Crop yield modelling based on potential yield

Abstract

Embodiments of the present disclosure relate to systems and methods for determining, calculating, or simulating crop yields for a growing season. In certain embodiments, the model starts with a potential yield that reflects the environmental limitations of a particular field, assuming that the grower has selected the appropriate crop variety for his growing season and is optimally managing his farm. In certain embodiments, the end-of-season yield is predicted by considering environmental stressors that are applied as penalties to the starting potential yield, and the crop yield model simulates post-season, mid-season, and end-of-season potential yields by classifying major categories of plant stressors. These stressors are used to track and penalize the potential yield using a dynamic set of inputs. The crop yield model works in conjunction with a crop phenology model to help identify historical averages for various phenological stages of crop development.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Patent Application No. 63 / 295,546, filed December 31, 2021, the disclosure of which is incorporated herein by reference in its entirety.

[0002] The present disclosure relates to computational techniques for modeling crop yields. [Background technology]

[0003] In agriculture, it is desirable to maximize the production or yield of a given agricultural product. Many techniques exist to simulate crop development and yield. However, model accuracy can be compromised if certain environmental stresses during the growing season are not considered. Furthermore, models often do not account for variability in planting conditions and changes in underlying assumptions resulting from changing climate and weather patterns.

[0004] In order to facilitate a fuller understanding of the present disclosure, reference is now made to the accompanying drawings, in which like elements are referenced with like numerals, and in which the drawings should not be construed as limiting the present disclosure, but are intended to be illustrative only, in which: [Brief description of the drawings]

[0005] [Figure 1] 1 illustrates an exemplary system architecture, according to an embodiment of the present disclosure.

[0006] [Diagram 2] FIG. 1 is a block diagram illustrating an exemplary computer system according to an embodiment of the present disclosure.

[0007] [Diagram 3] 1 is a graph illustrating a crop phenology model according to an embodiment of the present disclosure.

[0008] [Figure 4] FIG. 1 is a flow diagram illustrating field-level crop phenology modeling according to an embodiment of the present disclosure.

[0009] [Diagram 5] 1 is a plot showing the relationship between corn planting date and winter weather index for the 50% cohort.

[0010] [Figure 6] FIG. 1 is a flow diagram illustrating the inputs and outputs of various equations used in a field-level crop phenology model according to an embodiment of the present disclosure.

[0011] [Figure 7] FIG. 1 is a flow diagram illustrating a method for simulating plant development stages according to an embodiment of the present disclosure.

[0012] [Figure 8] 1 is a plot showing how potential yield is calculated starting from genetic potential.

[0013] [Figure 9] 1 is a plot showing the relationship between air temperature and crown temperature by snow depth.

[0014] [Figure 10] 1 is a plot showing the distribution of maximum clear sky radiation between ideal planting date and maturity date.

[0015] [Figure 11] Shows the effect of delayed planting on yield potential.

[0016] [Figure 12] FIG. 1 shows the soil water balance.

[0017] [Figure 13] 1 is a plot illustrating an example crop coefficient curve.

[0018] [Figure 14] 1 shows the minimum survival temperatures of winter wheat cultivars with low, medium, and high cold tolerance.

[0019] [Figure 15] FIG. 1 is a flow diagram illustrating a method for simulating crop yield during a growing season according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] Described herein are embodiments of a crop yield model for simulating crop potential yield. In certain embodiments, the model starts with a potential yield that reflects the environmental limitations of a particular field, assuming that the grower has selected the appropriate crop variety for his growing season and optimally manages his farm. End-of-season yield is predicted by considering environmental stressors, which are applied as penalties to the starting potential yield. In certain embodiments, the crop yield model simulates post-season potential yield, mid-season potential yield, and end-of-season potential yield by classifying major categories of plant stressors. These stressors are used to track and penalize potential yield using a dynamic set of inputs (e.g., weather, soil, and farm management activities). The crop yield model works in conjunction with a crop phenology model to help identify historical averages for various phenological stages of crop development. Embodiments herein advantageously enable farmers to better estimate post-season, mid-season, and end-season yield potential, develop new digital business models, and establish risk / reward thresholds for grower field activities.

[0021] In particular, crop yield can be considered to be the harvested biomass from a sown population of plants in an agricultural field, measured in units of mass per area, e.g., kilograms per hectare or pounds per acre.

[0022] Below, we discuss in more detail phenology models that describe plant development and crop yield models that utilize crop phenology models.

[0023] Terms such as "agent" as used in connection with this disclosure refer to any substance, material, or microorganism that can be used to treat crops, including, but not limited to, herbicides, fungicides, insecticides, acaricides, molluscicides, nematicides, rodenticides, repellents, bactericides, biocides, safeners, adjuvants, plant growth regulators, fertilizers, urease inhibitors, denitrification inhibitors, nitrification inhibitors, bioherbicides, biofungicides, bioinsecticides, bioacaricides, biomolluscicides, bionematicides, biorodenticides, biorepellents, biofungicides, biological biocides, biological safeners, biological adjuvants, biological plant growth regulators, biological urease inhibitors, biological denitrification inhibitors, or biological nitrification inhibitors, or any combination thereof.

[0024] General System Implementation An exemplary implementation of the embodiments described herein will now be described. FIG. 1 illustrates an exemplary system architecture 100 according to an embodiment of the present disclosure. The system architecture 100 includes a data store 110, user devices 120A-120Z, and a modeling server 130, each of which is communicatively coupled via a network 105. One or more of the devices of the system architecture 100 may be implemented using a generalized computer system 200, which will be described below with respect to FIG. 2. It should be understood that the devices of the system architecture 100 are merely exemplary, and that additional data stores, user devices, modeling servers, and networks may be present.

[0025] In one embodiment, network 105 may include a public network (e.g., the Internet), a private network (e.g., a local area network (LAN) or a wide area network (WAN)), a wired network (e.g., an Ethernet network), a wireless network (e.g., an 802.11 network or a Wi-Fi network), a cellular network (e.g., a Long Term Evolution (LTE) network), a router, a hub, a switch, a server computer, and / or combinations thereof. Although network 105 is shown as a single network, network 105 may include one or more networks operating as a standalone network or in cooperation with one another. Network 105 may utilize one or more protocols of one or more devices that are communicatively coupled. Network 105 may change to other protocols or from other protocols to one or more of the network devices.

[0026] In one embodiment, the data store 110 may include one or more of a short-term memory (e.g., random access memory), a cache, a drive (e.g., a hard drive), a flash drive, a database system, or another type of component or device that may store data. The data store 110 may also include multiple storage components (e.g., multiple drives or multiple databases) that may span multiple computing devices (e.g., multiple server computers). In certain embodiments, the data store 110 may be cloud-based. One or more of the devices of the system architecture 100 may utilize their own storage and / or the data store 110 to store public and private data, and the data store 110 may be configured to provide secure storage for private data. In certain embodiments, the data store 110 may be used for data backup or archival purposes.

[0027] The user devices 120A-120Z may include computing devices such as personal computers (PCs), laptops, mobile phones, smartphones, tablet computers, netbook computers, etc. The user devices 120A-120Z may also be referred to as "client devices" or "mobile devices." An individual user may be associated with one or more of the user devices 120A-120Z (e.g., may own and / or operate one or more of the user devices 120A-120Z). One or more of the user devices 120A-120Z may also be owned and utilized by different users in different locations. As used herein, a "user" may be referred to as an individual. However, other embodiments of the present disclosure encompass a "user" being a set of users and / or an entity controlled by an automated source. For example, a set of individual users connected as a community in a company or government organization may be considered a "user."

[0028] User devices 120A-120Z may each utilize one or more local data stores, which may be internal or external devices and each may include one or more of short-term memory (e.g., random access memory), cache, drive (e.g., hard drive), flash drive, database system, or another type of component or device capable of storing data. The local data stores may also include multiple storage components (e.g., multiple drives or multiple databases) that may span multiple computing devices (e.g., multiple server computers). In certain embodiments, the local data stores may be used for data backup or archival purposes.

[0029] User devices 120A-120Z may implement user interfaces 122A-122Z, respectively, that may enable the respective user devices to send and receive information to and from other user devices, data store 110, and modeling server 130. Each of user interfaces 122A-122Z may be a graphical user interface (GUI). For example, user interface 122A may be a web browser interface that may access, retrieve, present, and / or navigate content provided by modeling server 130 (e.g., web pages such as HyperText Markup Language (HTML) pages). In one embodiment, user interface 122A may be a standalone application (e.g., a mobile “app”) that enables a user to use user device 120A to send and receive information to and from other user devices, data store 110, and modeling server 130.

[0030] In one embodiment, modeling server 130 may include one or more computing devices (such as a rack mount server, a router computer, a server computer, a personal computer, a mainframe computer, a laptop computer, a tablet computer, a desktop computer, etc.), data stores (e.g., hard disks, memory, databases), networks, software components, and / or hardware components from which digital content may be retrieved. In certain embodiments, modeling server 130 may be a server utilized to retrieve / access content or information related to content by any of user devices 120. In certain embodiments, additional modeling servers may be present.

[0031] In certain embodiments, the modeling server 130 may implement a crop yield modeling component 140 that simulates potential crop yields. The functionality of the crop yield modeling component 140 is described in more detail below with respect to Figures 3-15.

[0032] 1, data store 110, user devices 120A-120Z, and modeling server 130 are each depicted as single disparate components, but these components may be implemented together in a single device or networked in various combinations of multiple different devices operating together. In particular embodiments, some or all of the functionality of modeling server 130 may be performed by one or more of user devices 120A-120Z or other devices under the control of modeling server 130.

[0033] FIG. 2 illustrates a diagrammatic representation of a machine in the exemplary form of a computer system 200 on which a set of instructions (e.g., for causing the machine to perform any one or more of the methodologies discussed herein) may be executed. In alternative embodiments, the machine may be connected (e.g., networked) to other machines in a LAN, an intranet, an extranet, or the Internet. The machine may operate as a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a web appliance, a server, a network router, a switch, or a bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by the machine. Furthermore, although only a single machine is shown, the term "machine" shall be construed to include any collection of machines that individually or collectively execute a set of instructions (or sets of instructions) to perform any one or more of the methodologies discussed herein. Some or all of the components of the computer system 200 may be utilized by or be an example of any of the devices of the system architecture 100, such as the data store 110, one or more of the user devices 120A-120Z, and the modeling server 130.

[0034] The exemplary computer system 200 includes a processing device (processor) 202, a main memory 204 (e.g., read only memory (ROM), flash memory, dynamic random access memory (DRAM), such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM)), a static memory 206 (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device 220, which communicate with each other via a bus 210.

[0035] Processor 202 represents one or more general-purpose processing devices, such as a microprocessor, a central processing unit, etc. More specifically, processor 202 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets, or a processor implementing a combination of instruction sets. Processor 202 may also be one or more special-purpose processing devices, such as an ASIC, a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, etc. Processor 202 is configured to execute instructions 226 to perform the operations and steps discussed herein.

[0036] Computer system 200 may further include a network interface device 208. Computer system 200 may also include a video display device 212 (e.g., a liquid crystal display (LCD), a cathode ray tube (CRT), or a touch screen), an alphanumeric input device 214 (e.g., a keyboard), a cursor control device 216 (e.g., a mouse), and a signal generation device 222 (e.g., a speaker).

[0037] Power device 218 may monitor the power level of a battery used to power computer system 200 or one or more of its components. Power device 218 may provide one or more interfaces that provide an indication of the power level, a time window remaining until shutdown of computer system 200 or one or more of its components, a power consumption rate, an indicator of whether the computer system is utilizing an external power source or battery power, and other power-related information. In certain embodiments, the indications related to power device 218 may be accessible remotely (e.g., accessible to a remote backup management module via a network connection). In certain embodiments, the battery utilized by power device 218 may be an uninterruptible power supply (UPS) local or remote to computer system 200. In such an embodiment, power device 218 may provide information regarding the power level of the UPS.

[0038] The data storage device 220 may comprise a computer readable storage medium 224 having stored thereon one or more sets of instructions 226 (e.g., software) embodying any one or more of the methodologies or functions described herein. The instructions 226 may also reside completely or at least partially within the main memory 204 and / or within the processor 202 during execution thereof by the computer system 200, the main memory 204, and the processor 202. The computer system 200, the main memory 204, and the processor 202 also comprise computer readable storage media. The instructions 226 may further be transmitted or received over a network 230 (e.g., network 105) via the network interface device 208.

[0039] In one embodiment, the instructions 226 include instructions for the crop yield modeling component 140, as described with respect to FIG. 1 and throughout this disclosure. For example, the crop yield modeling component 140 may be implemented by the modeling server 130 or the user devices 120A-120Z. Although in the exemplary embodiment, the computer-readable storage medium 224 is shown to be a single medium, the term "computer-readable storage medium" or "machine-readable storage medium" should be interpreted to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that store one or more sets of instructions. The term "computer-readable storage medium" or "machine-readable storage medium" should also be interpreted to include any transitory or non-transitory medium capable of storing, encoding, or carrying a set of instructions for execution by a machine and causing a machine to perform any one or more of the methodologies of the present disclosure. Thus, the term "computer-readable storage medium" should be interpreted to include, but is not limited to, solid-state memory, optical media, and magnetic media.

[0040] Phenology model A field-scale crop phenology model (also referred to herein as a "crop phenology model" or "phenology model") is used to simulate plant development stages during a growing season. Knowledge of the plant's stages, especially as a predictor, aids in determining when to apply chemicals and other crop development-dependent management. The crop phenology model, along with the user's field location, planting date, and variety information (i.e., one or more crop types), serves as a robust field-scale decision-making tool. In certain embodiments, the phenology model can be run under three different scenarios: historical, pre-growing season, or in-season. The phenology model can use a blend of various weather data inputs (historical, forecast, long-range forecast, and climatology) to generate phenology results based on the user's desired scenario.

[0041] Certain embodiments of the phenology model utilize temperature, rather than days after planting, to simulate plant development and growth during the growing season. Plants have thermal requirements to carry out their daily physiological processes. These requirements may be defined by air temperature. Plants can survive and function between upper and lower air temperature limits, and grow optimally somewhere between these limits. Each crop type has a unique reference temperature above which measurable biomass accumulation is observed during the growing season. This reference temperature minus the average daily air temperature is taken as a measure of the amount of heat for plant development and growth. The result of this subtraction is called a "growing degree day" or "growing degree unit." Daily growing degree days are accumulated over the growing season to track crop development and growth.

[0042] In certain embodiments, the phenology model uses climatologically derived degree-day accumulation between the planting and harvesting stages. However, pre-emergence crops grow in response to soil temperature rather than air temperature. Therefore, the embodiments described herein utilize one or more general formulas with crop type specific coefficients that use soil temperature at the seeding depth to better simulate crop germination and emergence. The use of soil temperature emergence functions allows for more accurate simulations of a given growing season than the degree-day accumulation approach alone.

[0043] In certain embodiments, adjustments are made to degree days both between emergence and flowering and between flowering and maturity. Adjustments to degree days between emergence and flowering take into account the effects of photoperiod and day length on crop development. This adjustment is particularly important for crops that undergo dormancy in winter. Adjustments to degree days between flowering and maturity take into account isocarpicity. As used herein, "isocarpicity" refers to a significant delay in crop development to the flowering stage that results in early grain filling (insufficient degree days). The derivation of growing degree days and the adjustments thereto are described in more detail below. Photoperiod and isocarpicity adjustments can be utilized to more realistically simulate physiological processes associated with plant development.

[0044] Certain embodiments utilize a climatological framework. In many countries, there is little or no data on crop varieties and their developmental characteristics. Therefore, embodiments herein contemplate a framework that utilizes historical observations of crop growth stages and climatological temperature records. This framework provides a general function with crop type specific coefficients to calculate the dates of the main phenological stages based on climatological indices. This allows the degree-day requirements for the number of days required for maturity to be determined climatologically anywhere in the world. The climatological framework provides an important reference for assessing the validity of the number of days to maturity for a user-provided variety and further enables decisions regarding whether the crop has achieved optimal development in the growing season.

[0045] The allocation of phenological stages to growing degree days is achieved indirectly through crop degree, which is calculated by dividing the degree days by the total number of degree days between stages, as discussed in more detail below. Crop degree alleviates the need to relate crop phenology schemes (e.g., the BBCH phenological scheme) to growing degree days by normalizing the degree days to the sum of degree days to maturity.

[0046] 1. Overview of the phenology model In certain embodiments, the phenology model is a physically deterministic model that dynamically simulates the phenological stages of plant growth. The model works on a set of general equations parameterized by crop type, allowing the model to rapidly scale across both crops and growing regions. In certain embodiments, the model first calculates crop degree. The calculation of crop degree is a unique approach that allows flexibility in accommodating different phenological schemes. The period between planting and emergence utilizes a soil temperature model to calculate degree, and the period between emergence and maturity utilizes degree days. Degrees, degree days, and mapping to the BBCH phenological scheme are discussed in more detail with respect to FIG. 3. FIG. 4 is a flow diagram illustrating a high-level overview of the crop phenology model, according to an embodiment of the present disclosure.

[0047] In a particular embodiment, crop growth factor (CGF) is defined to range from 0.0 to 3.0, and crop growth is divided into three periods: planting to crop emergence (0-1.0), emergence to maturity (1.0-2.0), and maturity to harvest (2.0-3.0). In the period between planting and crop emergence, crop growth factor depends on a function that tracks a 5-day moving average of soil temperature at the sowing depth. A soil temperature model provides the total number of days between planting and emergence. The growth factor for this period is then determined using a formula that divides the current number of days from the planting date by the total number of days until emergence is reached. In a particular embodiment, this growth factor changes dynamically as weather conditions and soil temperature change until the emergence date is reached.

[0048] In certain embodiments, the growth intensity between emergence and maturity is first calculated using the daily seasonal degree-days divided by the climatologically derived total degree-days between the phenological stages that define the period, and then the crop growth intensity between emergence and maturity is increased in steps from 1.0 to 2.0.

[0049] An example demonstrating growth rate will now be described. Based on the data shown in Figure 3, the number of accumulated degree days required between emergence and maturity is 2400 (in this example, 2700 minus the 300 accumulated between planting and emergence). For each day of the growing season, the degree days are calculated and accumulated. If there are 10 degree days the day after emergence, then the growth rate is 1.0 + 10 / 2400, or 1.0042 (note that the growth rate increases from 1.0 to 2.0 between emergence and maturity). If 15 degree days occur the next day, then the growth rate is now 1 + 25 / 2400, or 1.0104.

[0050] Similarly, crop intensities during the period between maturity and harvest can be calculated using the daily degree-days divided by the climatologically derived total degree-days between the phenological stages that define that period, with the crop intensities between maturity and harvest increasing in steps from 2.0 to 3.0.

[0051] Finally, the phenological stages, e.g. enumerated by BBCH codes, are related to crop stages. The conversion (or mapping) between stages and BBCH is both crop and region dependent. An example of mapping BBCH codes to maize stages could be: For planting, BBCH0 maps to growth level 0 In germination, BBCH5 maps to a growth stage of 0.5. In budding, BBCH9 maps to growth degree 1.0 In the first leaf, BBCH11 was mapped to a growth index of 1.02. In the nine-leaf stage, BBCH19 is mapped to a growth level of 1.24. For full emergence of the stigma, BBCH65 maps to a growth level of 1.55. In early milk ripeness, BBCH73 maps to a growth factor of 1.66. In the black layer (mature), BBCH89 is mapped to a growth level of 2.0 For harvest, BBCH99 was mapped to a growth level of 3.0

[0052] Although BBCH was selected as the default crop phenology scale, one of skill in the art will understand and appreciate that other phenology scales (e.g., the ISU scale) can be readily mapped to growth degrees.

[0053] FIG. 3 further illustrates the concept of days to maturity (DTM). As used herein, DTM may be defined in days as the amount of time required for a crop to reach maturity from the date of planting, and indicates the time when the total degree days between planting and maturity reach a cultivar-specific threshold for maturity in a climatically normal season. DTMs vary with crop type, cultivar, and growing region, and DTMs are typically reported by seed manufacturers. If not, DTMs can also be derived from past observations. Because DTMs determine the degree days required for a crop cultivar, utilizing a correct DTM is important for accurate simulation of phenological stages. An incorrect or inaccurate DTM can result in the model simulating crop development that is too fast or too slow, complicating planning of treatment timing or other stage-specific field activities during the growing season.

[0054] 2. Climatological calculations In certain embodiments, crop phenology models can be rapidly scaled by both crop type and growing region, which can be attributed to the underlying climatological backbone. By combining large geographical datasets, relationships can be built between long-term crop-specific planting, emergence, maturity, and harvest records and meteorological data of the same years and scale. An example of such a dataset is the crop statistics dataset provided by the National Agricultural Statistics Service (NASS) within the United States Department of Agriculture. For each US state, annual observations of planting, emergence, maturity, and harvest dates for a given crop are published. These dates are presented as a range or distribution of dates around the most valid or "average" date. The average date is referred to herein as the "50% cohort" date to indicate the position of the actual planting date in the respective distribution. The average date is used to build relationships with meteorological data. The German Weather Service (DWD) also provides similar statistics for Germany. It should be noted that these climatological calculations are valid for latitudes greater than 24 degrees north and south of the equator. In certain embodiments, a different climatological approach is used for the tropics.

[0055] 2.1 Annualized rates, winter weather indices, and generic 50% cohort functions The relationship between crop phenology and meteorological data can be defined in two steps. First, two climatological indices are developed from historical minimum and maximum temperature data. These indices can be used to define gradients in climatological environments around the world. Herein, the two indices are referred to as the "Annualized Rate" and the "Winter Climate Index." Using the period between January 1 and December 31, the Annualized Rate (RATIO) can be calculated as the climatological warmest daily temperature (TMPXH) (in °C) minus the climatological coldest daily temperature (TMPNH) (in °C) divided by the climatological warmest daily temperature (TMPXH) (in °C). In a particular embodiment, the climatological warmest and coldest temperatures are derived by harmonic fitting of 30 years of daily maximum and minimum temperature data, respectively. The formula for the Annualized Rate (RATIO) is defined as follows: RATIO=(TMPXH-TMPNH) / TMPXH,℃ Equation 1

[0056] Using the period between October 1 and March 31 in the Northern Hemisphere, the Winter Climate Index (WCI) may be calculated as the climatological winter daily maximum temperature (WTMPXH) (in °C) minus the climatological winter daily minimum temperature (WTMPNH) (in °C) divided by the climatological winter daily maximum temperature (WTMPXH) (in °C). The climatological winter daily maximum and minimum temperatures are derived by harmonic fitting of 30 years of daily winter maximum and minimum temperature data, respectively. For the Southern Hemisphere, the date range for WCI calculation is from April 1 to September 30. The Winter Climate Index (WCI) is defined as follows: WCI=(WTMPXH-WTMPNH) / WTMPXH,℃ Equation 2

[0057] The second step in deriving the relationship between crop phenology data and meteorological data is to create a function that relates the observed mean date of each crop-specific phenological stage to one of the climatic indices. The resulting function is used to determine the date of occurrence of the crop-specific phenological stage (PSDATE50%) for 50% of the fields in each climatological environment defined by one of the indices. In a particular embodiment, the values ​​of the coefficients (PSDATEA, PSDATEB, PSDATEC, PSDATED) in the function are unique for each crop and each phenological stage.

[0058] In certain embodiments utilizing an annual percentage rate (RATIO), PSDATE50%=PSDATEA-PSDATEB / (1.0+EXP(PSDATEC*(PSDATED-RATIO))),DOY Formula 3 and in certain embodiments utilizing the Winter Crop Index (WCI), PSDATE50%=PSDATEA-PSDATEB / (1.0+EXP(PSDATEC*(PSDATED-WCI))),DOY Formula 4 In the above equation, PSDATE50% is the mean or 50% cohort day at a stage; PSDATEA is the latest planting date for a stage, PSDATEB is the difference between the latest and earliest planting dates in a stage, PSDATEC is a unitless scalar that determines the slope of the curve at a given stage, PSDATED is the unitless minimum value of RATIO or WCI.

[0059] An example of a curve derived from this function is shown in Figure 5, which shows the relationship between the mean or 50% cohort planting date and WCI for corn (the function is plotted as a solid line and the observed phenological data is plotted as dots). In certain embodiments, the equation relating the mean or 50% cohort date to the RATIO and WCI is the same for all crop types, with coefficients that vary by crop type.

[0060] 2.2 50% Cohort Planting Date The following formula calculates the day of year (DOY) for the 50% cohort planting date (PLDATE50%) as a function of either RATIO or WCI.

[0061] In certain embodiments utilizing RATIO, PLDATE50%=PLDATEA-PLDATEB / (1.0+EXP(PLDATEC*(PLDATED-RATIO))),DOY Formula 5 and in certain embodiments utilizing WCI, PLDATE50%=PLDATEA-PLDATEB / (1.0+EXP(PLDATEC*(PLDATED-WCI))),DOY Formula 6 In the above equation, PLDATEA is the latest planting date, PLDATEB is the difference between the latest planting date and the earliest planting date, PLDATEC is a unitless scalar that determines the slope of the curve, PLDATED is the unitless minimum value of RATIO or WCI.

[0062] The above mentioned parameters PLDATEA, PLDATEB, PLDATEC and PLDATED may vary from crop to crop.

[0063] 3. User-specific field size calculation In certain embodiments, the above climatological functions play different roles when crop phenology models are run at different spatial scales or when knowledge of varietal characteristics or planting practices is limited. The role of the climatological functions is discussed here for an embodiment of a field-scale phenology model where a full description of crop varieties and planting dates is provided.

[0064] 3.1 Terminology In certain embodiments, to run the crop phenology model at field scale, a user (e.g., a grower) provides information on, at a minimum, crop species and variety selection and planting date. The selected variety has genetically expressed physiological traits (e.g., maturity rating, disease resistance, lodging resistance, drought tolerance, etc.) that may or may not be reported by the seed manufacturer. One trait important to phenological modeling is days to maturity (DTM), which is the number of days required for a crop to reach maturity after planting. The DTM is a measure of the length of the growing season. Thus, in certain embodiments, the DTM is derived with the assumption that the user plants the crop in a range of days around a typical or average date (as determined from past planting practices) in a given geographic region.

[0065] Because crops develop according to seasonal weather conditions rather than calendar days, in certain embodiments the DTM is converted to an environmental measure, such as degree days. Adding or accumulating degree days over a period defined by days to maturity can be interpreted as a measure of the amount of heat available to the crop over the growing season.

[0066] Assuming the planting date is known, the degree-day accumulation for a particular DTM may be derived from historical weather data in certain embodiments. The degree-day accumulation may correspond to the addition of one climatological degree-day between the planting date and the maturity date defined by the DTM. The climatological degree-day may be derived from the historical temperature records for the previous 10 years. Different user planting dates may change the calendar dates of the start and end of the DTM, so the required degree-day accumulation over the same number of days may also change. For the same DTM, the number of degree-days accumulated from a particular user planting date may be different from the number of degree-days accumulated from an average planting date based on historical practice. In other words, the user planting date may be different from the planting date in the research trials conducted by the seed manufacturer to define the DTM for a particular variety. Selecting the appropriate variety and planting date is important to maximize the potential yield in a growing season at a location. Planting too early or too late may result in a mismatch with the genetically expressed environmental requirements of the variety, reducing potential yield.

[0067] Even if the DTM for a variety is appropriate for a user's geographic location and planting date, there may be differences in the simulated and observed dates of a phenological stage during the growing season. Variables other than temperature may also affect development. Management decisions such as row and planting spacing, sowing depth, and growth regulator application may contribute to differences in the seasonal progression of a crop's phenological stages. Furthermore, because plant development within a field is typically non-uniform (i.e., different stages occur on the same day), observation protocols play a large role in determining representative stages across a field. To account for deviations in the output of the phenology model due to these various factors, certain embodiments may utilize a "biofix" factor to account for potentially large deviations between simulation and observation. The biofix factor may be a user-entered observation value that replaces the simulated phenological stage. In such an embodiment, the crop phenology model may incorporate the user-entered stage and perform the necessary real-time calibration of degree days up to and after the observation date. The end result of the calibration is a more accurate simulated stage for the remainder of the growing season.

[0068] As mentioned above, crop growth is defined in certain embodiments to correspond to three periods: planting to emergence (0-1.0), emergence to maturity (1.0-2.0), and maturity to harvest (2.0-3.0). The first period tracks underground plant growth, including seed germination, root growth, and emergence of the first leaf at the soil surface. The second period shows both underground root elongation and aboveground plant growth, including stem elongation and leaf increase, flowering, and fruit set and enlargement. The third period is characterized by the drying and defoliation of aboveground leaves and the thinning, shrinking, and death of the underground root system. Crop development during the first period is primarily a function of heat (soil temperature) and soil moisture (available moisture at the rooting depth). Crop development during the second period is primarily a function of light (incident solar radiation), heat (air temperature), and soil moisture (available moisture at the rooting depth). Crop development during the third period is a function of heat (air temperature) and canopy moisture (wetness due to precipitation).

[0069] Crop development and growth are affected differently by above-ground and below-ground environments during the growing season. Thus, in certain embodiments, the simulation of the phenological stages utilizes a different approach for each of the three growing degree periods. In certain embodiments, the phenological stages of the first period (from planting to emergence) are simulated with soil temperature. In certain embodiments, the stages of the second period (from emergence to maturity) are simulated with degree-day accumulations. In certain embodiments, the required degree-day accumulation for the second period is the total degree-days of the DTM between planting and maturity minus the total degree-days of the first period between planting and emergence. In certain embodiments, the stages of the third period (from maturity to harvest) are simulated with degree-day accumulations derived from historical practices and weather data.

[0070] The accumulated degree days for the first time period may be estimated using two approaches. In certain embodiments, the first approach utilizes the climatological functions described above. Degree-day accumulations may be calculated for days between the climatologically determined 50% cohort planting date and emergence date. The climatological planting to emergence degree days may be subtracted from the DTM planting to maturity degree days to derive the degree-day accumulation between emergence and maturity (also referred to as DDMAT).

[0071] In a particular embodiment, the second approach is to first utilize the user-entered planting date and perform a function based on soil temperature to calculate the emergence date. Then, using temperature data for each of the past 10 years, degree days are calculated between the user-entered planting date and the calculated emergence date (note that in different embodiments, the number of years may vary). Finally, the calculated degree days between the user planting date and the calculated emergence date over the entire 10-year period are averaged. As with the first approach, the 10-year average degree-day accumulation between planting and emergence is subtracted from the DTM planting-to-maturity degree-days to derive the DDMAT, or degree-day accumulation between emergence and maturity. Note that this second approach assumes that the user planting date is within a range of days around the average date used to define the variety's DTM. If the user planting date is outside the range, the DDMAT will be inaccurate and may result in errors in the simulation of phenological dates during the growing season.

[0072] 3.2 User Crop Emergence Date In certain embodiments, the User Emergence Date (UEMDATE) is determined by the soil conditions of the season following the User Planting Date (UPLDATE). The number of days to emergence may be determined by the soil temperature at the sowing depth and may be calculated using the Emergence (EM) function. In certain embodiments, crop emergence refers to the number of days between planting and when the first leaf breaks through the soil surface. Crop emergence depends, for example, on the 5-day moving average (5dST) of the soil temperature at the sowing depth. The Emergence (EM) formula defines the number of days required for emergence based on the weather conditions of the current season. The number of days required for emergence varies between a minimum (Min) and a maximum (Max) number of days from year to year, which is a function of the following parameters: the minimum number of days to emergence, the difference between the minimum and maximum number of days to emergence, a unitless scalar that determines the shape of the sigmoid curve, the soil temperature at the planting depth, a reference soil temperature corresponding to the maximum number of days to emergence, and a coefficient that normalizes the difference between the observed soil temperature and the reference soil temperature.

[0073] The five-day moving average of soil temperature varies from day to day after planting, and in certain embodiments, the number of days required for emergence may be dynamically calculated. A series of days with warm soil temperatures will shorten the number of days to emergence, while a series of cool soil temperatures will lengthen the number of days. In certain embodiments, the number of days required for emergence is dynamically calculated each day along with the accumulated number of days since planting. Emergence may be considered to have occurred on the day that the accumulated number of days since planting is equal to or greater than the required number of days as determined from the soil temperature emergence (EM) function.

[0074] As an example to illustrate the dynamic calculation of days to emergence, consider that the 5-day moving average of soil temperature at seeding depth was cold the day after planting. At the cold soil temperature, days to emergence is calculated to be 15 days. By the 4th day after planting (4 days accumulated), the soil warms up and the new calculation for days to emergence is 12 days. By the 8th day after planting (8 days accumulated), the soil continues to warm up and the new calculation is 10 days. For the next 2 days, the soil does not warm any more. When the accumulated days since planting reaches 10 days, which matches the number of days required for emergence, the seeds emerge on that day (UEMDATE).

[0075] Since the emergence date may be dynamically calculated after planting, the crop maturity may also be dynamically derived. In certain embodiments, the crop maturity ranges from 0 to 1.0 for the period between planting and emergence and may be calculated as the accumulated number of days after planting divided by the number of days required for simulated emergence. Since the number of days required may change from day to day, the crop maturity may change non-linearly in subsequent days after planting.

[0076] As an example to illustrate the dynamic nature of crop maturity between planting and emergence, the same progression of calculations of accumulated days after planting and days to emergence in the previous example is used. The calculation of crop maturity is set to 0 on the planting date. If the number of days required to emergence is calculated as 15 on the first day after planting, the maturity is 1 / 15 or 0.07. If the number of days required is calculated as 12 on the fourth day after planting, the maturity is 4 / 12 or 0.33. If the number of days required is calculated as 10 on the eighth day after planting, the maturity is 8 / 10 or 0.8. If the number of days required is calculated as 10 on the tenth day after planting, the maturity is 10 / 10 or 1.0, indicating emergence has occurred. The emergence date is 10 days after the planting date. Table 1 further illustrates this concept. [Table 1]

[0077] 3.3 User Crop Maturity Date (Spring Crops) In certain embodiments, the crop maturity date for a spring or winter crop is determined for a given season by adding degree days for each day after emergence until the day when the total is equal to or exceeds the varietal requirement defined by the DDMAT. The DDMAT for winter crops can be complicated by slowing down growth in the winter. Adjustments for winter slowing down or "dormancy" of the DDMAT are discussed in more detail below.

[0078] For both spring and winter crops, seed manufacturers typically only publish DTMs that define the length of a season (from planting to maturity), and users are rarely aware of the DDMAT of a variety because emergence dates vary widely from year to year due to soil conditions. Thus, the DDMAT is derived in certain embodiments from either historical planting practices or user-reported planting dates. Since user planting dates may be outside the range of dates used to determine the DTM from research studies, historical practices may be selected as a best practice.

[0079] In a particular embodiment, the derivation of the user crop maturity date (UMATDATE) involves three calculations. The first calculation is the user reported DTM (UDTM) degree-days. This calculation may be done after establishing the UDTM start date (planting date) and end date (maturity date). Using a climatological approach, the start date is the 50% planting date (PLDATE50%) and the end date is the planting date plus the number of days to maturity of the DTM (CMATDATE). The UDTM degree-days then corresponds to the sum of the climatological daily degree-days between the start date (PLDATE50%) and the end date (CMATDATE). UDTM = Σ(degree days between CMATDATE and PLDATE50%), degree days Equation 7

[0080] The second calculation determines the UDDMAT degree-days, i.e., the number of days from emergence to maturity for the reported user variety. The UDDMAT degree-days are calculated by subtracting the degree-days between the meteorological planting date (PLDATE50%) and the emergence date (EMDATE50%) from the UDTM degree-days. UDDMAT = UDTM-Σ(degree days between EMDATE50% and PLDATE50%), degree days Equation 8

[0081] UDDMAT represents the required degree days between emergence and maturity based on the user-reported DTM and the assumption that the planting date for the variety followed historical practice.

[0082] The third and final calculation to derive the user maturity date (UMATDATE) is to add the UDDMAT to the user emergence date (UEMDATE) in the equivalent number of days in the period defined by the accumulated degree days. UMATDATE=UEMDATE+UDDMAT(day),DOY Formula 9

[0083] Using the User Maturity Date (UMATDATE) calculation, the crop phenology model can provide a field-scale simulation of phenological stages for any user planting date and DTM in any geography. Note that the calculations above apply to spring crops. Winter crop development is complicated by dormancy during the winter and will be discussed in more detail later.

[0084] In a particular embodiment, the daily degree of growth is calculated between the emergence date and the harvest date by calculating the daily degree-day accumulation divided by UDDMAT. The accumulation of degree of growth starts at 1.0 (UEMDATE) and ends at 2.0 (UMATDATE). The accumulated degree of growth (AGF) is then mapped to the relevant crop phenology scale (e.g., 9 to 89 on the BBCH scale).

[0085] 3.4 User Crop Maturity Date (Winter Crops) In certain embodiments, the derivation of the User Crop Maturity Date (UMATDATE) for winter crops is similar to that for spring crops, except for the winter period of slower growth. At higher latitudes, crop development is delayed during the cooler winter months due to lower temperatures and fewer hours of daylight. This period of slower growth is referred to herein as "dormancy" and its length is determined by the crop's sensitivity to photoperiod and day length. In certain embodiments, the crop phenology model calculates the photoperiod sensitivity (R) using a dormancy (DORM) function defined as follows: P ) and day length (L P ) consider: DORM=1-0.002*R P *(20-L P ) 2 ,Unitless Equation 10 In the above formula, DORM is a unitless scalar, 0.0002 is the time -2 is a coefficient in units of R P is the photoperiod sensitivity as a unitless scalar, L P is the day length in hours.

[0086] DORM considers photoperiod sensitivity and day length to the rate of crop development during the phenological stages between emergence and flowering. DORM ranges between 1 (no developmental retardation) and 0 (complete cessation of development). In certain embodiments, the temperature-derived daily optimal developmental degree days (DOPT) of winter crops is multiplied by the daily dormancy function value (DORM) to derive the daily actual developmental degree days (DACT).

[0087] DACT reflects the slowing of development due to a substantial reduction in degree days on a given day, determined by the photoperiod sensitivity of the crop and the day length at the field location. Fewer degree days would require more days in the winter to achieve the same pace of development as a crop that does not experience dormancy. DACT is defined as: DACT=DOPT*DORM, degree day expression 11

[0088] To calculate the user crop maturity date for winter crops, UDDMAT is adjusted in certain embodiments to account for dormancy during the growing season. For spring crops, UDDMAT is the required degree-days between the emergence date and the maturity date based on the user DTM and the assumption that the planting date for that variety followed historical practice. Without dormancy, UDDMAT corresponds to the sum of DOPT degree-days between the user emergence date and the maturity date, as follows: UDDMAT = Σ(DOPT degree-days between UEMDATE and UMATDATE), degree-days Equation 12

[0089] In the presence of dormancy, UDDMAT, in certain embodiments, includes the sum of two: the first sum is the actual development (DACT) degree days between the user emergence date and the flowering date, and the second sum is the optimal development (DOCT) degree days between the user flowering date and the maturity date. UDDMATD corresponds to UDDMAT adjusted for dormancy. UDDMATD = Σ(DACT degree-days between UEMDATE and UFLDATE) + Σ(DOPT degree-days between UFLDATE and UMATDATE), degree-days Equation 13

[0090] The derivation of UDDMATD causes the User Crop Maturity Date (UMATDATE) for winter crops to be calculated by adding UDDMATD in the equivalent number of days in the period defined by the degree days to the User Emergence Date (UEMDATE) as follows: UMATDATE=UEMDATE+UDDMATD(day),DOY Formula 14

[0091] 3.5 User Crop Maturity Date and Equivalence The degree days between the user flowering and maturity dates may be further adjusted for isocarpicity. In certain embodiments, the crop phenology model accounts for isocarpicity by including the daily degree day requirements between flowering and maturity according to the developmental delay. In certain embodiments, such inclusion is accomplished in three steps. The first step is to calculate the degree days remaining (DACC) from the calendar date when the crop's crop growth factor (CGF) is 1.5 or greater to the climatologically derived maturity date (MATDATE). DACC = Σ(degree days between CGF1.5 date and MATDATE), degree days Equation 15

[0092] The second step is to calculate the number of accumulated degree days (DCLI) between the climatologically derived CGF of 1.5 and the maturity date (MATDATE). DCLI = Σ(degree days between CGF1.5 date and MATDATE), degree days Equation 16

[0093] In the third step, if DCLI is greater than DACC, there are not enough predicted degree days to reach crop maturity due to a significant delay in development at flowering. The ratio of DCLI / DACC is a scalar called the "Accelerator" (ACC) whose value is constrained to be greater than 1.0. DACC is multiplied by the accelerator ratio to adjust the accumulated degree days (ADACC) between the day when the CGF is 1.5 and the MATDATE. ADACC=ACC*DACC, degree day formula 17

[0094] In certain embodiments, the degree days of a crop during the growing season are increased by the accelerator each day after the CGF reaches 1.5. By including a degree day, the crop during the growing season increases or accelerates the degree day accumulation to maturity. When the CGF accelerates the development of a crop between 1.5 and maturity, it is called isocarpic.

[0095] 3.6 User Crop Harvest Date For both spring and winter crops, seed manufacturers rarely publish the number of days from maturity to harvest for a particular crop variety (e.g., type of crop). The length of the period between maturity and harvest may vary depending on the user planting date, variety selection, and weather conditions prevailing during the growing season. As a result, the degree-day accumulation for the period between maturity and harvest, as well as the period between planting and emergence, may be climatologically derived using, for example, 10 years of historical average temperatures. The derivation of this degree-day accumulation assumes that the crop was planted within a range of dates consistent with historical practice at a given location. The climatologically derived accumulated degree-days (DDHRV) between the 50% cohort maturity date (MATDATE50%) and the 50% cohort harvest date (HRVDATE50%) are: DDHRV=Σ(degree days between MATDATE50% and HRVDATE50%), degree days Formula 18

[0096] The User Crop Harvest Date (UHRVDATE) is calculated by adding the DDHRV in equivalent days for the period defined by the accumulated degree days up to the User Maturity Date (UMATDATE). UHRVDATE=UMATDATE+DDHRV(day),DOY Formula 19

[0097] In a particular embodiment, the daily degree of growth is calculated between the maturity date and the harvest date by calculating the daily degree-day accumulation divided by the DDHRV. The degree of growth accumulation starts at 2.0 (UMATDATE) and ends at 3.0 (UHRVDATE) and spans the BBCH range of 89 to 99 based on the variety mapping of BBCH codes to accumulated degree of growth (AGF).

[0098] FIG. 6 is a flow diagram 600 illustrating a field-level crop phenology model that utilizes the modeling parameters and simultaneous equations described and defined above to describe the various inputs and outputs at each stage of the model.

[0099] 7 is a flow diagram illustrating a method 700 for simulating a developmental stage of a plant according to an embodiment of the present disclosure. Method 700 may be performed by processing logic including hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed on a processing device to perform a hardware simulation), or a combination thereof. In certain embodiments, one or more elements of method 700 may be performed, for example, by a modeling server (e.g., crop yield modeling component 140 of modeling server 130).

[0100] At block 710, a processing device (e.g., a processing device of modeling server 130) receives input (e.g., over network 105) from a user's device (e.g., one or more of user devices 120A-120Z via respective user interfaces 122A-122Z). In certain embodiments, the input includes one or more crop varieties (e.g., one or more crop type identifiers) and a geographic location where the crop is grown.

[0101] In certain embodiments, the input does not specify a planting date for the crop. In other embodiments, the user input specifies an actual planting date or a planned planting date. In certain embodiments, the geographic location is specified as a geopolitical location such as a country, state / province, city, or town. In certain embodiments, the geographic location is specified by latitude and longitude or a Global Positioning System range. In certain embodiments, the location information is obtained directly from the device (e.g., the location of the device at the time of input) so that the user does not need to directly input the location.

[0102] At block 720, the processing device calculates a planting date for the crop based on the value of either the yearly climate index (Equation 1) or the winter climate index (Equation 2) for the specified geographic location. In certain embodiments, the climate index model is generated by modeling historical planting dates for the crop type as a function of the climate index.

[0103] At block 730, the processing device calculates the emergence date based on the planting date and the soil temperature model. In certain embodiments, the processing device simulates soil temperature at the sowing depth for the crop type. In certain embodiments, the processing device calculates the emergence date by estimating the number of days from the planting date of the crop to reach a planned stage of growth for the crop that represents emergence based on the soil temperature model.

[0104] At block 740, the processing device determines / calculates (e.g., simulates) the plant development stage of the crop as a growth profile based on the planting date, emergence date, and degree-day accumulation model. In certain embodiments, the processing device simulates the phenological development of the crop to maturity using the degree-day accumulation model to determine the date that the phenological stage will be reached at the particular location and in the particular growing season. The projected number of degree days is determined based on historical crop maturity data (which may be retrieved, for example, from data store 110). In certain embodiments, the processing device calculates the maturity date of the crop based on the predicted number of days and the emergence date. In certain embodiments, the processing device adjusts the winter growing degree days to reflect the photoperiod sensitivity of the winter crop.

[0105] In certain embodiments, the processing device transmits the growth profile to a user's device for display. In certain embodiments, the processing device transmits the growth profile to a separate computing device for further modeling or for generating a recommended chemical to apply to the crop. In certain embodiments, the processing device transmits the growth profile to a device adapted to operate a tool to harvest or process the crop.

[0106] Crop Yield Modelling Certain embodiments of the present disclosure relate to crop yield models that provide a simulation of end-of-season potential yield for a given growing season based on environmental stresses and management practices that may affect crop development at a field scale. Timing of chemical treatments and on-farm management decisions may be made during periods when crop stress is known to occur. Knowledge of the timing of stress and the magnitude of the impact on crop yield may provide a powerful decision-making tool for growers.

[0107] In certain embodiments, the crop yield model starts with a potential yield that reflects the user's (e.g., crop grower's) environmental constraints (e.g., considering the local soil and climate), and assumes that the user selected the appropriate crop variety for the growing season and managed the farm in an optimal manner. When an environmental stressor occurs, the end-of-season yield decreases depending on the magnitude and timing of the stress event. FIG. 8 is a plot showing how potential yield can be calculated by starting with the genetic potential yield and ending with the potential yield predicted by applying various subtraction penalties. For example, in certain embodiments, the crop yield model starts with the maximum possible yield, and when stress occurs, its effect is subtracted from the maximum possible yield. Stresses include, for example, environmental stresses, developmental stresses, and event stresses. FIG. 8 shows an exemplary order in which stresses are applied. Stresses include, for example, environmental stresses, developmental stresses, and event stresses. The model can be run under various scenarios, i.e., historical, pre-season, and in-season scenarios.

[0108] 1. Model Input In certain embodiments, user inputs to the crop yield model include location, planting date, crop type, days to maturity (DTM) of the variety, and soil type classification. Based on the user inputs provided, other parameters such as those listed in Table 2 may be identified as model inputs and retrieved to run the simulation. [Table 2]

[0109] In certain embodiments, the crop yield model utilizes weather input data, such as the various parameters listed in Table 3. From the user inputs, derived weather variables can be calculated, including day length (based on the user's location and date), maximum clear sky total incident solar radiation (for the user's location), actual clear sky total incident solar radiation (the amount received on any given day based on cloud cover), and open water evaporation. In addition to the direct and derived weather inputs, derived soil temperature and daily accumulated growing degree days can be calculated. [Table 3]

[0110] In certain embodiments, the crop yield model distinguishes between "ideal" and "actual" crop yield scenarios. "Ideal" crop yield corresponds to the maximum potential yield for a given crop type and variety without limitations on development beyond those related to the limitations of the user's location (e.g., climatological and soil-influenced characteristics). Beyond these limitations, "actual" crop yield results from developmental stresses throughout the growing season (e.g., heat, soil moisture, radiation) in conjunction with event stresses (e.g., winter kill, frost / freeze events) that affect crop development and potential yield.

[0111] In certain embodiments, crop development takes into account a user-defined or predicted planting date (as described above with respect to the phenology model embodiment) and varietal information in the form of days to maturity (DTM). The DTM may be converted to the number of growing degree days (DDMAT) required for the crop to reach maturity based on local climatology. In certain embodiments, crop emergence may be based on soil temperature, and crop development from emergence to maturity may be based on daily accumulation of degree days until the maturity value is reached. Soil and air temperatures for the "actual" crop may be based on observed values ​​for the current season.

[0112] The planting date and derived growing degree days to reach maturity may also be used to model an "ideal" crop. In certain embodiments, the "ideal" crop development is based on accumulated climatological averages (e.g., 10-year averages) of daily degree days and soil temperature, which represent the average weather conditions during the growing season and not the conditions of a particular year. This allows the "ideal" crop to be insensitive to weather fluctuations that may occur in a particular season. The emergence of the "ideal" crop may differ from the "actual" crop because the emergence of the ideal crop is determined by historical average soil temperatures and the actual crop is determined by the soil temperatures of a single season. In certain embodiments, the "ideal" crop requires the same number of accumulated degree days to progress from emergence to reaching maturity as the actual crop, as defined by the climatological degree days indicated by the DTM values. The difference in the dates of the phenological stages between emergence and maturity between the "ideal" and "actual" crops is due to the difference in the accumulation of degree days between the historical period and the single season period.

[0113] In addition to monitoring crop phenology through the CGF or another growth stage scale value (e.g., BBCH value) mapped to the CGF, crop development can also be monitored using the Leaf Area Index (LAI) starting after emergence, which can be used for both "ideal" and "real" crops. In certain embodiments, for each crop type, there is a maximum LAI value determined by past observations. Within the model, the maximum LAI value occurs when the degree-day accumulation reaches a defined, crop-specific percentage of the total number of accumulated degree-days required to reach maturity (DDLAI). Each day, the LAI percentage is calculated based on the daily degree-day accumulation (i.e., during the growing season for "real" crops and 10-year climatological for "ideal" crops) divided by the DDLAI.

[0114] In certain embodiments, crown temperature is a variable for winter crops that is used to determine the overwinter survival rate of plants. As used herein, crown temperature refers to the temperature of the soil adjacent to the growing point of the plant, i.e., the interface between the root tissue and the stem tissue. In the model, this depth may be assumed to be 2 cm for most annual crops. In certain embodiments, the function to calculate crown temperature uses snow depth and air temperature as inputs. Crown temperature may be calculated between germination and maturity.

[0115] FIG. 9 shows the relationship between air temperature and crown temperature as a function of snow depth (FIG. 2 is from Zheng et al., “The APSIM-Wheat Module (7.5 R3008),” 2015, apsim.info). In certain embodiments, the crown temperature function uses the relationship shown in FIG. 9 by calculating the slope of a line representing the snow depth gradient (SDS) relating air temperature to crown temperature.

[0116] 2. Environmental stress and variety restrictions As discussed above, the "ideal" crop is not subject to any environmental stresses other than those related to the user's location (e.g., less than ideal soils will result in reduced potential yield, the location has a shorter growing season due to latitude, etc.). Starting from the same planting date as the "ideal" crop, the "actual" crop will achieve less potential yield after accounting for stresses that occur during the growing season. In certain embodiments, the yield of the "actual" crop is less than the yield of the "ideal" crop.

[0117] The "ideal" crop can be an important reference for the "real" crop. First, the development of the "real" crop can be adjusted against the development of the "ideal" crop, i.e., by referring to the "ideal" crop, it can be determined whether the real crop develops faster or slower during the growing season. Second, the "ideal" crop increases its biomass every day before it is reduced by real crop stress.

[0118] In certain embodiments, the "real" crop works on the assumption of a partial yield loss. That is, if there is no stress, the partial yield loss is 0. If there is cumulative or extreme stress, the partial yield loss can reach 1.0. Starting from emergence, there is a maximum above-ground growth for each day defined by the ideal crop. If there is stress on a given day, the maximum growth is reduced by some percentage. This reduction is referred to herein as the partial yield loss. On a daily basis, this loss can range from 0 to 1.0. In certain embodiments, the daily partial losses are added throughout the growing season to determine the seasonal partial yield loss. The actual partial yield can be calculated by subtracting this seasonal partial yield loss from 1.0. The "real" partial yield is multiplied by the "delayed planting" potential yield to derive the actual absolute yield. Delayed planting yield is discussed in more detail below.

[0119] In certain embodiments, because potential yield at harvest is dependent on variety selection, the actual absolute yield is normalized against a relative historical average yield (e.g., for a minimum of 10 years). Relative yield may be reported to the grower as a percentage that is above 1.0 for a relatively good growing season, or below 1.0 for a relatively poor growing season. The relative yield may be multiplied by the farm's historically determined average yield to derive the grower's local actual absolute yield.

[0120] 2.1 Genetic Yield In a particular embodiment, the genetic yield (GENYLD) is set according to the maximum number of degree days associated with the theoretical maximum yield value of the selected hybrid. This assumes that all weather, soil, physiological, and management conditions are perfect and there are no stresses (i.e., conditions found in a controlled greenhouse environment). Genetic yields in tonnes / hectare and bushels / acre for selected crops are listed below in Table 4. [Table 4]

[0121] 2.2 Yield as influenced by soil In certain embodiments, soil influenced yield (EDYLD) is the adjustment of genetic yield for soils that are not ideal for crop growth and development. In certain embodiments, only soil type reduces genetic yield. Genetic yield is multiplied by the soil influenced yield percentage to derive soil influenced yield. The soil influenced yield percentage is a function of the soil suitability rating for the soil type. Exemplary soil influenced yield percentages are shown in Table 5. The soil influenced yield percentage may vary by crop type. [Table 5]

[0122] 2.3 Phenological yield Every location has an ideal planting date, emergence date, and maturity date based on either the Winter Climate Index (WCI) or the Climatic Annual Ratio (RATIO), as described above with respect to the crop phenology model (not associated with the "ideal" crop above). Phenological yield (PHENYLD) also assumes that the grower has selected the optimal variety for the location, i.e., the variety that maximizes the length of the growing season and takes full advantage of the solar radiation available for photosynthesis. In a particular embodiment, the ideal planting and emergence dates allow this optimal variety to reach inflorescence on June 21 in the Northern Hemisphere (December 21 in the Southern Hemisphere). In this context, the model considers the total accumulation of maximum clear sky radiation (MCSRC) that can occur between the emergence and maturity dates, centered around the summer solstice. Figure 10 shows a representation of the distribution of maximum clear sky radiation between the ideal planting and maturity dates, with inflorescence occurring on June 21. The total amount of accumulated clear sky radiation is a function of the user's latitude and longitude and is represented by the area under this curve. In a particular embodiment, phenological yield (PHENYLD) is the maximum potential yield for a given hybrid and location in any year and can be calculated as a function of soil-influenced yield (EDYLD) and maximum clear sky radiation (MCSR). Figure 11 shows a graph representing phenological yield. The curve represents the daily potential yield increment and the area under the curve is the seasonal potential yield. Panel (a) of Figure 11 shows the evolution of phenological yield.

[0123] 2.4 Delayed planting yield In certain embodiments, delayed planting yield (PLDYLD) takes into account the fact that most planting dates do not result in the longest period between emergence and maturity being centered on the day (June 21 in the Northern Hemisphere). PLDYLD also takes into account whether the grower has selected the appropriate variety. Panel (b) of FIG. 11 demonstrates the impact of delayed planting of the ideal variety on potential yield, and the potential impact if the crop does not reach maturity (the curves shift to the right and decrease in magnitude). The grower has the option to plant a shorter growing season variety, in which case the ideal planting date discussed above may not be appropriate, as shown by the curve shifted to the left in panel (c) of FIG. 11. In this scenario, delayed planting of the shorter growing season variety would be beneficial, as it would be centered on June 21, maximizing photosynthetic capacity, as shown by the curve shifted back to June 21 in panel (d) of FIG. 11.

[0124] In certain embodiments, PLDYLD is calculated based on the simulated clear sky radiation (SIMCSR) for one day between the user planting date and the simulated maturity date (which varies by variety selected). SIMCSR = Σ(MCSR on the day between planting date and maturity date), MJ / m 2 formula 20

[0125] Although both phenological yield and delayed planting yield may use the same formula to calculate radiation integration, they may yield two different results due to the difference in radiation integration period. Phenological yield is the theoretical maximum yield for the location based on the best planting date and variety selection. In most cases, delayed planting yield is less than the phenological yield value because the actual planting date is not the optimal date to maximize radiation for photosynthesis. By comparing delayed planting yield against phenological yield, the yield loss due to the choice of planting date can be evaluated, with all other conditions (including the variety planted) being equal.

[0126] At this point, the model discussion takes into account planting dates, variety selection, and the geographic constraints of the user's location. With these yield constraints, an "ideal" crop yield is predicted. To obtain the "actual" crop yield, the "ideal" crop yield is subjected to the stress factors discussed below.

[0127] 3. Developmental stress In certain embodiments, developmental stress is calculated on a daily basis and affects yield development for the current day only. In certain embodiments, all developmental stress is calculated on a daily basis between emergence and maturity and is accumulated throughout the growing season to obtain the final end-of-season yield loss. Unless otherwise stated, the formula applies to all crop types.

[0128] 3.1.Growth light After emergence, the crop canopy receives different levels of incoming solar radiation depending on cloud cover. How the crop utilizes that radiation depends on its growth stage. In a particular embodiment, the potential yield on a particular day is a function of the daily maximum clear sky incoming solar radiation (SRSX) (dependent on the user's location) and the leaf area index (LAI) of the canopy. The "actual" crop yield is a function of the actual daily incoming solar radiation (SRST) and a threshold determined by the canopy LAI. Calculation of solar radiation threshold (SRT) to determine maximum potential yield as a function of the ratio of the daily canopy LAI to its maximum value LAI (MAXLAI). The MAXLAI for some crops is listed in Table 6. [Table 6]

[0129] In a particular embodiment, if the actual daily incoming solar radiation (SRST) is equal to or greater than the threshold, the model indicates that the crop will develop to its full potential yield for that day. If the SRST is less than the threshold, the actual yield will be less than the potential. In a particular embodiment, the developmental light yield loss is calculated as a percentage using the solar radiation threshold (SRT) and the actual irradiance (SRST) as follows: Daily developmental light yield loss = (SRT-SRST) / SRT Equation 21 In the above formula, SRST≦SRT.

[0130] In certain embodiments, developmental light yield is determined by integrating the daily developmental light yield loss. Developmental light yield = 1-Σ(Daily developmental light yield loss) Equation 22

[0131] 3.2 Growth fever After emergence, crops may be exposed to excessively high temperatures on a single day. This may include temperatures exceeding either (or both) the defined daily minimum temperature threshold (TMN) or maximum temperature threshold (TMX) between crop emergence and maturity, resulting in yield loss. This exposure to high temperatures is referred to herein as development heat (Dev Heat) yield loss in the crop model. In certain embodiments, two sets of crop-specific temperature thresholds are defined for both the daily minimum temperature (TMN) and the daily maximum temperature (TMX). One threshold in each set defines the temperature at which development heat stress begins, and the other threshold defines when maximum development heat stress occurs. If the daily TMN and TMX are below the threshold for the onset of development heat stress, no development heat stress is indicated. If the daily TMN or TMX temperatures exceed the maximum development heat stress threshold, the maximum level of development heat stress is applied. If TMN and TMX are included in the thresholds between the onset and maximum of heat stress, the daily developmental heat stress is calculated as a function of TMN or TMX during the temperature range defined by the onset and maximum temperature thresholds. Above the crop-specific temperature thresholds (implemented as conditional statements for each crop), the defined developmental heat yield is used. Outside the defined temperature range, the crop-specific developmental heat yield loss can be calculated as a function of the minimum (TMNF) and maximum (TMXF) temperature rate formulas defined for each crop.

[0132] 3.3 Soil moisture for growth After emergence, crop roots go through various levels of soil moisture ranging from the permanent wilting point to field capacity. In certain embodiments, the maximum available moisture for a particular rooting depth (which increases as the crop develops) on a particular day is considered to be the potential soil moisture. The actual available moisture calculated from the daily soil water balance (shown in FIG. 12) is less than or equal to the maximum value. The daily soil water balance takes into account the availability of water to the roots due to effective precipitation, and water loss due to actual crop evapotranspiration. A detailed explanation of how effective precipitation and evapotranspiration are calculated is provided below.

[0133] In certain embodiments, potential yield on a particular day is a function of potential soil moisture and actual yield is a function of actual soil moisture. Yield loss due to soil moisture deficiency (when actual soil moisture is below potential) may be calculated according to Equation 23. Below is an equation for determining if there is a soil moisture deficiency (called the "Water Balance Approach") and outlines the process shown in Figure 12. The equation is divided into two sections, one presented above the soil line on the diagram and one presented below the soil line. Daily soil moisture yield loss = 1-actual available moisture / potential available moisture Equation 23

[0134] In a particular embodiment, the daily increment in developing soil water yield as a percentage is calculated from the loss percentage as follows: Soil moisture yield during development = 1-Σ(soil moisture loss during development in one day) Equation 24

[0135] In certain embodiments, the water balance method calculates daily available soil moisture by adding soil precipitation and subtracting evaporation and crop transpiration, the combined effect of which is commonly referred to as "evapotranspiration."

[0136] In certain embodiments, the accounted soil available moisture is determined from soil texture and crop-specific root zone depth and varies between an upper limit (field capacity) and a lower limit (maximum deficit). There is also an intermediate value before the maximum deficit that indicates the onset of crop stress. If the available moisture falls below the lower limit (maximum deficit), the crop will be irreparably damaged (Figure 12).

[0137] 3.3.1 Above-ground calculations Effective Precipitation Although a certain amount of precipitation may reach the earth's surface, only a small portion of the total amount replenishes the soil's available moisture. This is called "effective" precipitation. The amount of precipitation that reaches the soil surface is a function of the crop canopy density and the evaporation rate of the vegetation surface. The amount of precipitation that enters the soil after interception by the canopy and evaporation from the leaves is called "effective" precipitation, which in certain embodiments is the difference between the total amount of precipitation and the amount of precipitation intercepted by the canopy.

[0138] Evapotranspiration In certain embodiments, the crop coefficient is a scaling factor used to adjust potential evapotranspiration to reflect crop development and subsequent changes in evapotranspiration during the growing season. The crop coefficient assumes that the evaporation rates of both the soil and crop can be scaled to open water evaporation (see below). That is, if either the soil layer or the crop canopy transports water to the atmosphere at a rate equal to the open water, the crop coefficient is set to 1.0. Available moisture in the soil coupled to the crop canopy structure can achieve a crop coefficient from 0 to greater than 1.0.

[0139] The crop coefficient curve mimics the biomass accumulation of the crop as shown in FIG. 13. In a particular embodiment, six coefficients are used to define the curve. The first coefficient is the crop coefficient planted (CCPL) which is equal to the fallow soil at the time of sowing. The second coefficient is the crop coefficient emergent (CCEM) which corresponds to the start of the vegetative growth period. The third coefficient is the crop coefficient reproductive (CCRP) which indicates the end of the vegetative growth period and the maximum leaf area index and the start of the reproductive period. The fourth coefficient is the crop coefficient flowering (CCFL) which indicates the end of the reproductive period and the start of fruit development. The fifth coefficient is the crop coefficient mature (CCMT) which corresponds to the end of fruit development or maturity. The sixth and final crop coefficient is the crop harvest (CCHV) which indicates the end of the growing season but the presence of dead material on the soil surface. It should be noted that the values ​​associated with the crop coefficients vary from crop to crop and the values ​​shown in FIG. 13 are for illustrative purposes only.

[0140] In certain embodiments, for each day, a crop coefficient (CC) is calculated based on the curve shown in Figure 13. For example, if the crop development is between emergence and reproduction, the daily crop coefficient value is interpolated between those values ​​using the daily accumulation of degree days divided by the total number of degree days between emergence and reproduction.

[0141] In a particular embodiment, the evaporation-related process follows the Food and Agriculture Organization (FAO) scheme, which uses a series of steps and coefficients to calculate both soil evaporation and crop evapotranspiration. First, open water evaporation (EOWT) is provided as a variable derived from meteorological data input. The EOWT is multiplied by a fixed coefficient, for example 0.8, to derive a reference crop evapotranspiration (EVRT) (in this case, short grass, which is the standard vegetation cover at the weather station) as follows: EVRT=0.8*EOWT,mm Equation 25

[0142] In certain embodiments, to determine potential evapotranspiration, the EVRT must be adjusted for the crop by multiplying it by a daily crop coefficient (CC), which allows the base evapotranspiration to be adjusted based on the crop's development (from a low rate during the early growth phase to the highest possible rate during the period between reproduction and maturity). EVPT=CC*EVRT,mm Formula 26

[0143] In certain embodiments, the actual evapotranspiration (EVAT) is derived by first calculating a ratio (EVAT2EVPT) that scales the actual evapotranspiration to be less than or equal to the maximum daily potential evapotranspiration limit, and then multiplying that ratio by the potential evapotranspiration (EVPT) as follows: EVAT=EVPT*(EVAT2EVPT),mm Formula 27 In the above equation, the EVAT2EVPT ratio is modeled based on the available soil moisture after precipitation-related processes and the maximum effective moisture above field capacity.

[0144] 3.3.2 Underground calculation In certain embodiments, the soil water balance variables are presented as absolute or relative values. If absolute, the decrease or increase in available moisture (in mm) can be tracked relative to minimum and maximum stress values. This variation in available soil moisture can be translated into daily and cumulative soil moisture deficits.

[0145] In certain embodiments, the soil moisture calculations in the crop yield model are for a single soil layer from the surface to the rooting depth at any date within the growing season. This approach is commonly referred to as the "water trough." This designation emphasizes that all moisture in the root zone is immediately available to the crop, regardless of its depth below the surface.

[0146] In certain embodiments, a phenological model is used to adjust the root zone throughout the growing season depending on the stage of the crop. Starting at planting, the root zone steadily increases from the seeding depth to a maximum defined for each crop type. The root zone determines the maximum soil moisture available to the crop.

[0147] In certain embodiments, prior to planting the crop, the model assumes bare soil that can store a predetermined amount of water that can be lost through evaporation in the soil, based on soil texture. This predetermined amount of water is calculated as a percentage of the maximum water that can be stored in the root zone, based on the soil's water capacity. For example, if the soil's water capacity is 0.18 mm of water per mm of soil depth, then 100 mm of soil will have 18 mm of water available for evaporation in the soil. The amount of water stored in a particular soil will vary depending on soil texture and other physical properties.

[0148] After planting, the soil moisture available to the crop on the first day is at the sowing depth, which is usually in the range of 25.4-50.8 mm (1-2 in). For example, assuming a water capacity of 0.18, at a soil depth of 25.4 mm, there is 4.572 mm of available moisture (25.4 mm soil × 0.18 mm water / mm soil). As roots grow, the available soil moisture increases accordingly. As the growing season progresses, the available soil moisture reaches a crop-specific maximum defined by the maximum rooting depth (root zone). For example, the maximum rooting depth for wheat is 508 mm. Assuming a water capacity of 0.18 mm per mm of soil, the maximum available moisture in the root zone is 91.44 mm (508 mm soil × 0.18 mm water / mm soil).

[0149] In certain embodiments, at each time point during the growing season, the amount of available water in the field capacity (AWFC) is calculated based on soil type (available water holding capacity or AWHC) and rooting depth (CRD). AWFC=AWHC*CRD,mm Formula 28

[0150] In a particular embodiment, the actual available soil moisture is determined by precipitation and evaporation-related processes occurring simultaneously in the environment. In the yield model, these processes are considered additively and iteratively. Thus, a daily calculation cycle of available soil moisture starts from the final total moisture of the previous day as follows: AWT1 t =AWT3 t-1 ,mm Equation 29

[0151] The effective precipitation is then added to the available soil moisture as follows: AWT2 t =AWT1 t +EPCP,mm formula 30

[0152] Water retention occurs when the available precipitation is greater than the field capacity. Water retention is a short-term event where there is a certain amount of moisture remaining at the soil surface after a rainfall event. Water retention is possible until a maximum moisture depth (PONDMX) occurs at the soil surface. In a specific embodiment, this maximum is 25.4 mm. The maximum amount of moisture due to water retention (PONDMX) is added to the available moisture at field capacity (AWFC) to derive the maximum available moisture above field capacity (AWFCX) as follows: AWFCX=AWFC+PONDMX,mm Formula 31

[0153] Available soil moisture (AWT2 t ) is greater than the maximum available water over field capacity (AWFCX), then potential water storage is exceeded and the remaining water is considered runoff. RUNOFF=AWT2 t -AWFCX, but AWT2 t >AWFCX,mm formula 32

[0154] Otherwise, AWT2 t When is less than or equal to AWFCX, it represents the soil moisture balance after precipitation-related processes.

[0155] Actual evapotranspiration is subtracted from the available soil moisture after considering precipitation-related processes as follows: AWT3 t =AWT2 t -EVAT,mm formula 33

[0156] In certain embodiments, available soil moisture (AWT2 t ) is smaller than the actual evapotranspiration, the available water AWT3 t is set to 0. At this point, the daily calculation cycle of available soil moisture is completed and the final AWT3 t This is the starting point for the next day's calculation.

[0157] In certain embodiments, the ratio of actual to potential evapotranspiration (EVAT / EVPT) is considered to be equal to the ratio of actual to potential soil moisture, which is used to calculate developmental stress on yield due to soil moisture.

[0158] 4. Event Stress 4.1 Winter withering Winter crops and perennial plants must tolerate low temperature-related stress in the winter months during long periods of low temperature or become susceptible to winter kill. In certain embodiments, an accumulation of "days of cold tolerance" is input into the calculation of minimum survival temperature, which is an input into the calculation of winter kill yield loss.

[0159] In certain embodiments, hardy days are accumulated when crop development reaches a defined crop-specific initial vegetative growth state (such as the three-leaf stage (BBCH13) for winter wheat and winter barley). For each day after a particular growth stage, if the crown temperature (described above) on any day is 9° C. or less, that day is counted as a hardy day.

[0160] The minimum survival temperature of winter crops is highly dynamic from year to year. It depends not only on the cold acclimation of the crop, but also on snow cover and, to some extent, on soil moisture. Figure 14 shows the minimum survival temperature of winter wheat varieties with low, medium and high cold hardiness. One inch (25.4 mm) soil temperature is overlaid on the minimum survival temperature curve (Source: Manitoba Agricultural Meteorology Program). In certain embodiments, day length and cold hardiness days (CHD) are inputs into the simulated minimum survival temperature curve.

[0161] Winter kill occurs when the canopy temperature drops to or below the minimum survival temperature. In certain embodiments, winter kill days (WKD) are counted when the canopy temperature is at or below the minimum survival temperature. The accumulated winter kill days determine the level of cold stress during the winter season. Winter kill yield (WYL) may be expressed as: WYL=1-Σ(winter kill yield loss per day) Equation 34 It can also be calculated as a function of the accumulated WKD, the daily crown temperature (CROWNT), and the daily minimum survival temperature (MST).

[0162] 4.2 Spring or autumn frost / freeze After emergence, whether winter crops planted in autumn or spring, the leaves of the crop may be exposed to frost or freeze, which may result in yield loss until the crop reaches maturity. In certain embodiments, if the crop is affected by a frost / freeze event, the calculation of the yield loss depends on the type of crop. In certain embodiments, frost / freeze yield loss for corn and soybean is calculated considering damage to the leaves as a function of surface soil temperature. In certain embodiments, surface soil temperature (ST0A) is used because it better represents the radiant heat loss experienced by young seedlings. For all other crops, the yield loss may be modeled using the minimum temperature of the day and a crop-specific temperature threshold defined for yield loss. A spring or fall frost / freeze may be considered a yield loss event and persists through the remainder of the growing season. A spring or fall frost / freeze is of concern between crop growth stages 1.0 and 2.0 (emergence and maturity), and the degree of yield loss is defined by a crop-specific function and threshold.

[0163] 4.3 Surface water loss After emergence, crops may experience heavy rain events that saturate the soil. Saturated soils prevent oxygen from reaching the roots, resulting in reduced yields. To account for saturated soils, the soil water balance takes into account the retention of surface water. The presence of surface water indicates that soil moisture is greater than the Aquatic Field Carrying Capacity (AWFC).

[0164] Surface water loss occurs when the available moisture is between the soil field capacity (AWFC) and a defined maximum water retention depth (PONDMX), which in a particular embodiment is set at 25.4 mm. If on a given day between the emergence and reproductive stages the soil water balance exceeds the limit at the aboveground part, that day may be indicated as a saturated water day. In a particular embodiment, if the accumulated saturated water days exceed 16 days by the first reproductive growth stage, the daily yield increment between reproductive and maturity is reduced by 75%. This yield loss persists through the remaining growing season until the crop matures and may be expressed as: Surface water loss yield = 1 - surface water loss Equation 35

[0165] 4.4 Crown loss If the "real" (simulated) crop develops earlier than the "ideal" (climatological) crop before reproduction, the "real" crop canopy will not reach its full potential biomass, and therefore potential yield will be reduced. This yield reduction is due to reduced clear sky solar radiation due to the shortened period between emergence and reproduction stages (not being able to photosynthesize sufficiently) relative to the "ideal" crop growth stage interval. In certain embodiments, the canopy yield loss is a function of the integrated clear sky irradiance for the shortened "real" crop period relative to the integrated clear sky irradiance for the "ideal" crop period. In certain embodiments, the integrated clear sky irradiance for the growing period between emergence and reproduction of the "real" crop may be updated daily, since daily weather observations are replaced by predicted daily weather forecasts that may change the development of the "real" crop growth stages. The canopy yield may be expressed as: Canopy Yield = 1-Σ(Daily Canopy Yield Loss) Equation 36

[0166] 4.5 Fruiting loss If a real crop experiences extreme heat combined with soil moisture stress during the flowering stage, poor fruit set (reduced seed number) will occur, resulting in reduced yield. In a particular embodiment, this yield reduction is calculated in two steps. In the first step, a three-day moving average of the daily maximum temperature is calculated for each day between growth stages 1.45 and 1.55 (from just after flowering begins until about 50% flowering). Each day, the three-day average temperature is compared to a crop-specific maximum temperature threshold (TMXT), which indicates the onset of heat stress.

[0167] In a second step, fruit set yield losses for the remainder of the season can be calculated as a function of the percentage of maximum temperature (TMXF) and the 3-day average available soil moisture (AW3DA) for that period. The percentage of maximum temperature is a stress index that scales temperatures above TMXT to a range of 0 to 1 using a crop-specific divisor that defines the degree of heat stress per degree for temperatures above a threshold.

[0168] In a particular embodiment, the daily increment in fruit set yield is calculated and integrated over a crop growth period of 1.45 to 1.55 (early flowering to 50% flowering) and used to calculate fruit set yield for that season. Fruit yield = 1-Σ(Daily fruit yield loss) Equation 37

[0169] 4.6 Ripening loss If the actual crop development is slower than the ideal crop, there is insufficient time for carbohydrate translocation to ripen. There may be sufficient fruit set (number of seeds), but the average size of all seeds is smaller than the potential size. In certain embodiments, the assessment of ripening loss begins when the ideal crop reaches the reproductive stage (e.g., growth degree = 1.5). On the day when the "ideal" crop reaches a growth degree of 1.5, the "actual" crop development is compared to the "ideal" crop development. If the "actual" crop growth degree is lower than the "ideal" crop growth degree on that day, there will be ripening loss.

[0170] In a particular embodiment, grain filling loss is determined in two steps: First, the grain filling loss ratio (SFLR) of the accumulated "actual" crop yield percentage (Σ(actual crop yield)) to the accumulated "ideal" crop yield percentage (Σ(ideal crop yield)) is calculated for the days when the "ideal" crop growth rate is 1.5 (early flowering) and preceding the "actual" crop growth rate, as follows: SFLR = Σ(actual crop yield) / Σ(ideal crop yield) Equation 38 In the above formula, the yield accumulation period is the period between emergence and early flowering (maturity=1.5) for the ideal crop, and for the "real" crop, it is the period from the date of emergence to the date when the "ideal" crop reaches early flowering. If the maturity of the "real" crop is higher than that of the "ideal" crop, the SFLR is set to 1.0.

[0171] In certain embodiments, grain filling yield loss is simply the difference between 1.0 and the grain filling loss ratio (SFLR) as follows: Grain filling yield loss = 1.0-SFLR Equation 39

[0172] The daily fractional increment of filled yield as a percentage is calculated from the loss percentage as follows: Grain-filled yield = 1 - Grain-filled yield loss = SFLR Equation 40

[0173] 15 is a flow diagram illustrating a method 1500 for determining / calculating (e.g., simulating) a crop yield for a growing season according to an embodiment of the present disclosure. Method 1500 may be performed by processing logic including hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed on a processing device to perform a hardware simulation), or a combination thereof. In certain embodiments, one or more elements of method 1500 may be performed, for example, by a modeling server (e.g., crop yield modeling component 140 of modeling server 130).

[0174] At block 1510, a processing device (e.g., a processing device of modeling server 130) receives input (e.g., over network 105) from a user's device (e.g., one or more of user devices 120A-120Z via respective user interfaces 122A-122Z). In particular embodiments, the input includes a crop type (e.g., an identifier for one or more crop types), a planting date of the crop, and a geographic location where the crop is grown.

[0175] In certain embodiments, the processing device calculates or retrieves a number of parameters utilized to calculate an ideal crop potential yield, in certain embodiments, the parameters represent weather conditions, soil properties, and crop-specific growing degree days required for the crop to mature, the parameters being derived at least in part from the crop type, crop planting date, and geographic location in combination with historical weather and crop observations.

[0176] In certain embodiments, the input does not specify a planting date for the crop. In other embodiments, the user input specifies an actual planting date or a planned planting date. In certain embodiments, the geographic location is specified as a geopolitical location such as a country, state / province, city, or town. In certain embodiments, the geographic location is specified by latitude and longitude or a Global Positioning System range. In certain embodiments, the location information is obtained directly from the device (e.g., the location of the device at the time of input) so that the user does not need to directly input the location.

[0177] At block 1520, the processing device calculates an ideal crop potential yield based at least in part on the crop type, planting date, geographic location, and the phenology model. In certain embodiments, the ideal crop potential is calculated as a profile representing crop potential yield as a function of date, for the geographic location, that represents a maximum potential yield calculated without including developmental, environmental, and event stresses. In certain embodiments, the processing device calculates the phenology model by simulating plant development stages of the crop based on the planting date, estimated emergence date, and degree-day accumulation model. The phenology model may be calculated according to method 700, for example.

[0178] At block 1530, the processing device calculates a stress model based at least in part on developmental and event stresses predicted or observed to occur during the growing season. In certain embodiments, the processing device calculates the developmental stresses based at least in part on radiation loss conditions, extreme temperature conditions, and water loss conditions predicted during the growing season. In certain embodiments, the processing device calculates the event stresses based at least in part on a fruit set loss model and a grain filling loss model. In certain embodiments, the processing device calculates the event stresses based at least in part on a soil moisture model and a canopy development loss model.

[0179] At block 1540, the processing device calculates the actual crop yield by applying the stress model as a curtailment penalty to the ideal crop yield potential.

[0180] In certain embodiments, the processing device transmits the actual crop yield to a user's device for display. In certain embodiments, the processing device transmits the growth profile to a separate computing device for further modeling or for generating recommended chemicals to apply to the crop. In certain embodiments, the processing device transmits the growth profile to a device adapted to operate a tool to harvest or process the crop.

[0181] It is to be understood that the embodiments described herein are not limited to use in any particular application or environment, and that modifications can be made to the disclosed embodiments without departing from the spirit and scope of the disclosure. Although the disclosure has been described herein with reference to particular embodiments in particular environments for particular purposes, those skilled in the art will recognize that its utility is not limited in this respect, and that the disclosure may be beneficially implemented in any number of environments for any number of purposes.

[0182] For ease of explanation, the methods of the present disclosure are depicted and described as a series of operations. However, operations according to the present disclosure may occur in various orders and / or simultaneously and together with other operations not shown and described herein. Moreover, not all illustrated operations may be required to perform a method in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that a method may alternatively be represented as a series of interrelated states via a state diagram or events. In addition, it should be understood that the methods disclosed herein may be stored on an article of manufacture to aid in transporting and transferring instructions for performing such methods to a computing device. As used herein, the term "article of manufacture" is intended to encompass a computer program accessible from any computer-readable device or storage medium.

[0183] In the above description, numerous details are set forth. However, it will be apparent to one skilled in the art having the benefit of this disclosure that the present disclosure may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the present disclosure.

[0184] Some portions of the detailed descriptions may be presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. These steps require physical manipulations of physical quantities. Usually, although not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of convention, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[0185] It should be noted, however, that all of these and similar terms should be associated with the appropriate physical quantities and are merely convenient labels applied to those quantities. As is apparent from the above discussion, unless specifically stated otherwise, discussions throughout this specification utilizing terms such as "compose," "receive," "convert," "produce," "stream," "apply," "masking," "display," "read," "transmit," "calculate," "generate," "add," "subtract," "multiply," "divide," "select," "analyze," "optimize," "calibrate," "detect," "store," "execute," "analyze," "determine," "enable," "identify," "modify," "convert," "aggregate," "extract," "execute," "schedule," "simulate," and the like, should be understood to refer to operations and processes of a computer system or similar electronic computing device that manipulates and transforms data represented as physical (e.g., electronic) quantities in the registers and memory of the computer system into other data similarly represented as physical quantities in the memory or registers of the computer system, or other such information storage, transmission, or display device.

[0186] The present disclosure also relates to an apparatus, device, or system for performing each of the operations herein. The apparatus, device, or system may be specially constructed for the required purpose, or may include a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer-readable or machine-readable storage medium, such as any type of disk, including, but not limited to, a floppy disk, an optical disk, a compact disk read-only memory (CD-ROM), and a magneto-optical disk, a read-only memory (ROM), a random access memory (RAM), an EPROM, an EEPROM, a magnetic or optical card, or any type of medium suitable for storing electronic instructions.

[0187] The term "example" or "exemplary" is used herein to mean serving as an example, illustration, or illustration. Any aspect or design described herein as "example" or "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the word "example" or "exemplary" is intended to present concepts in a concrete manner. As used in this application, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from the context, "X includes A or B" is intended to mean any of the natural inclusive arrangements. That is, if X includes A; X includes B; or X includes both A and B, then "X includes A or B" is satisfied under any of the foregoing examples. In addition, as used in this application and the appended claims, the articles "a" and "an" are generally to be construed to mean "one or more" unless otherwise specified or clear from the context to refer to the singular form. Throughout this specification, reference to an "embodiment" or "one embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrases "embodiment" or "one embodiment" in various places throughout this specification do not necessarily all refer to the same embodiment. Furthermore, it should be noted that the designation "A-Z" used to refer to particular elements in the drawings is not intended to limit the particular number of elements. Thus, "A-Z" should be interpreted as one or more of the elements being present in a particular embodiment.

[0188] The present disclosure is not limited in scope by the specific embodiments described herein. Indeed, other various embodiments and modifications of the present disclosure, other than those described herein, will become apparent to those skilled in the art upon reading this specification and the accompanying drawings. Accordingly, such other embodiments and modifications are intended to be included within the scope of the present disclosure. Moreover, while the present disclosure has been described in connection with particular embodiments in particular environments for particular purposes, those skilled in the art will recognize that its usefulness is not limited thereto, and the present disclosure may be beneficially implemented in any number of environments for any number of purposes. Accordingly, the claims set forth below should be construed in light of the full scope and spirit of the present disclosure as described herein, along with the full range of equivalents to which such claims are entitled.

[0189] The present invention can also be represented by the following embodiments. Embodiment 1: 1. A method for determining, calculating, or simulating crop yield for a growing season, the method comprising: receiving input from a user's device, database, or sensor, the input including a type of crop, a planting date of the crop, and a geographic location where the crop is grown; Calculating an ideal crop potential yield based at least in part on the crop type, planting date, geographic location, and the phenology model; calculating a stress model based at least in part on developmental and event stresses predicted or observed to occur during the growing season; Calculating the actual crop yield by applying the stress model as a reduction penalty to the ideal crop potential yield; The actual crop yield, the user's device for display; a separate computing device for further modeling or for generating recommended chemicals to apply to the crop; or A device adapted to operate a tool for harvesting or processing a crop and A method comprising: Embodiment 2: Calculating or retrieving a plurality of parameters utilized to calculate an ideal crop potential yield, the plurality of parameters representing weather conditions, soil characteristics, and crop-specific growing degree days required for the crop to mature, the plurality of parameters being derived at least in part from the crop type, the crop planting date, and the geographic location in combination with historical weather and crop observations. 2. The method of embodiment 1, further comprising: Embodiment 3: 3. The method of claim 1 or 2, wherein the ideal crop potential is calculated as a profile of crop potential as a function of date, for a geographic location, the profile representing the maximum potential yield calculated without including developmental, environmental and event stresses. Embodiment 4: The method of any one of embodiments 1 to 3, further comprising calculating a phenology model by determining, calculating, or simulating a plant development stage of the crop based on the planting date, the crop variety, the estimated emergence date, and the degree-day integration model. Embodiment 5: 5. The method of any one of the preceding claims, further comprising calculating a developmental stress based at least in part on predicted radiation loss conditions, extreme temperature conditions, and water loss conditions during the growing season. Embodiment 6: 6. The method of any one of the preceding claims, further comprising calculating an event stress based at least in part on the fruit set loss model and the ripening loss model. Embodiment 7: 7. The method of any one of the preceding claims, further comprising calculating an event stress based at least in part on a soil moisture model and a canopy loss model. Embodiment 8: 8. The method according to any one of the preceding embodiments, further comprising at least partially using the actual crop yield as a direct or indirect control parameter for controlling agricultural machinery that can be used to treat the crop. Embodiment 9: 9. The method of any one of the preceding embodiments, further comprising causing the device to operate a tool for harvesting or processing the crop based on the calculated actual crop yield. Embodiment 10: A system for determining, calculating, or simulating a crop yield for a growing season, the system comprising: a memory device; and a processing device operably coupled to the memory device, the processing device configured to execute a method according to any one of embodiments 1 to 9. Embodiment 11: A non-transitory computer-readable medium encoding instructions that, when executed by a processing device, cause the processing device to perform a method according to any one of embodiments 1 to 9. Embodiment 12: A tool for harvesting or processing crops, comprising an on-board processing device configured to carry out the method according to any one of embodiments 1 to 9.

Claims

1. 1. A method for determining, calculating, or simulating crop yield for a growing season, the method comprising: receiving input from a user device, database, or sensor, the input including a type of crop, a planting date of the crop, and a geographic location of the crop being grown; calculating an ideal crop yield potential based at least in part on the type of crop, the planting date, the geographic location, and a phenology model; calculating a stress model based at least in part on developmental and event stresses predicted or observed to occur during the growing season; calculating an actual crop yield by applying the stress model as a reduction penalty to the ideal crop yield potential; The actual crop yield, said user's device for display; a separate computing device for further modeling or for generating recommended chemicals to apply to said crops; or a device adapted to operate a tool for harvesting or processing said crop; and sending to one or more of A method comprising:

2. 10. The method of claim 1, further comprising calculating or retrieving a plurality of parameters utilized to calculate the ideal crop yield potential, the plurality of parameters representing weather conditions, soil properties, and crop-specific growing degree days required for crop maturity, the plurality of parameters being derived at least in part from the type of crop, the planting date of the crop, and geographic location in combination with historical weather and crop observations.

3. 2. The method of claim 1, wherein the ideal crop potential is calculated as a profile of crop yield potential as a function of date for the geographic location, the profile representing the maximum potential yield calculated without including developmental, environmental, and event stresses.

4. 10. The method of claim 1, further comprising calculating the phenology model by determining, calculating, or simulating a plant development stage of the crop based on the planting date, crop variety, estimated emergence date, and degree-day accumulation model.

5. 10. The method of claim 1, further comprising calculating developmental stress based at least in part on predicted radiation loss conditions, extreme temperature conditions, and water loss conditions for the growing season.

6. The method of claim 1 , further comprising calculating an event stress based at least in part on a fruit set loss model and a ripening loss model.

7. The method of claim 1 , further comprising calculating an event stress based at least in part on a soil moisture model and a canopy loss model.

8. 10. The method of claim 1, further comprising using the actual crop yield, at least in part, as a direct or indirect control parameter for controlling agricultural machinery usable to treat the crop.

9. The method of claim 1 , further comprising causing a device to operate the tool to harvest or process the crop based on the calculated actual crop yield.

10. 1. A system for determining, calculating, or simulating crop yield for a growing season, the system comprising: a memory device; a processing device operably coupled to the memory device; The processing device comprises: receiving input from a user device, database, or sensor, the input including a type of crop, a planting date of the crop, and a geographic location of the crop being grown; calculating an ideal crop yield potential based at least in part on the type of crop, the planting date, the geographic location, and a phenology model; calculating a stress model based at least in part on developmental and event stresses predicted or observed to occur during the growing season; calculating an actual crop yield by applying the stress model as a reduction penalty to the ideal crop yield potential; The actual crop yield, said user's device for display; a separate computing device for further modeling or for generating recommended chemicals to apply to said crops; or a device adapted to operate a tool for harvesting or processing said crop. The system is configured as follows:

11. 11. The system of claim 10, wherein the processing device is further configured to calculate or retrieve a plurality of parameters utilized to calculate the ideal crop yield potential, the plurality of parameters representing weather conditions, soil properties, and crop-specific growing degree days required for crop maturity, the plurality of parameters being derived at least in part from the type of crop, the planting date of the crop, and geographic location in combination with historical weather and crop observations.

12. 11. The system of claim 10, wherein the ideal crop potential is calculated as a profile of crop yield potential as a function of date for the geographic location, the profile representing a maximum potential yield calculated without including developmental, environmental, and event stresses.

13. 11. The system of claim 10, wherein the processing device is further configured to calculate the phenology model by determining, calculating, or simulating a plant development stage of the crop based on the planting date, crop variety, estimated emergence date, and degree-day accumulation model.

14. 11. The system of claim 10, wherein the processing device is further configured to calculate developmental stress based at least in part on predicted radiation loss conditions, extreme temperature conditions, and water loss conditions for the growing season.

15. The system of claim 10 , wherein the processing device is further configured to calculate an event stress based at least in part on a fruit set loss model and a ripening loss model.

16. The system of claim 10 , wherein the processing device is further configured to calculate an event stress based at least in part on a soil moisture model and a canopy loss model.

17. 11. The system of claim 10, wherein the processing device is further configured to use the actual crop yield, at least in part, as a direct or indirect control parameter for controlling agricultural machinery usable to treat the crop.

18. The system of claim 10 , wherein the system is integrated into an agricultural machine.

19. A non-transitory computer readable medium encoding instructions that, when executed by a processing device, cause the processing device to perform the method of any one of claims 1 to 9.

20. A tool for harvesting or processing crops, comprising an on-board processing device configured to carry out the method of any one of claims 1 to 9.

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