Program, method and system

Abstract

To provide a system capable of proposing appropriate farm field management work which is to be performed after harvesting, to an agricultural worker, in order to suppress the emission of greenhouse gases.SOLUTION: A program is executed by a system 1 having a processor 31 and configured to make a proposal about management of a farm field for an agricultural worker, the program causing the processor 31 to execute the steps of: acquiring farm field information including the quantity of organic substances left in the farm filed after harvesting; and estimating the amount of methane gas to be produced from the farm field in a future specific period, based on the input farm field information.SELECTED DRAWING: Figure 8

Description

Technical Field

[0001] The present disclosure relates to programs, methods, and systems.

Background Art

[0002] Recently, systems have been provided to support the reduction of greenhouse gas emissions, which are considered to contribute to global warming, in the agricultural field. For example, the following patent documents disclose support systems for reducing greenhouse gas emissions from paddy fields by farmers. In this system, using methane suppression cultivation parameters for estimating the amount of methane gas emissions, the amount by which the emissions of methane gas, which is a greenhouse gas, are suppressed is calculated.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the conventional system, it has not been possible to make specific proposals to farmers regarding what operations should be performed after harvesting.

[0005] An object of the present disclosure is to provide a system that can propose appropriate field management operations to be performed after harvesting in order to suppress the generation of greenhouse gases for farmers.

Means for Solving the Problems

[0006] One aspect of the present disclosure is a program executed by a system having a processor and making a proposal regarding the management of a farm field performed by a farmer, the processor being caused to perform steps of: obtaining farm field information including the amount of organic matter remaining in the farm field after harvesting; and estimating the amount of methane gas generated from the farm field in a future specific period based on the input farm field information.

Advantages of the Invention

[0007] According to the present disclosure, it is possible to propose appropriate farm field management work to be performed after harvesting to a farmer in order to suppress the generation of greenhouse gases.

Brief Description of the Drawings

[0008]

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Mode for Carrying Out the Invention

[0009] Hereinafter, with reference to the drawings, an embodiment of the present invention will be described. In the following description, the same parts are denoted by the same reference numerals. Their names and functions are also the same. Therefore, detailed descriptions thereof will not be repeated.

[0010] <1. Configuration of System 1> Hereinafter, the configuration of an agricultural work support system 1 (hereinafter simply referred to as system 1) according to an embodiment of the present invention will be described.

[0011] (1-1) Overall Configuration First, the overall configuration of system 1 will be described with reference to FIG. 1. As shown in FIG. 1, system 1 is a system that proposes appropriate field management work (hereinafter simply referred to as management work) to be performed after harvesting to agricultural workers in order to suppress the generation of greenhouse gases (for example, methane gas) from paddy fields (fields) in rice cultivation. By performing the management work proposed by system 1, agricultural workers can effectively suppress the generation of methane gas from paddy fields in the following year.

[0012] Here, the management work refers to various operations carried out by farmers to manage the field, especially after harvesting, among the farming operations performed by farmers. The management work includes, for example, the following operations. · The operation of raking the straw left in the field at the time of harvesting into the field (raking operation) · Irrigation management work carried out after harvesting · Soil management work such as spreading fertilizers carried out after harvesting · Control work against pests and weeds carried out after harvesting Note that the management work includes various operations carried out to manage the field after harvesting, not limited to the above examples.

[0013] Figure 1 is a diagram showing the overall configuration of system 1. As shown in Figure 1, system 1 includes an operator terminal 10, a manager terminal 20, a management server 30, a sensor 40, and an agricultural work vehicle 50.

[0014] The operator terminal 10 is an information processing device operated by a user (farmer) who performs agricultural work using system 1. The operator terminal 10 is realized by a portable tablet terminal, smartphone, or stationary PC (Personal Computer), laptop PC, etc. corresponding to system 1. The operator terminal 10 is connected to the network 80.

[0015] The manager terminal 20 is an information processing device operated by a manager who manages the maintenance · operation of system 1. The manager terminal 20 is realized by a stationary PC (Personal Computer), laptop PC, or tablet terminal, smartphone, etc. corresponding to system 1. The manager terminal 20 is connected to the network 80.

[0016] The management server 30 is a server device that performs various operations described later and provides various types of information related to agricultural work support to the operator terminal 10 via the network 80. The management server 30 includes various computers such as a personal computer and a server computer (for example, a web server, an application server, a database server, or a combination thereof). In the present embodiment, the management server 30 will be described by taking as an example a cloud server that makes one or more virtualized servers available via a network.

[0017] The sensor 40 is a measuring device that senses the state of the field. The sensor 40 can communicate with the management server 30 via the network 80 by wireless communication. The sensor 40 senses the state of the field by a remote operation of the operator terminal 10 by an agricultural worker or according to a preset measurement frequency. The information sensed by the sensor 40 includes, for example, the following. That is, a plurality of sensors 40 may be installed in the field. · Soil components of the field · Temperature and humidity of the field · Temperature of the soil in the field · Characteristics of the field

[0018] Also, the sensor 40 may be a camera that photographs the state of the field. In this case, the sensor 40 periodically photographs the state of the field and transmits it to the management server 30. For example, when the field is a paddy field, a plant marker may be grown as a mark of the state change of the paddy field. The appearance of the plant marker changes according to the increase or decrease of specific molecules contained in the soil. The sensor 40 may periodically photograph the state of the plant marker.

[0019] The agricultural work vehicle 50 is a vehicle that performs agricultural work by the operation of an agricultural worker. The agricultural work vehicle 50 includes a combine used for harvesting. The agricultural work vehicle 50 records the work history of the work executed as the harvesting work and transmits it to the management server 30 by wireless communication. The agricultural work vehicle 50 has a GPS (Global Positioning System) module and manages by associating the position information with the work history. That is, the work history received by the management server 30 includes the work content and the position information where the work was performed.

[0020] (1-2) Hardware configuration of the worker terminal 10 Next, with reference to FIG. 2A, the hardware configuration of the worker terminal 10 will be described. FIG. 2A is a diagram showing the hardware configuration of the worker terminal 10. As shown in FIG. 2A, the worker terminal 10 includes a processor 11, a memory 12, a storage 13, a communication IF (Interface) 14, an input device 15, and an output device 16. Note that since the configuration of the administrator terminal 20 is generally the same as that of the worker terminal 10, the description thereof will be omitted.

[0021] The worker terminal 10 is communicably connected to the management server 30 via the network 80. The worker terminal 10 is connected to the network 80 by communicating with communication devices such as a wireless base station 81 corresponding to communication standards such as 5G and LTE (Long Term Evolution), and a wireless LAN router 82 corresponding to wireless LAN (Local Area Network) standards such as IEEE (Institute of Electrical and Electronics Engineers) 802.11.

[0022] The processor 11 is hardware for executing an instruction set described in a program stored in the storage 13, and is composed of an arithmetic unit, a register, a peripheral circuit, and the like.

[0023] The memory 12 is for temporarily storing programs and data to be processed by the programs, etc., and is a volatile memory such as a DRAM (Dynamic Random Access Memory).

[0024] The storage 13 is a storage device for storing programs and data, such as a flash memory or an HDD (Hard Disc Drive). The programs stored in the storage 13 include, for example, the following programs. ·Program of the OS (Operating System) ·Program of an application that executes information processing (e.g., a web browser)

[0025] The data stored in the storage 13 includes, for example, the following data. ·Database referred to in information processing ·Data obtained by executing information processing (execution result of information processing)

[0026] The communication IF 14 is an interface for inputting and outputting signals so that the operator terminal 10 can communicate with an external device.

[0027] The input device 15 is an input device for receiving input operations from the user (e.g., a pointing device such as a touch panel, touch pad, mouse, etc., a keyboard, etc.).

[0028] The output device 16 is an output device for presenting information to the user (display, speaker, etc.).

[0029] (1 - 3) Functional configuration of the operator terminal 10 Next, with reference to FIG. 2B, the functional configuration of the operator terminal 10 will be described. FIG. 2B is a diagram showing the functional configuration of the operator terminal 10. As shown in FIG. 2B, the operator terminal 10 includes an antenna 111, a wireless communication unit 101, a storage unit 102, a control unit 103, an operation reception unit 104, a position information sensor 105, and a camera 106.

[0030] The operator terminal 10 also has functions and configurations not shown in FIG. 2B (for example, a battery for holding power, a power supply circuit for controlling the supply of power from the battery to each circuit, etc.). As shown in FIG. 2B, each block included in the operator terminal 10 is electrically connected by a bus or the like.

[0031] The antenna 111 radiates the signal emitted by the operator terminal 10 as a radio wave. Also, the antenna 111 receives a radio wave from space and supplies the received signal to the wireless communication unit 101.

[0032] The wireless communication unit 101 performs modulation / demodulation processing and the like for transmitting and receiving signals via the antenna 111 so that the operator terminal 10 can communicate with other wireless devices.

[0033] The wireless communication unit 101 is a communication module including a tuner, an RSSI (Received Signal Strength Indicator) calculation circuit, a CRC (Cyclic Redundancy Check) calculation circuit, a high-frequency circuit, and the like. The wireless communication unit 101 performs modulation / demodulation and frequency conversion of the wireless signals transmitted and received by the operator terminal 10, and supplies the received signal to the control unit 103.

[0034] The operation reception unit 104 has a mechanism for receiving the input operation of the user. Specifically, the operation reception unit 104 is configured as a touch screen and includes a touch sensing device 1041 and a display 1042.

[0035] The touch sensing device 1041 receives the input operation of the user of the operator terminal 10. The touch sensing device 1041 detects the contact position of the user on the touch panel, for example, by using a capacitance-type touch panel. The touch sensing device 1041 outputs a signal indicating the contact position of the user detected by the touch panel to the control unit 103 as an input operation.

[0036] The display 1042 displays data such as images, videos, and texts according to the control of the control unit 103. The display 1042 is realized by, for example, an LCD (Liquid Crystal Display) or an organic EL (Electro-Luminescence) display.

[0037] The position information sensor 105 is a sensor that detects the position of the operator terminal 10 and is, for example, a GPS (Global Positioning System) module. The GPS module is a receiving device used in a satellite positioning system. In the satellite positioning system, signals from at least three or four satellites are received, and based on the received signals, the current position of the operator terminal 10 equipped with the GPS module is detected.

[0038] The camera 106 is a device that receives light by a light receiving element and outputs it as a photographed image.

[0039] The storage unit 102 is composed of, for example, a flash memory or the like, and stores data and programs used by the operator terminal 10. The storage unit 102 stores at least user information 1021, a field image 1022, and various programs (not shown).

[0040] The user information 1021 is information about the user who uses the operator terminal 10. The user information 1021 includes account information (user ID and password for identifying the user) that is required to be input when the user logs in to the system 1 by the operator terminal 10. In addition, the user information 1021 may include various information about the attributes of the user registered when the user installs a predetermined application on the operator terminal 10 in order to use the system 1.

[0041] The user ID may be a customer ID issued to the user. The user ID may be a user account of a service that provides various functions such as mail, video call, calendar, storage, document creation, spreadsheet creation, and news distribution in the form of SaaS (Software as a Service) etc. (which may include services for corporations).

[0042] The field image 1022 is image data of a field taken using the operator terminal 10. As the image data of the field, in addition to those taken by the operator himself / herself, image data periodically taken by the operator terminal 10 fixed at a specific position is also included.

[0043] The control unit 103 reads the program stored in the storage unit 102 and executes the instructions included in the program to control the operation of the operator terminal 10. The control unit 103 is, for example, an application pre-installed in the operator terminal 10. By operating according to the program, the control unit 103 functions as an input reception unit 1031, a transmission / reception unit 1032, a photographing unit 1033, a storage processing unit 1034, and a display control unit 1035.

[0044] The input reception unit 1031 performs a process of receiving a user's input operation on the input device 15 such as the touch-sensing device 1041.

[0045] The transmission / reception unit 1032 performs a process for the operator terminal 10 to transmit and receive data to and from an external device such as the management server 30 according to a communication protocol.

[0046] The photographing unit 1033 performs a process of activating the camera 106 and photographing the field as the subject in response to an operation from the agricultural operator.

[0047] The memory processing unit 1034 performs a process of storing data received by the operator terminal 10 and data obtained by performing calculations according to a program in the storage unit 102. For example, the memory processing unit 1034 controls the storage process of the captured image in the storage unit 102.

[0048] The display control unit 1035 performs a process of presenting information to the user by displaying the information on the display 1042. The display control unit 1035 has a function as a web browser, accesses information output by the management server 30 to the logical line (TCP connection) with the operator terminal 10, and performs a process (rendering) of displaying it on the display 1042 of the operator terminal 10.

[0049] (1-4) Hardware configuration of the management server 30 Next, with reference to FIG. 1, the hardware configuration of the management server 30 will be described. As shown in FIG. 1, the management server 30 includes a processor 31, a memory 32, a storage 33, a communication IF 34, and an input / output IF 35.

[0050] The processor 31 is hardware for executing an instruction set described in a program stored in the storage 33, and is composed of an arithmetic unit, registers, peripheral circuits, and the like.

[0051] The memory 32 is for temporarily storing a program and data processed by the program or the like, and is a volatile memory such as a DRAM (Dynamic Random Access Memory).

[0052] The storage 33 is a storage device for storing programs and data, and is, for example, a flash memory or an HDD (Hard Disc Drive). The programs stored in the storage 33 include, for example, the following programs. · Program of the OS (Operating System) · Program of an application that executes information processing (for example, a web browser)

[0053] The data stored in the storage 33 includes, for example, the following data. · Database referenced in information processing · Data obtained by executing information processing (i.e., the execution result of information processing)

[0054] The communication IF 34 is an interface for inputting and outputting signals for the management server 30 to communicate with an external device.

[0055] The input / output IF 35 functions as an interface with an input device for receiving input operations via the worker terminal 10 and the administrator terminal 20, and an output device for presenting information to the worker terminal 10 and the administrator terminal 20.

[0056] (1-5) Functional configuration of the management server 30 Next, with reference to FIG. 3, the functional configuration of the management server 30 will be described. FIG. 3 is a diagram showing the functional configuration of the management server 30. As shown in FIG. 3, the management server 30 exhibits functions as a communication unit 301, a storage unit 302, and a control unit 303.

[0057] The communication unit 301 performs processing for the management server 30 to communicate with an external device.

[0058] The storage unit 302 stores data and programs used by the management server 30. The storage unit 302 stores, for example, the following data. · User database (User DB) 3021 · Field database (Field DB) 3022 · Work history database (Work history DB) 3023 · Sensor database (Sensor DB) 3024 · Soil component database (Soil component DB) 3025 · Weather history database (Weather history DB) 3026 · Residual organic matter database (Residual organic matter DB) 3027 · Yield history database (Yield history DB) 3028 · Occurrence prediction database (Occurrence prediction DB) 3029 · Management operation identification model 3041 · Occurrence quantity estimation model 3042

[0059] The user DB 3021 is a database that manages information about farmers who use the system 1 to receive support for agricultural operations. Details of the data structure of the user DB 3021 will be described later.

[0060] The field DB 3022 is a database that manages information about fields operated by farmers. Details of the data structure of the field DB 3022 will be described later.

[0061] The work history DB 3023 is a database that manages the history of various agricultural operations carried out by farmers as field management. Details of the data structure of the work history DB 3023 will be described later.

[0062] The sensor DB 3024 is a database that manages information about the sensors 40 installed in the field. Details of the data structure of the sensor DB 3024 will be described later.

[0063] The soil component DB 3025 is a database that manages information about soil components detected from the field. Details of the data structure of the soil component DB 3025 will be described later.

[0064] The weather history DB 3026 is a database that manages the history of the weather around the field. Details of the data structure of the weather history DB 3026 will be described later.

[0065] The residual organic matter DB 3027 is a database that manages information about the state and quantity of organic matter (straw) remaining in the field after harvesting. Details of the data structure of the residual organic matter DB 3027 will be described later.

[0066] The yield history DB 3028 is a database that manages information about the actual results of yields. Details of the data structure of the yield history DB 3028 will be described later.

[0067] The generation prediction DB3029 is a database that manages information related to the prediction of methane gas generation from the field. Details of the data structure of the generation prediction DB3029 will be described later.

[0068] The management work specific model 3041 is obtained by causing a machine learning model to perform machine learning according to a model learning program based on learning data. For example, in the present embodiment, the management work specific model 3041 is learned to output the content of appropriate management work (hereinafter referred to as work proposal information) for suppressing the generation of methane gas from the field in the following year with respect to the input field information. The management work specific model 3041 is constructed by the administrator and stored in advance in the storage unit 302 of the management server 30. The management work specific model 3041 performs re-learning by the administrator as necessary.

[0069] The learning data of the management work specific model 3041 uses, for example, field information as input data and work proposal information for suppressing the generation of methane gas from the field in the following year as correct answer output data. The work proposal information includes the following information. · Type of management work · Method of management work (including specific aspects such as procedures and equipment to be used) · Recommended time period for implementing the management work

[0070] The field information input to the management work specific model 3041 may include the predicted generation amount of methane gas whose generation is predicted from the field. The predicted generation amount of methane gas is estimated by the generation amount estimation model 3042.

[0071] The management work identification model 3041 according to this embodiment is, for example, a parameterized composite function in which a plurality of functions are combined. The parameterized composite function is defined by a combination of a plurality of adjustable functions and parameters. The management work identification model 3041 according to this embodiment may be any parameterized composite function that satisfies the above requirements, but is, for example, a multi-layer neural network model (hereinafter referred to as a "multi-layer network"). The management work identification model 3041 using a multi-layer network has an input layer, an output layer, and at least one intermediate layer or hidden layer provided between the input layer and the output layer. The management work identification model 3041 is assumed to be used as a program module that is part of artificial intelligence software.

[0072] As the multi-layer network according to this embodiment, for example, a deep neural network (DNN), which is a multi-layer neural network targeted for deep learning, can be used. As the DNN, for example, a convolutional neural network (CNN) targeted for images may be used.

[0073] The generation amount estimation model 3042 is a mathematical model described by the relationship between the amount of organic matter remaining in the field, the work history of agricultural work performed on the field, and the estimated value of the amount of methane gas generated from the field in the following year. FIG. 4A is a diagram showing an example of a work content table related to the generation amount estimation model 3042. FIG. 4B is a diagram showing an example of a generation amount table related to the generation amount estimation model 3042.

[0074] The work content table shown in FIG. 4A and the generation amount table shown in FIG. 4B describe the relationship between the amount of rice straw in the plowing work, the work performance, and the estimated amount of methane gas generated in the following year. In the illustrated example, the case where the type of soil is black peat soil is shown.

[0075] Specifically, the work indicated by the symbol P in FIG. 4A shows the following content. · The length of the straw remaining at the time of harvest is 10 cm · The rotational speed of the tiller PTO at the time of plowing in the remaining straw is 540 rpm · The working speed of the tiller at the time of plowing in the remaining straw is 3 km / h

[0076] And, the predicted methane generation amount indicated by the symbol P in Fig. 4B is 6 g per square meter per year. That is, when the amount of straw remaining in the field in the above-described harvesting operation is small, the predicted methane gas generation amount from that field in the following year is estimated to be 6 g per square meter per year. The classification of the amount of straw in this case is preset. The predicted methane generation amount in the generation amount table is prepared in advance for each type of soil.

[0077] That is, since the generation amount estimation model 3042 is constructed based on the relationship between the work content table and the generation amount table, it is possible to output the predicted methane generation amount in the following year in that field for the farming operations performed by the farmer. Farming operations include field management operations performed during the fallow period.

[0078] The control unit 303 exhibits functions as various modules by the processor 31 of the management server 30 performing processing according to a program. The control unit 303 exhibits functions as a transmission / reception control module 3031, an acquisition module 3032, a specification module 3033, a prediction module 3034, and an output module 3036.

[0079] The transmission / reception control module 3031 controls the process in which the management server 30 transmits and receives signals according to a communication protocol to and from an external device.

[0080] The acquisition module 3032 acquires information transmitted from the farmer terminal 10A in response to an operation by the farmer. Further, the acquisition module 3032 acquires sensing results transmitted from various sensors 40. The acquisition module 3032 stores the acquired various information in a predetermined storage area (each database) in the storage unit 302.

[0081] Specific module 3033 identifies appropriate work proposal information for suppressing methane gas generation from the field in the following year based on the acquired field information, and generates the work proposal information. Specifically, by inputting the field information into the management work identification model 3041, the specific module 3033 acquires the appropriate field management work output from the management work identification model 3041.

[0082] The prediction module 3034 also performs a prediction of methane gas generation. The prediction module 3034 uses the field information to predict the generation of methane gas in the following year. The prediction module 3034 calculates the estimated value of the amount of methane gas generated for both the case where no field management work is performed during the fallow period after harvest and the case where field management work is performed during the fallow period. "The following year" as used in this disclosure is the year (or fiscal year) following the harvest time, and is an example of a "specific future period". That is, the prediction module 3034 estimates the amount of methane gas generated in a specific future period using the field information.

[0083] For the case where no field management work is performed during the fallow period after harvest, the prediction module 3034 calculates the estimated value of the amount of methane gas generated using the methane gas emission factor. The methane gas emission factor is included in the greenhouse gas inventory published by a public research institution and is set for each soil type. The methane gas emission factor is set with a target value for methane gas emissions per unit area per year. The prediction module 3034 calculates the estimated value of the amount of methane gas generated from the field in the following year using the soil type, field area, and methane gas emission factor of the field to be evaluated.

[0084] For the case where the amount of residual organic matter is considered, the prediction module 3034 calculates the estimated value of the amount of methane gas generated from the field in the following year by inputting the field information into the generation amount estimation model 3042.

[0085] The aggregation module 3035 calculates the expected reduction amount of methane gas that is expected to be reduced by implementing the management work. Based on the calculated expected reduction amount of methane gas, the aggregation module 3035 performs at least one of applying for carbon credits and calculating the carbon footprint. The aggregation module 3035 uses the expected reduction amount to create application information that conforms to a predetermined application form for undergoing review by a carbon credit certification authority.

[0086] The output module 3036 outputs various types of information obtained by the executed processing in response to the operations of the farmer. The output module 3036 outputs, for example, the following information. · Work proposal information · Information stored in each database

[0087] In addition, the output module 3036 transmits the carbon credit application information created by the aggregation module 3035 to the carbon credit certification authority.

[0088] (1-6) Data structure Next, an example of the data structure of each database stored in the storage unit 302 of the management server 30 will be described with reference to FIGS. 5 to 7.

[0089] (1-6-1) User DB3021 FIG. 5A is a diagram showing an example of the data structure of the user DB3021. As shown in FIG. 5A, the user DB3021 stores information about users who use the system 1 as farmers. The user DB3021 records new records through a user registration operation when a farmer starts using the system 1.

[0090] The user DB3021 includes an item "user ID", an item "name", an item "personal information", and an item "affiliated organization".

[0091] The item "user ID" stores user identification information that can identify a user who uses the system 1 as a farmer.

[0092] In the item "Name", information regarding the name of the user corresponding to the user ID is stored.

[0093] In the item "Personal Information", various types of personal information regarding the user corresponding to the user ID are stored. The personal information includes, for example, the following. · The user's age, gender, date of birth, login password, etc · The user's address, contact information, etc

[0094] In the item "Affiliated Organization", the name of the organization such as an agricultural cooperative to which the user corresponding to the user ID belongs is stored. Note that if the user does not belong to any organization, the item "Affiliated Organization" may be blank.

[0095] Note that the structure of the user DB3021 shown in FIG. 5A is merely an example, and the user DB3021 may include columns for storing other data items.

[0096] (1-6-2) Field DB3022 FIG. 5B is a diagram showing an example of the data structure of the field DB3022. As shown in FIG. 5B, the user DB3021 stores information regarding the fields managed by the user who is a farmer. The field DB3022 records new records by an input operation from the farmer.

[0097] The field DB3022 includes an item "Field ID", an item "Location Information", an item "Field Area", an item "Administrator ID", an item "Crop Planting Status", an item "Topography Information", and an item "Field Image".

[0098] In the item "Field ID", identification information of the field that can identify the field is stored.

[0099] In the item "Location Information", the location information of the field corresponding to the field ID is stored.

[0100] In the item "Field Area", the area of the field corresponding to the field ID is stored.

[0101] In the item "Manager ID", the user ID of the user corresponding to the manager of the field corresponding to the field ID is stored.

[0102] In the item "Crop Planting Status", the crop planting status of the field corresponding to the field ID is stored.

[0103] In the item "Topographic Information", the topographic information of the field corresponding to the field ID is stored. The topographic information includes the soil type of the field.

[0104] In the item "Field Image", the image information of the field corresponding to the field ID is stored.

[0105] Note that the structure of the field DB3022 shown in Fig. 5B is merely an example, and the field DB3022 may include columns for storing other data items.

[0106] (1-6-3) Work History DB3023 Fig. 5C is a diagram showing an example of the data structure of the work history DB3023. As shown in Fig. 5C, the work history DB3023 stores the history of agricultural operations performed by farmers for field management. New records are recorded in the work history DB3023 through the input by farmers.

[0107] The work history DB3023 includes an item "Work ID", an item "Field ID", an item "Work Name", an item "Work Content", and an item "Work Time".

[0108] In the item "Work ID", the identification information of the work that can identify the agricultural work performed by the farmer is stored.

[0109] In the item "Field ID", the identification information of the field where the agricultural work corresponding to the work ID is performed is stored.

[0110] In the item "Work Name", the name of the agricultural work corresponding to the work ID is stored.

[0111] In the item "Work content", the content of the agricultural work corresponding to the work ID is stored. The content of the agricultural work includes the following information. · Specific procedures of the agricultural work · Number of times of the agricultural work · Range where the agricultural work was carried out · Names of chemicals such as fertilizers used in the agricultural work

[0112] In the item "Work period", information regarding the time when the agricultural work corresponding to the work ID was carried out is stored.

[0113] Note that the structure of the work history DB3023 shown in Fig. 5C is merely an example, and the work history DB3023 may include columns for storing other data items.

[0114] (1-6-4) Sensor DB3024 Fig. 6A is a diagram showing an example of the data structure of the sensor DB3024. As shown in Fig. 6A, the sensor DB3024 stores information regarding the sensor 40 installed in the farm field. When the sensor 40 is installed in the farm field, a new record is recorded based on the input by the agricultural worker in the sensor DB3024.

[0115] The sensor DB3024 includes the item "Sensor ID" and the item "Farm field ID".

[0116] In the item "Sensor ID", the identification information of the sensor 40 that can identify the sensor 40 installed in the farm field is stored.

[0117] In the item "Farm field ID", the identification information of the farm field where the sensor 40 corresponding to the sensor ID is installed is stored.

[0118] Note that the structure of the sensor DB3024 shown in Fig. 6A is merely an example, and the sensor DB3024 may include columns for storing other data items.

[0119] (1-6-5) Soil component DB3025 Figure 6B is a diagram showing an example of the data structure of the soil component DB3025. As shown in Figure 6B, the soil component DB3025 stores information on the soil components detected by the sensor 40 from the field. The soil components include at least the oxidation-reduction potential of paddy soil and the content of specific molecules related to the oxidation-reduction state. When the sensing result by the sensor 40 is transmitted to the management server 30, a new record is recorded in the soil component DB3025.

[0120] The soil component DB3025 includes an item "measurement ID", an item "sensor ID", an item "measurement date and time", and an item "soil component data".

[0121] In the item "measurement ID", identification information of the sensing result that can identify the sensing result of measuring the soil component is stored.

[0122] In the item "sensor ID", identification information of the sensor 40 that obtained the sensing result corresponding to the measurement ID is stored.

[0123] In the item "measurement date and time", the date and time when the sensing result corresponding to the measurement ID was obtained is stored.

[0124] In the item "soil component data", the value of the soil component is stored as the sensing result corresponding to the measurement ID. Examples of the soil component data include the following. · Oxidation-reduction potential: A value indicating the oxidation-reduction potential of the soil · pH value: A value indicating the acidity or alkalinity of the soil · Methanogenic bacteria content: The amount of methanogenic bacteria contained in the soil · Nitrogen content: The concentration of nitrogen in the soil · Phosphorus content: The concentration of phosphorus in the soil · Potassium content: The concentration of potassium in the soil · Organic matter content: The concentration of organic matter in the soil · Inorganic matter content: The concentration of inorganic substances such as iron and manganese

[0125] For example, the soil component DB3025 may include soil analysis results. The soil analysis results are values obtained by performing various analyses on the collected soil samples and measuring the contents of nutrients, minerals, organic matter, etc. As analysis results, in addition to the same evaluation items as the above sensing results, soil particle distribution, water retention capacity, salt absorption amount, and electrical conductivity, etc. are included.

[0126] Also for example, in the soil component DB3025, the values of the soil information database publicly disclosed by agricultural research institutions may be referred to, and any data items may be diverted as new columns.

[0127] Note that the structure of the soil component DB3025 shown in FIG. 6B is merely an example, and the soil component DB3025 may include columns in which other data items are stored. Also, as soil components, the contents of components other than the above may be included.

[0128] (1-6-6) Weather history DB3026 FIG. 6C is a diagram showing an example of the data structure of the weather history DB3026. As shown in FIG. 6C, the weather history DB3026 stores the weather history of the field. The weather history DB3026 has new records recorded when the acquisition module 3032 of the management server 30 periodically acquires the weather history from an external weather information system. Note that the weather information may be acquired by the sensor 40.

[0129] The weather history DB3026 includes an item "observation ID", an item "observation location", an item "observation date", and an item "weather information".

[0130] In the item "observation ID", identification information of the observation results that can identify the weather observation results is stored.

[0131] In the item "observation location", information on the location where the observation corresponding to the observation ID was performed is stored.

[0132] In the item "observation date", the date when the observation corresponding to the observation ID was performed is stored.

[0133] The content of the observation corresponding to the observation ID is stored in the item "weather information". The content of the observation includes, for example, the following. · Type of weather · Precipitation · Temperature, humidity · Wind speed, wind direction · Information on other external environments of the field

[0134] Note that the structure of the weather history DB3026 shown in Fig. 6C is merely an example, and the weather history DB3026 may include columns for storing other data items.

[0135] (1-6-7) Residual organic matter DB3027 Fig. 7A is a diagram showing an example of the data structure of the residual organic matter DB3027. As shown in Fig. 7A, the residual organic matter DB3027 stores information on the state and amount of organic matter (grain straw) remaining in the field after harvesting. When the agricultural work vehicle 50 performs a harvesting operation and transmits information on the grain straw left in the field as work results, a new record is recorded in the residual organic matter DB3027.

[0136] The residual organic matter DB3027 includes an item "log ID", an item "field ID", an item "work ID", an item "residual position", an item "organic matter type", an item "amount of organic matter", and an item "state of organic matter".

[0137] The item "log ID" stores identification information for identifying the record of the residual organic matter.

[0138] The item "field ID" stores identification information of the field where the residual organic matter corresponding to the log ID occurred.

[0139] The item "work ID" stores the identification information in the work history DB3023 regarding the harvesting operation in which the residual organic matter corresponding to the log ID occurred.

[0140] In the item "Residual Location", the location information of the position where the residual organic matter corresponding to the log ID is generated is stored. The location information is measured by the GPS antenna of the agricultural work vehicle 50. Specifically, the residual location is calculated based on the specifications of the agricultural work vehicle 50 from the location information of the agricultural work vehicle 50 and the time-series data such as conveyance, etc., on which point the cereal straw fell during the harvesting operation by the agricultural work vehicle 50 (latitude and longitude information).

[0141] In the item "Organic Matter Type", the type of the residual organic matter corresponding to the log ID is stored.

[0142] In the item "Amount of Organic Matter", the amount of the residual organic matter corresponding to the log ID is stored. The amount of organic matter is classified into categories such as "Large", "Medium", and "Small" based on the preset standard per unit area. The amount of organic matter may be managed, for example, by a reference value of the mass or volume of the cereal straw discharged from the agricultural work vehicle 50 at the time of harvesting.

[0143] In the item "State of Organic Matter", the state of the residual organic matter corresponding to the log ID is managed. The state of the organic matter includes the size (length) of the cereal straw. Also, when the cereal straw is taken out of the field, information to that effect is stored. When the cereal straw is taken out of the field, the amount of organic matter remaining in the field becomes only at the stock base, and the amount of organic matter will extremely decrease. In this case, a preset correction coefficient is applied to the amount of organic matter.

[0144] Note that the structure of the residual organic matter DB3027 shown in FIG. 7A is merely an example, and the residual organic matter DB3027 may include columns in which other data items are stored.

[0145] (1-6-8) Yield History DB3028 FIG. 7B is a diagram showing an example of the data structure of the yield history DB3028. As shown in FIG. 7B, the yield history DB3028 stores the actual value of the final yield in the field. When the yield history DB3028 receives an input operation of the yield after harvesting from the agricultural operator via the operator terminal 10, a new record is recorded.

[0146] The yield history DB 3028 includes an item "harvest ID", an item "field ID", an item "harvest date", an item "harvested product", and an item "yield".

[0147] In the item "harvest ID", identification information of the harvest operation that can identify the yield record is stored.

[0148] In the item "field ID", identification information of the field where the harvest operation corresponding to the harvest ID was performed is stored.

[0149] In the item "harvest date", the date when the harvest operation corresponding to the harvest ID was performed is stored.

[0150] In the item "harvested product", the name of the crop harvested by the harvest operation corresponding to the harvest ID is stored.

[0151] In the item "yield", the actual value of the yield (harvest amount) in the harvest operation corresponding to the harvest ID is stored.

[0152] Note that the structure of the yield history DB 3028 shown in FIG. 7B is merely an example, and the yield history DB 3028 may include columns for storing other data items.

[0153] (1-6-9) Generation prediction DB 3029 FIG. 7C is a diagram showing an example of the data structure of the generation prediction DB 3029. As shown in FIG. 7C, the generation prediction DB 3029 stores predicted values of methane gas generation from the field. When the generation prediction of methane gas is performed in response to an instruction from a farmer, a new record is recorded in the generation prediction DB 3029.

[0154] The generation prediction DB 3029 includes an item "generation prediction ID", an item "field ID", an item "prediction condition", an item "prediction date", and an item "generation prediction value".

[0155] In the item "generation prediction ID", identification information of the generation prediction of methane gas that can identify the generation prediction result is stored.

[0156] In the item "field ID", identification information of the field that is the target of the occurrence prediction corresponding to the occurrence prediction ID is stored.

[0157] In the item "prediction conditions", the prediction conditions used for the occurrence prediction corresponding to the occurrence prediction ID are stored.

[0158] In the item "prediction date", the date on which the occurrence prediction corresponding to the occurrence prediction ID was made is stored.

[0159] In the item "occurrence prediction value", the predicted value of methane gas generation obtained by the occurrence prediction corresponding to the occurrence prediction ID is stored. The occurrence prediction value includes the predicted occurrence time and the predicted occurrence amount.

[0160] Note that the structure of the occurrence prediction DB3029 shown in FIG. 7C is merely an example, and the occurrence prediction DB3029 may include columns for storing other data items.

[0161] <2. Overview of the Embodiment> Next, with reference to FIG. 8, an overview of the embodiment of the system 1 will be described. FIG. 8 is a diagram showing an overview of the embodiment of the system 1.

[0162] As shown in FIG. 8, the system 1 proposes field management work during the fallow period after the harvest of crops. At this time, it is known that by plowing the rice straw left in the field after harvest into the field, the generation of methane gas from the field in the next year's rice cultivation can be suppressed.

[0163] However, in the conventional plowing work, it was impossible to determine specifically what kind of work should be done to effectively suppress the generation of methane gas from the field in the next year. For this reason, farmers have carried out the plowing work of rice straw in a work mode considered appropriate by using their own past experience and advice from the surroundings.

[0164] Therefore, without being able to determine to what extent the actual stubble incorporation work has suppressed the generation of methane gas from the fields in the following year, there is a current situation where farmers are habitually performing stubble incorporation work.

[0165] Therefore, in System 1, appropriate field management work is proposed to suppress the generation of methane gas from the fields in the following year by using field information including the amount of organic matter remaining in the fields after harvesting. Specifically, System 1 creates a field MAP using information on the organic matter in the fields included in the work results of the harvesting work.

[0166] FIG. 9 is a diagram illustrating a field MAP. As shown in FIG. 9, in the plan view of the field area, the amount of organic matter remaining in the field is expressed as a heat map in the field MAP. In the illustrated example, the amount of organic matter is expressed by shading. A large amount of organic matter remains in the darker-colored areas, and the amount of organic matter remaining in the lighter-colored areas is less.

[0167] System 1 refers to the field MAP, acquires the amount of organic matter for each field together with the location information, and then proposes the specific working mode of appropriate stubble incorporation work. The working mode includes the following information. · The timing and frequency of the stubble incorporation work · The cutting length of the cereal straw · The incorporation depth · The speed of the tiller performing the stubble incorporation work Thereby, it is expected to optimize the field management work including the stubble incorporation work of the cereal straw.

[0168] In addition, the field management work proposed by the system 1 is not limited to the weeding work. For example, the system 1 may propose a work of spraying fungal materials or fertilizers (such as calcium cyanamide) on the fallow field. By spraying the fungal materials on the field according to the amount and state of the organic matter, the decomposition of the organic matter in the field can be promoted. In addition, by spraying calcium cyanamide on the field according to the amount and state of the organic matter, the microorganisms contained in the field can be promoted to decompose the organic matter. Such processing of the system 1 will be described in detail below.

[0169] <3. Operation of System 1> Next, the processing by the system 1 will be described.

[0170] (3-1) Acquisition Process of Work Performance First, the acquisition process of work performance by the system 1 will be described. FIG. 10 is a flowchart showing the acquisition process of work performance by the system 1.

[0171] As shown in FIG. 10, in the acquisition process of work performance, the agricultural work vehicle 50 transmits the performance of the harvesting work (step S101). Specifically, the agricultural work vehicle 50 transmits various information related to the harvesting work to the management server 30 via the network 80 by wireless communication. The various information related to the harvesting work includes the following information. · Information about the field where harvesting was performed · Information about harvesting, such as the harvested crop, harvesting time, and harvest amount · Information about the remaining organic matter, such as the type, amount, and state of the organic matter left in the field

[0172] Thereafter, the management server 30 receives the information related to the work performance transmitted from the agricultural work vehicle 50 (step S201). Specifically, the transmission / reception control module 3031 of the management server 30 acquires the information related to the work performance transmitted from the agricultural work vehicle 50.

[0173] After that, the management server 30 stores the received information on work performance (step S202). Specifically, the transmission / reception control module 3031 of the management server 30 stores the received information in the corresponding database.

[0174] After that, the management server 30 creates a field MAP (step S203). Specifically, the aggregation module 3035 of the management server 30 creates a heatmap that becomes the field MAP using the information on residual organic matter stored in the residual organic matter DB 3027. The heatmap is stored in the storage unit 302 for each field area. As shown in FIG. 8, the harvesting work may be performed multiple times. In this case, the work performance is transmitted for each harvesting work, and the field MAP is updated each time. As described above, the acquisition process of the work performance by the system 1 is completed.

[0175] (3-2) Specific process for identifying appropriate field management work Next, the specific process for identifying appropriate field management work by the system 1 will be described. FIG. 11 is a flowchart showing the specific process for identifying appropriate field management work by the system 1.

[0176] As shown in FIG. 11, in the specific process for identifying appropriate field management work, the worker terminal 10 receives an instruction operation for a management work proposal from the farmer (step S111). Specifically, the farmer operates the worker terminal 10 at an arbitrary timing to input a proposal instruction for the field management work to be performed in order to suppress the generation of methane gas from the field in the following year. For example, this operation is performed when the farmer considers the field management to be performed during the fallow period after harvesting. The proposal instruction for the management work includes the identification information of the target field. The worker terminal 10 transmits the proposal instruction for the management work to the management server 30.

[0177] After that, the management server 30 receives an instruction for a management work proposal (step S211). Specifically, the transmission / reception control module 3031 of the management server 30 receives a proposal instruction for management work transmitted from the worker terminal 10. The transmission / reception control module 3031 of the control unit 303 inputs the received proposal instruction for management work to the acquisition module 3032.

[0178] After that, the management server 30 acquires field information (step S212). Specifically, the acquisition module 3032 of the management server 30 refers to and acquires the field information regarding the field targeted by the instruction from among the field information stored in the field DB 3022. Further, the acquisition module 3032 refers to the residual organic matter DB and acquires a record of the residual organic matter in the field to be evaluated. Further, the acquisition module 3032 may refer to the generation prediction DB and acquire a predicted generation value of methane gas calculated using the generation amount estimation model 3042 for the field to be evaluated. The acquisition module 3032 inputs each acquired piece of information to the identification module 3033.

[0179] After that, the management server 30 identifies an appropriate field management work (step S213). Specifically, the identification module 3033 of the management server 30 inputs the field information including the amount of organic matter input from the acquisition module 3032 to the management work identification model 3041, thereby acquiring work proposal information regarding an appropriate field management work. The work proposal information includes, for example, the following information. · The timing and frequency of the plowing work · The plowing depth · The speed of the tiller for performing the plowing work

[0180] Also, in step S213, the identification module 3033 may input the predicted generation value of methane gas to the management work identification model 3041. At this time, the identification module 3033 may use the estimated value of the generation amount of methane gas calculated for each area constituting the field. In this case, the identification module 3033 identifies an appropriate management work for each area of the field.

[0181] After that, the management server 30 outputs the identified farm management work (step S214). Specifically, the output module 3036 of the management server 30 outputs the work proposal information obtained by the identification module 3033 to the worker terminal 10.

[0182] Also, in step S214, the output module 3036 may compare the estimated methane gas generation amounts in the cases of performing and not performing the farm management work, and output the identified farm management work. In this case, the farmer can immediately grasp the expected value of the effect of the farm management work proposed by the system 1.

[0183] After that, the worker terminal 10 presents the identified farm management work to the farmer (step S112). Specifically, the worker terminal 10 presents the work proposal information transmitted from the management server 30 by displaying it on the display 1042 to the farmer. By checking the work proposal information displayed on the display 1042, the farmer can check the work content and the time to be carried out for the appropriate farm management work to suppress the generation of methane gas. In this way, the system 1 can propose an appropriate farm management work to the farmer to suppress the generation of methane gas. Thus, the specific process of identifying the appropriate farm management work by the system 1 is completed.

[0184] (3-3) Application process for carbon credits Next, the application process for carbon credits by the system 1 will be described. FIG. 12 is a flowchart showing the application process for carbon credits by the system 1. In this process, a process of calculating the expected reduction amount of methane gas expected to be reduced by performing appropriate farm management work and a process of applying for carbon credits based on the calculated expected reduction amount are performed.

[0185] As shown in FIG. 12, in the application process of carbon credits, first, the farmer inputs the work results of the field management work into the worker terminal 10 (step S121). Specifically, when the farmer performs the field management work according to the work proposal information output from the system 1 or when performing a field management work different from the work proposal information, the farmer inputs the work results. The worker terminal 10 transmits the input work results to the management server 30.

[0186] After that, the management server 30 receives the work results of the field management work (step S221). Specifically, the transmission / reception control module 3031 of the management server 30 receives the work results transmitted from the worker terminal 10 and stores them in the work history DB 3023.

[0187] In the system 1, steps S121 and S221 are repeatedly performed throughout the fallow period. That is, when the farmer performs the field management work proposed by the system 1, the farmer inputs the work results.

[0188] After that, for example, at the end of the fallow period, the farmer operates the worker terminal 10 to input an application instruction for carbon credits. Specifically, the farmer applies for carbon credits as the result of the field management work during the fallow period. Note that the application for carbon credits may be made for each field management work. Also, the application for carbon credits may be made by an application agent who has received a request from the farmer operating another terminal device that is communicably connected to the management server 30 via the network 80.

[0189] After that, the management server 30 calculates the expected amount of methane gas reduction (step S222). Specifically, the prediction module 3034 of the management server 30 performs a prediction of methane gas generation. The prediction module 3034 compares the predicted amount of methane gas generation predicted assuming that the field management work is not performed with the predicted amount of methane gas generation predicted after the field management work is performed. Thereby, the prediction module 3034 calculates the expected amount of methane gas reduction due to actually performing the field management work.

[0190] Thereafter, the management server 30 applies for carbon credits (step S223). Specifically, the aggregation module 3035 of the management server 30 creates application information for carbon credits using the estimated reduction of methane gas and information on the work actually performed by the farmer in the field. The application information includes the following information. · Information identifying the farmer (name, address, etc.) · Information identifying the field (address, location, etc.) · Details of the field management work actually carried out · Estimated amount of methane gas reduction due to the implementation of field management work · Other information necessary for the review required by the certification authority

[0191] Thus, the application process for carbon credits by the system 1 is completed. In this way, in the system 1, based on the farming work carried out by the farmer, the estimated amount of methane gas reduction can be calculated and an application for carbon credits can be made. This can save the effort involved in the farmer's application for carbon credits.

[0192] <4. Modification Example> A modification example of this embodiment will be described. In the following description, for the same configurations and the same processes as those in the foregoing embodiment, detailed descriptions thereof will be omitted.

[0193] In the system 1 according to the modification example, the field MAP may be updated using the weather history. That is, the aggregation module 3035 may refer to the weather history of the field recorded in the weather history DB 3026, predict the future weather and temperature trends, and then estimate how the decomposition of the residual organic matter will proceed and reflect it in the field MAP.

[0194] Also, in the system 1, each time the work result of the tilling operation is received, the reduction amount of the straw may be reflected in the field MAP. By checking the field MAP in which the reduction amount of the straw is reflected, an efficient work plan can be formulated.

[0195] In addition, as information regarding the state of the organic matter acquired by the system 1, the following information may be included. · The height of the stubble at the time of the harvesting operation · Information on how the straw was processed during the harvesting operation by the agricultural work vehicle 50 (for example, bundling the straw as it is without cutting it)

[0196] Also, in the system 1, at least one of work information indicating the results of field management work already performed in the field and weather information indicating the weather in the field may be acquired. In this case, the system 1 may specify appropriate field management work based on the field information and at least one of the acquired work information and weather information.

[0197] <5. Other Variants> The following describes other variants.

[0198] In the above-described embodiment, methane gas was cited as an example of the greenhouse gas, but this is not the only case. That is, the system 1 may propose appropriate field management work for the purpose of reducing greenhouse gases other than methane gas.

[0199] In the above-described embodiment, rice was cited as an example of the crop grown in the field, but this is not the only case. That is, the system 1 may propose appropriate field management work for the purpose of reducing greenhouse gases for fields growing crops other than rice.

[0200] Also, in the above-described embodiment, the field information based on the sensing results acquired by the sensor 40 was used, but this is not the only case. The field information may refer to the soil information for each region publicly available by research institutions related to agriculture, acquire the field information regarding the fields managed by the agricultural workers, and specify the above-described work proposal information.

[0201] As described above, the embodiments of the present invention have been described in detail, but the scope of the present invention is not limited to the above embodiments. Further, various improvements and modifications are possible within the scope not departing from the gist of the present invention. Also, the above embodiments and modified examples can be combined or a part thereof can be omitted.

[0202] <6. Supplementary Note> The matters described in the embodiments and modified examples are appended below.

[0203] (Supplementary Note 1) A program executed by a system having a processor and making a proposal regarding the management of a farm field performed by a farmer, the program causing the processor to acquire farm field information including the amount of organic matter remaining in the farm field after harvesting; estimate the amount of methane gas generated from the farm field during a future specific period based on the input farm field information; and execute the above.

[0204] (Supplementary Note 2) The program further causes the processor to specify appropriate farm field management operations for promoting the decomposition of the organic matter after harvesting in order to suppress the generation of methane gas from the farm field during the specific period; and output the specified farm field management operations to an operator terminal.

[0205] (Supplementary Note 3) In the step of specifying appropriate farm field management operations, appropriate farm field management operations are specified using a generation amount estimation model described by the relationship between the amount of the organic matter, the work history of the farming operations performed on the farm field, and the estimated value of the amount of methane gas generated from the farm field during the specific period.

[0206] (Supplementary Note 4) The organic matter includes rice straw remaining in the farm field after harvesting. The field management operation includes the program described in Appendix 2 or 3, which includes at least one of the timing, frequency, and depth in the operation of weeding rice straws.

[0207] (Appendix 5) The field management operation includes the program described in any one of Appendices 2 to 4, which includes the operation of spraying bacteria or fertilizers that decompose the organic matter.

[0208] (Appendix 6) The field information includes a field map in which the amount of rice straw cut by a harvester at the time of harvesting the paddy rice is linked to the position information of the field and the time information at the time of harvesting. In the step of identifying an appropriate field management operation, For each area constituting the field, an estimated methane gas generation amount is calculated to identify an appropriate field management operation, and the program described in any one of Appendices 2 to 5 is used.

[0209] (Appendix 7) In the field map, the amount of rice straw is linked to state information indicating the state of the rice straw cut by a harvester at the time of harvesting the paddy rice, and the program described in Appendix 6 is used.

[0210] (Appendix 8) To the processor, Execute a step of obtaining at least one of work information indicating the results of the field management operations already performed in the field and weather information indicating the weather in the field. In the step of identifying an appropriate field management operation, an appropriate field management operation is identified based on the field information and at least one of the obtained work information and weather information, and the program described in any one of Appendices 2 to 7 is used.

[0211] (Appendix 9) In the step of outputting a specific farm management operation, the estimated methane gas generation amounts in the cases of performing and not performing the farm management operation are compared, and the specific farm management operation is outputted, according to the program described in any one of Supplementary Notes 2 to 8.

[0212] (Supplementary Note 10) To the processor, A step of calculating an expected reduction amount of methane gas expected to be reduced by performing the specific farm management operation; Based on the calculated expected reduction amount, at least one of applying for carbon credits and calculating a carbon footprint is further executed, according to the program described in any one of Supplementary Notes 2 to 9.

[0213] (Supplementary Note 11) A method executed by a system having a processor and making a proposal regarding the management of a farm performed by a farmer, wherein the processor Based on an operation from an operator terminal, a step of acquiring farm information including the amount of organic matter left in the farm after harvesting; Based on the input farm information, a step of specifying an appropriate farm management operation for promoting the decomposition of the organic matter after the harvesting in order to suppress the generation of methane gas from the farm in the following year; A step of outputting the specific farm management operation to the operator terminal and executing.

[0214] (Supplementary Note 12) A system having a processor and making a proposal regarding the management of a farm performed by a farmer, wherein the processor A module that acquires farm information including the amount of organic matter left in the farm after harvesting based on an operation from an operator terminal; A module that specifies an appropriate farm management operation for promoting the decomposition of the organic matter after the harvesting in order to suppress the generation of methane gas from the farm in the following year based on the input farm information; A module that outputs the identified field management work to the operator terminal, and A field management system comprising the same.

[0215] (Appendix 13) One aspect of the present disclosure is a program executed by a system having a processor and making a proposal regarding the management of a field performed by a farmer, the processor being caused to perform steps of: obtaining field information including the amount of organic matter left in the field after harvesting, based on an operation from an operator terminal; identifying appropriate field management work for promoting the decomposition of the organic matter after the harvesting, in order to suppress the amount of methane gas generated from the field in the following year, based on the input field information; and outputting the identified field management work to the operator terminal.

Industrial Applicability

[0216] The present invention is applicable to the field of agricultural work support systems.

Explanation of Signs

[0217] 1 Agricultural work support system 10 Operator terminal 20 Administrator terminal 30 Management server 31 Processor 32 Memory 33 Storage 34 Communication IF 35 Input / output IF 301 Communication unit 302 Storage unit 303 Control unit 40 Sensor 80 Network

Claims

1. A program executed by a system that has a processor and makes proposals regarding the management of a farm field performed by a farmer, wherein the processor performs a step of acquiring farm field information including the amount of organic matter remaining in the farm field after harvesting, performs a step of estimating the amount of methane gas generated from the farm field during a specific future period based on the input farm field information, and a program for causing the above to be executed.

2. The processor performs a step of identifying appropriate farm field management operations for promoting the decomposition of the organic matter after harvesting in order to suppress the generation of methane gas from the farm field during the specific period, and further performs a step of outputting the identified farm field management operations to an operator terminal. The program according to claim 1.

3. In the step of identifying appropriate farm field management operations, appropriate farm field management operations are identified using a generation amount estimation model described by the relationship between the amount of the organic matter, the work history of the farming operations performed on the farm field, and the estimated value of the amount of methane gas generated from the farm field during the specific period. The program according to claim 2.

4. The organic matter includes rice straw remaining in the farm field after harvesting, and the farm field management operations include at least one of the timing, frequency, and depth in the rice straw tilling operation. The program according to claim 2.

5. The farm field management operations include an operation of spraying bacteria or fertilizer for decomposing the organic matter. The program according to claim 2.

6. The farm field information includes a farm field map in which the amount of rice straw cut by a harvester at the time of harvesting rice is linked to the location information of the farm field and the time information at the time of harvesting, In the step of identifying appropriate farm field management operations, appropriate farm field management operations are identified by calculating an estimated value of the amount of methane gas generated for each area constituting the farm field. The program according to claim 2.

7. In the farm field map, the amount of the rice straw is linked to state information indicating the state of the rice straw cut by a harvester at the time of harvesting the rice. The program according to claim 6.

8. The processor is caused to perform a step of acquiring at least one of work information indicating the results of the farm field management operations already performed in the farm field and weather information indicating the weather in the farm field. In the step of identifying the appropriate field management operation, the appropriate field management operation is identified based on at least one of the field information, the obtained operation information, and the weather information, according to the program of claim 2.

9. In the step of outputting the identified field management operation, the estimated methane gas generation amounts in the cases of performing and not performing the field management operation are compared, and the identified field management operation is output, according to the program of claim 2.

10. The processor is caused to calculate the expected reduction amount of methane gas expected to be reduced by performing the identified field management operation; and perform at least one of applying for carbon credits and calculating a carbon footprint based on the calculated expected reduction amount, according to the program of claim 2.

11. A method executed by a system having a processor and making proposals regarding the management of a field performed by a farmer, wherein the processor obtains field information including the amount of organic matter remaining in the field after harvesting; and estimates the amount of methane gas generated from the field in a future specific period based on the input field information. The method is executed.

12. A system having a processor and making proposals regarding the management of a field performed by a farmer, wherein the processor includes a module for obtaining field information including the amount of organic matter remaining in the field after harvesting; and a module for estimating the amount of methane gas generated from the field in a future specific period based on the input field information. The system is provided.

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