition data  180  viewing that may be in the past, present, or future and may accordingly provide historic, current, or forecasted field condition data  180 . Grower  110  may accordingly select a particular date and time to view field condition data  180  for particular fields  120  (shown in  FIG. 1 ). Screenshot  1000  illustrates a consolidated view of field condition data  180  for a particular field  120  at a particular date. More specifically, field condition data  180  shown includes output of field weather data module  411 , field workability data module  412 , growth stage data module  413 , and soil moisture data module  414 . Screenshot  1100  similarly shows output of field precipitation module  415  of a particular field  120  over a particular time period. As described above and herein, such field condition data  180  is determined using a localized method that determines such field conditions uniquely for each field  120 . 
       FIGS. 12 and 13  illustrate such field condition data  180  displayed graphically using maps. More specifically, from the view of screenshots  1200 , grower  110  may select a particular portion of a map to identify field condition data  180  for each of fields  120 . Screenshot  1300  accordingly illustrates such a display of field condition data  180  for a particular field  122 . 
     Referring to  FIGS. 14-20 , screenshots  1400 ,  1500 ,  1600 ,  1700 ,  1800 ,  1900 , and  2000  illustrate the display of fields  120  (shown in  FIG. 1 ) associated with grower  110  (shown in  FIG. 1 ). More specifically, in screenshot  1400  grower  110  provides field definition data  160  (shown in  FIG. 1 ) to define fields  120 , indicated graphically. Accordingly, a plurality of fields  120  are illustrated and may be reviewed individually or in any combination to obtain field condition data  180  (shown in  FIG. 1 ) and/or recommended agricultural activities  190  (shown in  FIG. 1 ). Note that screenshot  1400  illustrates that grower  110  may own, use, or otherwise manage a plurality of fields  120  that are substantially far from one another and associated with unique geographic and meteorological conditions. It will be appreciated that the systems and methods described herein, providing hyper localized field condition data  180  and recommended agricultural activities  190 , substantially helps grower  110  to identify meaningful distinctions between each of fields  120  in order to effectively manage each field  120 . 
     In screenshot  1500 , grower  110  (shown in  FIG. 1 ) may see a tabular view indicating identifiers for each field  120  (shown in  FIG. 1 ) in conjunction with a map view of such fields. Grower  110  may navigate using the tabular view (or the graphical view) to individual actions associated with each field  120 . Accordingly, screenshot  1600  illustrates enhanced information shown to grower  110  upon selecting a particular field for review from either the tabular view or the graphical view (e.g., by clicking on one of the fields). As is illustrated in screenshots  1700 ,  1800 ,  1900 , and  2000 , grower  110  may additionally enhance display (or “zoom in”) to view a smaller subset of fields  120 . 
     Referring to  FIGS. 21 and 22 , screenshots  2100  and  2200  illustrate historical data that may be provided by grower  110  (shown in  FIG. 1 ) or any other source to identify notes or details associated with planting. More specifically, grower  110  may navigate to a particular date in screenshot  2400  and view planting notes as displayed in screenshot  2200 . 
     Referring to  FIG. 23 , screenshot  2300  presents a tabular view that allows grower  110  (shown in  FIG. 1 ) to group or consolidate common land units (“CLUs”) into “field groups”. As a result, data associated with a particular field group may be viewed commonly. In some examples, grower  110  may be interested in viewing and managing particular fields  120  (shown in  FIG. 1 ) in particular combinations based on, for example, common crops or geographies. Accordingly, the application and systems described facilitate such effective management. 
     Referring to  FIGS. 24-30 , screenshots  2400 ,  2500 ,  2600 ,  2700 ,  2800 ,  2900 , and  3000  illustrate the use of a “field manager” tool that enables grower  110  (shown in  FIG. 1 ) to view information for a plurality of fields in a tabular format. Screenshots  2400 ,  2500 ,  2600 ,  2700 ,  2800 ,  2900 , and  3000  further indicate that grower  110  may view field condition data  180  in common with field-specific &amp; environmental data  170  (shown in  FIG. 1 ). For example screenshot  2400  illustrates, on a per field basis, current cultivated crop, acreage, average yield, tilling practices or methods, and residue levels. By contrast, screenshot  2500  illustrates that grower  110  may apply a filter  2510  to identify particular subgroups of fields  120  for review based on characteristics including current cultivated crop, acreage, average yield, tilling practices or methods, and residue levels. The field manager tool also enables grower  110  to update or edit information. Screenshots  2600 ,  2700 ,  2800 ,  2900 , and  3000  show views wherein grower  110  may update or edit information for previous periods of cultivation. More specifically, in screenshot  2600 , general data may be updated while in screenshot  2700 , planting data may be updated. Similarly, in screenshot  2800 , harvest data may be updated and in screenshot  2900 , nitrogen data may be updated. In screenshot  3000 , soil characteristics data may be updated. 
     As used herein, the term “non-transitory computer-readable media” is intended to be representative of any tangible computer-based device implemented in any method or technology for short-term and long-term storage of information, such as, computer-readable instructions, data structures, program modules and sub-modules, or other data in any device. Therefore, the methods described herein may be encoded as executable instructions embodied in a tangible, non-transitory, computer readable medium, including, without limitation, a storage device and/or a memory device. Such instructions, when executed by a processor, cause the processor to perform at least a portion of the methods described herein. Moreover, as used herein, the term “non-transitory computer-readable media” includes all tangible, computer-readable media, including, without limitation, non-transitory computer storage devices, including, without limitation, volatile and nonvolatile media, and removable and non-removable media such as a firmware, physical and virtual storage, CD-ROMs, DVDs, and any other digital source such as a network or the Internet, as well as yet to be developed digital means, with the sole exception being a transitory, propagating signal. 
     This written description uses examples to disclose the disclosure, including the best mode, and also to enable any person skilled in the art to practice the embodiments, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the disclosure is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.