Agricultural support methods

Abstract

To accurately estimate information about agricultural crops.SOLUTION: A computer executes the following process: estimating the growth amount of each leaf and each inflorescence of the crop based on actual measurements or predicted values of a cultivation environment after a specified date, calculating the sum of each growth amount and the ratio of each growth amount to the sum based on each estimated growth amount, and estimating a dry matter distribution amount of each leaf and each inflorescence based on the result of the calculation and the actual measurements or predicted values of the cultivation environment.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an agricultural support method. [Background technology]

[0002] Traditionally, catalogs and other materials containing information on agricultural products have generally described the characteristics and properties of each variety using words, images, and average values. Therefore, when farmers select varieties to cultivate, they refer to the characteristics and properties of each variety, as described using words, images, and average values.

[0003] Furthermore, in recent years, a technology has been developed that allows users to specify the cultivation area, cultivation period, and crop type, and then displays a list of varieties that can be cultivated in the specified cultivation area and period from among the specified crops (see, for example, Patent Document 1). [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2002-203002 [Overview of the Initiative] [Problems that the invention aims to solve]

[0005] However, catalogs only list the general characteristics and properties of varieties, making it difficult to accurately determine whether they are suitable for the intended growing location and environmental conditions. As a result, there is a risk that crops may not grow as expected when actually cultivated. Furthermore, even if varieties are listed as shown in Patent Document 1, it is still difficult to determine which variety should be cultivated.

[0006] The present invention aims to provide an agricultural support method that can accurately estimate information about agricultural products. [Means for solving the problem]

[0007] In one embodiment, the agricultural support method is an agricultural support method for estimating information about crops, which estimates the growth amount of each leaf and each flower cluster of the crop based on measured or predicted values ​​regarding the cultivation environment after a predetermined date. In addition, the sink strength of each leaf and each fruit cluster is estimated from the estimated growth amount of each leaf and each flower cluster. Each of the above estimated Sink strength Based on the above, Sink strength The sum and the respective Sink strength In addition to calculating the percentage, The ratio of each sink strength to the calculated total, Actual or predicted values ​​regarding the aforementioned cultivation environment to based on Using the calculated amount of photosynthesis in the crop, This is an agricultural support method in which a computer performs a process to estimate the amount of dry matter distributed to each of the aforementioned leaves and each of the aforementioned flower clusters. [Effects of the Invention]

[0008] The agricultural support method of the present invention has the effect of being able to accurately estimate information about agricultural crops. [Brief explanation of the drawing]

[0009] [Figure 1] This diagram shows the configuration of an agricultural system according to one embodiment. [Figure 2] Figure 2(a) shows the hardware configuration of the user terminal in Figure 1, and Figure 2(b) shows the hardware configuration of the server in Figure 1. [Figure 3] This is a functional block diagram of the server. [Figure 4] This is a flowchart showing the server's processing. [Figure 5] This diagram shows an overview of the growth model. [Figure 6] This diagram conceptually illustrates the changes in crop size during the simulation. [Figure 7] This shows the relationship between the growth stage and size of leaves and stems, expressed as relative values. [Figure 8] This figure shows an example of weather data used in the simulation. [Figure 9] This figure shows an example of the parameters used in the simulation. [Figure 10] Figures 10(a) and 10(b) conceptually illustrate the leaf growth process and fruit growth process in this simulation. [Figure 11] This is Figure (1) showing the screen displaying the simulation results. [Figure 12] This is Figure (2) showing the screen displaying the simulation results. [Figure 13] Figures 13(a) and 13(b) are diagrams (part 3) showing the screen displaying the simulation results. [Figure 14] This is Figure (4) showing the screen displaying the simulation results. [Figure 15] This flowchart shows an example of the processing in the update section. [Modes for carrying out the invention]

[0010] The following describes in detail one embodiment of the agricultural system with reference to Figures 1 to 15. Figure 1 schematically shows the configuration of the agricultural system 100 according to one embodiment. The agricultural system 100 of this embodiment is a system for providing information to support farmers (hereinafter referred to as "producers") in selecting cultivars and managing cultivation when cultivating fruit vegetables such as tomatoes, strawberries, cucumbers, and bell peppers. In this embodiment, the case of a producer cultivating strawberries will be described.

[0011] As shown in Figure 1, the agricultural system 100 comprises a server 10 as an agricultural support device and user terminals 70. The user terminals 70 are devices such as PCs (Personal Computers), tablet devices, and smartphones used by producers. The server 10 and user terminals 70 are connected to a network 80 such as the internet, enabling information exchange between the devices.

[0012] The user terminal 70 transmits the information entered by the producer to the server 10. Figure 2(a) shows the hardware configuration of the user terminal 70. As shown in Figure 2(a), the user terminal 70 includes a CPU (Central Processing Unit) 190, ROM (Read Only Memory) 192, RAM (Random Access Memory) 194, a storage unit (here, an SSD (Solid State Drive) or HDD (Hard Disk Drive)) 196, a network interface 197, a display unit 193, an input unit 195, and a portable storage medium drive 199 capable of reading portable storage media 191. Each of these components of the user terminal 70 is connected to a bus 198. The display unit 193 includes a liquid crystal display, and the input unit 195 includes a keyboard, mouse, touch panel, etc.

[0013] Server 10 is a device that acquires information from user terminals 70, generates information to support strawberry variety selection and cultivation management based on the acquired information, and outputs a screen displaying this information to user terminals 70 used by producers.

[0014] Figure 2(b) shows the hardware configuration of server 10. As shown in Figure 2(b), server 10 includes a CPU 90, ROM 92, RAM 94, storage unit (in this case, SSD or HDD) 96, network interface 97, and a portable storage medium drive 99, etc., as a computer. Each of these components of server 10 is connected to bus 98. In server 10, the CPU 90 executes programs (including cultivation support programs) stored in ROM 92 or HDD 96, or programs (including cultivation support programs) read from the portable storage medium 91 by the portable storage medium drive 99, thereby realizing the functions of each component shown in Figure 4. Note that the functions of each component in Figure 4 may also be realized by integrated circuits such as ASICs (Application Specific Integrated Circuits) or FPGAs (Field Programmable Gate Arrays).

[0015] Figure 3 shows a functional block diagram of the server 10. In the server 10, the CPU 90 executes a program, and as shown in Figure 3, it functions as a selection reception unit 41, an environmental data acquisition unit 42 as an acquisition unit, a cultivation information acquisition unit 43, a simulation unit 44, an output unit 45, and an update unit 46. Figure 3 also illustrates the parameter DB 50, which serves as a storage unit stored in the storage unit 96 of the server 10.

[0016] The selection reception unit 41 receives information on the combination of varieties (e.g., Tochiotome, Koiminori, etc.) and cultivation locations (e.g., Tsukuba, Morioka, Kurume, etc.) selected by the producer. The producer may select one or more combinations of varieties and locations. The selection reception unit 41 transmits the received information to the simulation unit 44.

[0017] The environmental data acquisition unit 42 acquires environmental data for the cultivation site based on the information of the cultivation site received by the selection reception unit 41. The environmental data acquisition unit 42 acquires data corresponding to the selected cultivation site from past weather data and future weather data (predicted data) managed within the server 10 or on devices other than the server 10. The weather data includes data such as outdoor solar radiation and temperature, solar radiation and temperature inside the greenhouse, humidity, CO2 concentration, soil temperature, and soil moisture. The environmental data acquisition unit 42 transmits the acquired data to the simulation unit 44.

[0018] The cultivation information acquisition unit 43 acquires cultivation information entered by the producer (for example, planting date, planting density, soil cultivation / hydroponics cultivation, number of leaves at planting, fertilizer application amount, nutrient solution concentration, etc.). The cultivation information acquisition unit 43 transmits the acquired information to the simulation unit 44.

[0019] The simulation unit 44 reads parameters corresponding to the variety received by the selection reception unit 41 from the parameter DB 50, and uses the read parameters and data transmitted from the environmental data acquisition unit 42 and the cultivation information acquisition unit 43 to create a growth model. Here, the parameters stored in the parameter DB 50 are, for example, the parameters shown in Figure 9, and are defined for each variety. Then, the simulation unit 44 uses the created growth model to perform a simulation of crop growth when the variety selected by the producer is cultivated at the cultivation site and cultivation method selected by the producer. The simulation results from the simulation unit 44 include leaf area, flowering date by flower cluster, yield by flower cluster, fruit dry matter distribution rate, photosynthesis rate, growth rate (stem and leaves), growth rate (fruit), nutrient absorption rate (fertilizer amount), etc. The simulation unit 44 transmits the simulation results to the output unit 45.

[0020] The output unit 45 generates a screen that displays the simulation results received from the simulation unit 44 and transmits it to the user terminal 70 used by the producer. At this time, the output unit 45 summarizes the simulation results for each cultivation condition (combination of variety, cultivation location, and cultivation information) and generates a screen that displays the simulation results for different cultivation conditions in a way that allows for comparison.

[0021] The update unit 46 obtains information from producers about the varieties they actually cultivated and the cultivation results (for example, leaf area, flowering date per flower cluster, yield per flower cluster, etc.). The update unit 46 then updates the parameters stored in the parameter DB 50 so that the actual cultivation results and the simulation results are closer together. The update unit 46 may also decide whether or not to update the parameters using the actual cultivation results based on a predetermined confidence level for producers who have entered the actual cultivation results.

[0022] (Regarding the processing on Server 10) Next, we will explain the details of the processing performed by server 10.

[0023] Figure 4 shows the processing of server 10 in a flowchart. In the processing shown in Figure 4, first, in step S10, the selection reception unit 41 waits until the producer selects a combination of variety and cultivation location on the user terminal 70. Here, for example, it is assumed that the producer has selected the following combinations: "Tochiotome Tsukuba", "Tochiotome Morioka", "Koiminori Tsukuba", "Oishi Berry Tsukuba", and "Sachinoka Kurume".

[0024] If the decision in step S10 is affirmed, in the next step S12, the selection reception unit 41 obtains the combination of the selected variety and cultivation site and transmits it to the environmental data acquisition unit 42 and the simulation unit 44.

[0025] Next, in step S14, the environmental data acquisition unit 42 acquires weather data (past data and forecast data) for the cultivation site based on the cultivation site selected by the producer, which is received by the selection reception unit 41.

[0026] Next, in step S16, the cultivation information acquisition unit 43 waits until the producer inputs cultivation information (for example, planting time, planting density, soil cultivation / nutrient cultivation, number of leaves at planting). Once the producer inputs the cultivation information, the process moves to step S18, where the cultivation information acquisition unit 43 acquires the input cultivation information and transmits it to the simulation unit 44.

[0027] Next, in step S20, the simulation unit 44 reads the parameters for each variety selected by the producer, which are stored in the parameter DB 50, and creates a growth model corresponding to the selected variety and cultivation site combination based on these parameters, the weather data acquired by the environmental data acquisition unit 42, and the cultivation information acquired by the cultivation information acquisition unit 43.

[0028] The simulation unit 44 then runs a simulation using the created growth model and obtains simulation results showing how each variety grows at each cultivation site. Details of the growth model and simulation results created by the simulation unit 44 will be described later. The simulation unit 44 transmits the simulation results to the output unit 45.

[0029] Next, in step S22, the output unit 45 generates a screen displaying the simulation results and sends it to the user terminal 70. The screen displaying the simulation results is, for example, one of the screens shown in Figures 11 to 13.

[0030] (Regarding the simulation) The simulation performed by the simulation unit 44 will be described in detail below.

[0031] (1) Basic concept of simulation In this embodiment, a growth model capable of explaining important differences between varieties is used. Specifically, a growth model capable of explaining characteristics related to photosynthesis, distribution of photosynthetic products, leaf development and elongation, flower development and enlargement, nutrient absorption, and morphological characteristics (leaf shape, light reception) is used, as shown in Figure 5. Although this embodiment describes a simulation for strawberries, the model is designed so that the meaning of the parameters is clear, allowing it to be applied to other crops.

[0032] (a) Regarding growth patterns In the simulation, the growth model is given daily weather data (daily average temperature and daily global solar radiation) to calculate the growth status of each part of the crop (roots, leaves, crown, and fruit). Here, in reality, crops are always in a growth state and their size (size of roots, leaves, crown, number of fruits, etc.) is constantly changing. However, in the simulation, the crop size is fixed at midnight, and it is assumed that the plant receives temperature and solar radiation from midnight to midnight in that state. To reduce the error caused by this assumption, the calculation interval of the simulation (the calculation interval in this simulation is 1 day) can be shortened.

[0033] In this simulation, when calculating for day N, the crop size for that day is determined at 0:00 on day N. This means that in this simulation, the conditions for the crop to receive temperature and solar radiation (relative light reception: a value derived from the leaf area index), and the distribution rate (the proportion of photosynthetic products produced that day distributed to the leaves, crown, flower cluster, and roots) are fixed from 0:00 to 24:00 on day N. Then, at 24:00 on day N, the amount of photosynthesis is calculated based on the temperature, solar radiation, and CO2 concentration for that day (day N), and the crop size changes based on this value through crop growth calculations. Figure 6 conceptually illustrates the change in crop size in this simulation. Note that for day 1 of the calculation, the crop size at 0:00 (day 0 crop size) cannot be calculated, so producers or others set this value.

[0034] (b) Relationship between the amount and size of photosynthetic products In this simulation, photosynthetic products are called "sources," and the organs that produce sources are called "source organs." Source organs are mainly leaves. On the other hand, organs that store and consume sources are called "sink organs." Sink organs include roots, crowns, leaves, and fruits, but the sink organ that consumes the most sources is the fruit.

[0035] In this simulation, crop growth is assumed to be primarily determined by temperature. For example, the growth process of a single leaf is described using accumulated temperature as follows: The accumulated temperature at the point of growth cessation is set to 1.0, and the accumulated temperature at the start of growth is set to 0. The growth stage of the leaf is shown as a relative value of the accumulated temperature. This is defined as the [Leaf Growth Index].

[0036] Here, we define the relative leaf size corresponding to the [Leaf Growth Index] as the [Leaf Size Index] (Figure 7). The [Leaf Size Index] is set with 1.0 representing the size of a fully grown leaf and 0 representing the size before development begins.

[0037] In this simulation, the growth of leaves, fruits, etc., is approximated by a sigmoid curve. The relationship between [Leaf Growth Index] and [Leaf Size Index] is expressed by equation (1) (Figure 7). x is the [Leaf Growth Index] and S(x) is the [Leaf Size Index].

[0038]

number

[0039] The difference in the value of S(x) at 0:00 and 24:00 on day N (see Figure 7) is defined as the [Leaf Increase Index], which is an indicator of the daily increase in leaf growth. Similarly, the relative growth of the flower clusters is defined as the [Fruits Increase Index]. Note that the values ​​of the sigmoid curve for this simulation are S(x)=0.007 at x=0 and S(x)=0.993 at x=1, so we change them to S(x)=0 for x less than or equal to x and S(x)=1 for x greater than or equal to x.

[0040] (c) Overview of the simulation model The simulation is performed based on meteorological data and parameters (constants). The meteorological data used in the simulation, as shown in Figure 8, includes the daily average temperature (°C) and the daily outdoor global solar radiation (MJ / m²). 2 ), CO2 concentration (ppm), etc. An example of parameters set by users such as producers is shown in Figure 9. This simulation is performed on a daily basis, and there are values ​​calculated for each leaf, each flower cluster, and for the entire crop. Growth is calculated for each organ of the crop (leaves, crown, fruit, roots, flower clusters).

[0041] (2) Simulation method (a) Overview of the simulation This simulation is broadly composed of the following processes: "Photosynthesis Rate Calculation," "Crop Growth Calculation," "Fruit Yield Calculation," and "Nutrient Absorption." These calculations are performed on a daily basis. The variables used in the photosynthesis rate calculation (relative light reception, distribution rate) are the values ​​at 0:00 on the calculation day (the day before the calculation day). Relative light reception is a variable used to determine the amount of light that the crop uses for photosynthesis from the amount of solar radiation. The distribution rate is the proportion of the source of synthesis that the crop produces on that day that is distributed to each organ (roots, leaves, crown, fruit). Based on the distribution rate, these values, and the temperature and solar radiation of the day, the amount of photosynthesis at 24:00 on the calculation day is calculated. Then, based on the amount of photosynthesis, the growth rate of the crop at 24:00 on the calculation day is calculated. The crop growth rate calculation is divided into (a) distribution rate calculation, (b) leaf area calculation, (c) number of fruits set calculation, (d) dry weight of fruit calculation, and (e) distribution rate calculation.

[0042] In this specification, the subscript N attached to a variable name indicates the result at 24:00 on day N. Variables calculated for each individual leaf are given the subscript M (leaf rank), and variables calculated for each flower cluster are given the subscript F (flower cluster rank). Variables and constants used in formulas are enclosed in square brackets []. In this simulation, the value at 0:00 on day 1 of calculation is considered the "initial value".

[0043] (b) Crop growth process In this simulation, crop growth is determined by its relationship to the previous leaf, the previous flower cluster rank (the order in which leaves and flower clusters develop; earlier development indicates a lower rank), and the accumulated temperature. Figures 10(a) and 10(b) conceptually illustrate the growth process of leaves and flower clusters in this simulation (the values ​​described below are hypothetical values ​​based on a hypothetical variety and are not actual values). As shown in Figure 10(a), starting from the beginning of growth of the previous leaf, the growth of the next leaf begins when the accumulated temperature reaches 150°C. Starting from this point of growth initiation, leaf growth ends when the accumulated temperature reaches 450°C. On the other hand, as shown in Figure 10(b), starting from the beginning of flower bud differentiation of the previous ranked flower cluster, flower bud differentiation of the next flower cluster begins when the accumulated temperature reaches 600°C. Starting from this flower bud differentiation, the flowering stage begins when the accumulated temperature reaches 600°C, and the enlargement of the fruits constituting the flower cluster begins. Furthermore, starting from the time of flowering, the growth of the flower cluster ends when the accumulated temperature reaches 600°C. Also, as shown in Figure 10(b), the period from the start of flower bud differentiation to the accumulated temperature of 150°C is the flower number determination period, and the number of fruits set is determined by the conditions during this period.

[0044] (c) Calculation of photosynthesis amount Light utilization efficiency (LUE) (gDW / MJ) is a value that indicates the total dry matter production of a crop per unit of light received. It is given as a function of temperature or CO2 concentration.

[0045] Also, the amount of solar radiation inside the greenhouse [total solar radiation inside the greenhouse] (MJ / m 2 ) is [Outdoor total solar radiation] (MJ / m 2 ) is calculated by multiplying it by the [solar radiation transmittance] (constant) of the greenhouse (Equation (2)). [Total solar radiation inside the greenhouse] N =[Solar transmittance] · [Total outdoor solar radiation] N …(2)

[0046] Furthermore, the amount of light (MJ / m²) that crops use for photosynthesis 2 ) is calculated using the following equation (3). Note that the relative light-receiving rate is the value obtained from the leaf area on the previous day. [Light received amount] N =[Relative light reception amount] (N-1)·[Total daily solar radiation inside the house] N …(3)

[0047] Furthermore, the [dry matter production](gDW / m 2 ), which is the photosynthesis amount of the entire crop per unit area, is obtained by the following formula (4). [Dry matter production] N =[Light reception amount] N ·[LUE] N …(4)

[0048] Also, the [photosynthesis amount](gDW / plant), which is the photosynthesis amount per plant, is obtained by the following formula (5) using [Plant Density](plants / m 2 ), which is the number of plants per unit area (constant). [Photosynthesis amount] N =[Dry matter production] N / [Plant Density] …(5)

[0049] Also, in the leaf area calculation to be performed later, [SLA](m 2 / gDW), which is the specific leaf area, the leaf area per dry weight of the leaf, is used. [SLA] N is expressed as a function of temperature.

[0050] (3) Regarding crop growth calculation (a) Partition amount calculation In the partition amount calculation, the partition amount of the produced photosynthesis amount to each organ is determined. The partition amount is calculated as a value for the entire crop and is basically determined by the photosynthesis amount and the partition rate, but [Fruit cluster potential growth amount](gDW / plant) is set as the maximum value of the source that the fruit cluster can receive (maximum partition amount value) and correction is performed. [Fruit cluster potential growth amount] can be obtained by the following formula (6). Note that the [Fruit cluster growth coefficient](gDW / (plant·°C)) in formula (6) is a coefficient indicating the maximum value of the source that one fruit cluster can receive per 1°C of temperature, and [Total Fruits Increase Index] is the total value of the relative growth amounts of all fruit clusters. [Fruit cluster potential growth amount] N =[Fruit cluster growth coefficient]·[Temperature]N ·[Total Fruits Increase Index] N-1 …(6)

[0051] The distribution amount is calculated in three stages. Therefore, the variables for the first two stages are denoted as (1) and (2) to distinguish them from the final result. The units for photosynthesis and distribution are (gDW / plant). [Leaf distribution (1)], [Crown distribution (1)], [Inflorescence distribution (1)], and [Root distribution (1)] (gDW / plant) are calculated using the following equations (7A) to (7D), assuming that the amount of photosynthesis from 0:00 to 24:00 on the current day is allocated to each organ according to the distribution rate of the previous day (0:00 on the current day). [Leaf distribution amount (1)] N =[leaf distribution ratio] N-1 ·[Photosynthesis amount] N …(7A) [Crown distribution amount (1)] N =[Crown distribution rate] N-1 ·[Photosynthesis amount] N …(7B) [Flower bunch distribution amount (1)] N =[Flower cluster distribution rate] N-1 ·[Photosynthesis amount] N …(7C) [Root distribution amount (1)] N =[root distribution ratio] N-1 ·[Photosynthesis amount] N …(7D)

[0052] Furthermore, [Leaf distribution amount (2)], [Crown distribution amount (2)], and [Root distribution amount (2)] (gDW / plant) are corrected as follows so that the flower cluster distribution amount does not exceed the flower cluster potential growth amount. [Leaf distribution amount (2)] N =[leaf distribution amount(1)] N …(7A)' [Crown distribution amount (2)] N =[Crown distribution amount (1)] N …(7B)' [Root distribution amount (2)] N =[root distribution amount(1)] N …(7D)'

[0053] Regarding [Flower cluster distribution amount (2)], see [Flower cluster distribution amount (1)]. N <[Flower cluster potential growth amount] N If so, [Inflorescence distribution amount (2) N =[Inflorescence distribution amount (1)] N …(7C1)' In other cases, [Flower bunch distribution amount (2)] N =[Flower cluster potential growth amount] N …(7C2)' Let's assume that.

[0054] Here, the amount of photosynthesis that was not distributed to the flower cluster because it exceeded the flower cluster potential growth rate, known as [excess dry matter] (gDW / plant), is expressed by the following equation (8). [Excess dry matter] N =([Leaf distribution amount(1)] N +[Crown distribution amount (1)] N +[Inflorescence distribution amount (1)] N +[Root distribution amount (1)] N )-([Leaf distribution amount (2)] N +[Crown distribution amount (2)] N +[Inflorescence distribution amount (2)] N +[Root distribution amount (2)] N ) …(8)

[0055] The surplus dry matter is reallocated to parts other than the flower clusters, and the respective allocation amounts are determined by the following equations (9A) to (9D). [Leaf distribution amount] N =[leaf distribution amount(2)] N +[Leaf surplus distribution rate] · [Surplus dry matter] N …(9A) [Crown distribution amount] N =[Crown distribution amount (2)] N +[Crown surplus distribution rate] · [Surplus dry matter] N …(9B) [Flower bunch distribution amount] N =[Inflorescence distribution amount (2)] N …(9C) [Root distribution amount] N =[root distribution amount(2)] N+[Root surplus distribution rate] · [Surplus dry matter] N …(9D)

[0056] (b) Leaf area calculation In calculating leaf area, the area and the dry matter weight (DW) of the leaf and crown are calculated for each individual leaf. First, the [Leaf Growth Index] (dimensionless), which indicates the leaf growth stage as a relative value of the accumulated temperature, is determined by the following formula (10A). Here, M is the leaf rank. [Leaf Growth Index] M,N ={[Accumulated Temperature] N -(M-1)·150} / 450 …(10A)

[0057] The leaf size index (dimensionless) is calculated using the following formula (10B). The initial value of the Leaf Size Index is set to zero.

[0058]

number

[0059] Furthermore, the leaf growth index [Leaf Increase Index] (dimensionless) is calculated using the following equation (11) from the change in leaf size index from 24:00 the previous day (0:00 on the current day) to 24:00 on the current day. [Leaf Increase Index] M,N =[Leaf Size Index] M,N -[Leaf Size Index] M,N-1 …(11)

[0060] Furthermore, the sum of the [Leaf Increase Index] of all leaves is defined as the [Total Leaf Increase Index], and the [Total Leaf Increase Index] is calculated using the following formula (12). Note that NL represents the number of leaves.

[0061]

number

[0062] Then, using these values, the dry weight of the leaves, [Leaf DW] (gDW / plant), is calculated as follows. Note that the initial value of [Leaf DW] is set by the producer, etc., only for the first leaf (here, the leaf that newly unfolded after planting is considered the first leaf) (for example, 10.0), and all others are set to zero. [Leaf DW] M,N =[Leaf DW] M,N-1 +[Leaf ΔDW] M,N …(13A) Note: [Leaf ΔDW] M,N This can be expressed by the following equation (13B). [Leaf ΔDW] M,N =[leaf distribution amount] M,N • [Leaf Increase Index] M,N [Total Leaf Increase Index] N …(13B)

[0063] Furthermore, the crown is assumed to increase by a certain percentage with each increase in the number of leaves, and the dry matter weight [Stem DW] (gDW / plant) is calculated in the same way as [Leaf DW], as follows. Note that the initial value of [Stem DW] is set by the producer only for the first node (here, the node that newly develops after planting is considered the first node) (for example, 5.0), and all others are set to zero. [Stem DW] M,N =[Stem DW] M,N-1 +[Stem ΔDW] M,N …(14A) Note: [Stem ΔDW] M,N This can be expressed by the following equation (14B). [Stem ΔDW] M,N =[Crown distribution amount] M,N • [Leaf Increase Index] M,N [Total Leaf Increase Index] N …(14B)

[0064] Leaf Area (m 2 / plant) is obtained by the following formula (15) using Specific Leaf Area [SLA] (m 2 / gDW). Note that [Leaf Area] is set by the producer for only the first leaf, and the other leaves are set to 0. [Leaf Area] M,N =[Leaf Area] M,N-1 +[SLA] N ·[Leaf Allocation Quantity] M,N ·[Leaf Increase Index] M,N / [Total Leaf Increase Index] N …(15)

[0065] Also, if the total area of [Leaf Area] of the leaves growing on the crop is [Total Leaf Area(1)] (m 2 / plant), [Total Leaf Area(1)] can be obtained from the following formula (16).

[0066]

Equation

[0067] Furthermore, calculate the leaf parameters used for photosynthesis calculation.

[0068] First, for the Leaf Area Index [LAI] (dimensionless), which is the leaf area per unit land area, it is obtained from the following formula (17) using [Plant Density] (plants / m 2 ), which is the number of crop plants per unit land area. [LAI] N =[Total Leaf Area] N ·[Plant Density] …(17)

[0069] Furthermore, the [relative light reception] (dimensionless), which represents the ratio of the amount of solar radiation received by crops to the amount of solar radiation per unit area of ​​land, is calculated using the extinction coefficient [K] (dimensionless) and the leaf area index of the previous day from the following equation (18). Note that the extinction coefficient is a coefficient that represents how easily light penetrates into the interior of the plant canopy. [Relative light reception] N =1-exp(-[K]·[LAI] N ) …(18)

[0070] (c) Calculation of number of fruit set The number of fruits set is determined for each flower cluster from the amount of photosynthesis (period dry matter production) during a specific period (flower number determination period) starting the day after flower bud differentiation of the crop begins, and from the relationship between period dry matter production and the number of fruits set. Flower bud differentiation occurs in the order of flower cluster rank, and it is assumed that the next flower cluster rank will not differentiate until a certain amount of temperature has accumulated after the flower cluster rank one rank lower (previous rank flower cluster) has differentiated.

[0071] The [Differentiation Initiation Index], which indicates the start of differentiation, is determined by the [Differentiation Condition Determination A] (°C) and [Differentiation Condition Determination B] described below. Here, the [Accumulated Temperature during Flower Bud Differentiation] (°C) is the accumulated temperature on the day the [Differentiation Initiation Index] first becomes 1, and is set to -1.0 before that.

[0072] In this simulation, based on the value of [accumulated temperature during flower bud differentiation] above, we determine [differentiation condition judgment A], which is an index for determining whether the accumulated temperature from the day after flower bud differentiation of the previous rank inflorescence began exceeds a certain value (for example, 600°C), as follows. The value of 600°C is set because, in this variety, four leaves usually develop between inflorescences, and the accumulated temperature required for the development of one leaf is set at 150°C. In the formula, F indicates the rank of the inflorescence. [Differentiation start index] F-1,N If = 1, [Differentiation condition judgment A] F,N =[Accumulated Temperature] N -([Accumulated temperature during flower bud differentiation] F-1,N +600) …(19A) In other cases, [Differentiation condition judgment A] F,N = -1.0 …(19B) And so they ask.

[0073] Furthermore, the [differentiation condition determination B] (gDW / plant), which is an indicator for determining whether the amount of photosynthesis is sufficient for flower bud differentiation, is calculated from the following formula (20). [Differentiation condition judgment B] N = (Average value of [photosynthesis rate] from N-6 to N days) …(20)

[0074] Furthermore, the [Differentiation Initiation Index], which indicates the start of flower bud differentiation, will remain at 1 once it reaches that value. In other words, [Differentiation Initiation Index] F,N-1 If = 1, [Differentiation start index] F,N =1 …(21A) 0.0≦[Differentiation condition judgment A] F,N , and 1.0 < [Differentiation Condition Judgment B] N If so, [Differentiation start index] F,N =1 …(21B) In other cases, [Differentiation start index] F,N =0 …(21C) Let's assume that.

[0075] Furthermore, the [accumulated temperature during flower bud differentiation] in equation (19A) above is the accumulated temperature on the day when the previous day's value of the [differentiation start index] was 0 and the current day's value was 1. The initial value of the [accumulated temperature during flower bud differentiation] is set to -1.0, and once the [accumulated temperature during flower bud differentiation] is set, it remains at the same value. The reason for setting the initial value to a negative value is to distinguish between the [accumulated temperature during flower bud differentiation] after the start of flower bud differentiation as a positive value and the value before that as a negative value. This distinction based on positive and negative values ​​is also performed for the accumulated temperature during flowering. In other words, [Differentiation Initiation Index] F,N-1 = 0, and [Differentiation Start Index] F,N If = 1, [Accumulated temperature during flower bud differentiation] F,N =[Accumulated Temperature] N …(22A) In other cases, [Accumulated temperature during flower bud differentiation] F,N =[Accumulated temperature during flower bud differentiation] F,N-1 …(22B) That is the case.

[0076] Furthermore, in this simulation, the period during which the accumulated temperature after the start of flower bud differentiation is in the range of 0 to 150°C is defined as the flower number determination period, and the [Flower Number Determination Period Index (1)] (dimensionless), which serves as an indicator of whether it is the flower number determination period, is calculated as follows. Note that the flower number determination period is defined as the period during which one leaf unfolds, and assuming that the accumulated temperature per leaf is 150°C, the accumulated temperature of the flower number determination period is set to 150°C. In other words, [Differentiation Initiation Index] F,N If = 0, [Flower Count Determination Period Index (1)] F,N = -1.0 …(23A) In other cases, [Flower Count Determination Period Index (1)] F,N =([Accumulated Temperature] N -[Accumulated temperature during flower bud differentiation] F,N ) / 150 …(23B)

[0077] The [Flower Number Determination Period Index] (dimensionless) is calculated as follows, such that it is 1 when the accumulated temperature from the start of flower bud differentiation is in the range of 0 to 150°C. 0.0 ≤ [Flower Count Determination Period Index (1)] F,N If ≤ 1.0, [Flower Count Determination Period Index] F,N =1 …(24A) In other cases, [Flower Count Determination Period Index] F,N =0 …(24B)

[0078] By the way, the cumulative amount of photosynthesis during the flower number determination period, [Determination Period Photosynthesis Amount] (gDW / plant), is expressed by the following equation (25). [Photosynthesis amount during the determination period] F,N =[Photosynthesis amount during the determination period] F,N-1+[Flower Count Determination Period Index] F,N ·[Photosynthesis amount] F,N …(twenty five)

[0079] Then, [Number of fruits (1)] (fruit / plant) is calculated using the above formula (25) as follows: [Photosynthesis rate during the determination period] [Number of fruits per unit of dry matter production during the determination period] (m 2 • Individuals / gDW (Co., Ltd.), and [Plant Density] (Co., Ltd. / m 2 Using ), it can be calculated as shown in equation (26). [Number of fruit set (1)] F,N =[Photosynthesis amount during the determination period] M,N • [Number of fruits per unit of dry matter production during the decision period] • [Plant Density] …(26)

[0080] Furthermore, the number of fruits per plant should be adjusted to fall within the range of 1.0 to 10.0, as follows. That is, [Number of fruits (1)] F,N If <1.0, [Number of fruit set] F,N =1.0 …(27A) 1.0≦[Number of fruit set (1)] F,N If ≤ 10.0, [Number of fruit set] F,N= [Number of fruit set (1)] F,N …(27B) In other cases, [Number of fruit set] F,N =10.0 …(27C) Let's assume that.

[0081] The flowering period is defined as the point when the accumulated temperature from the flower bud differentiation stage reaches 600°C, and this accumulated temperature is denoted as [Flowering Period Accumulated Temperature](°C). The value of 600°C is set assuming that flowering occurs when four leaves have developed after flower bud differentiation, and that each leaf requires an accumulated temperature of 150°C. The Flowering Period Accumulated Temperature is only valid after the Flower Bud Differentiation Period Accumulated Temperature has been determined, and its initial value is set to -1.0. Furthermore, once the Flowering Period Accumulated Temperature is set, it is assumed to remain at the same value. In other words, [accumulated temperature during flower bud differentiation] F,N If < 0, [Accumulated temperature during flowering period] F,N = -1.0 …(28A) In other cases, [Accumulated temperature during flowering period] F,N =[Accumulated temperature during flower bud differentiation] F,N +600 …(28B) This is the result.

[0082] (d) Inflorescence dry weight calculation The Fruits Growth Index (dimensionless), which represents the relative growth stage of the flower cluster, is calculated as follows. If the accumulated temperature during flowering is invalid (less than 0), the Fruits Growth Index is set to -1.0. The initial value of the Fruits Growth Index is also set to -1.0. In other words, [accumulated temperature during flowering period] F,N If < 0, [Fruits Growth Index] F,N = -1.0 …(29A) In other cases, [Fruits Growth Index] F,N =([Accumulated Temperature] N -[Accumulated temperature during flowering period] F,N ) / 600 …(29B) Let's assume that.

[0083] Furthermore, the [Fruits Size Index] (dimensionless) is calculated using the following equation (30). The initial value of the Fruits Size Index is set to zero.

[0084]

number

[0085] Furthermore, the relative increase in the growth of the flower clusters from 24:00 the previous day (0:00 on the current day) to 24:00 on the current day is used to calculate the [Fruits Increase Index (1)] (dimensionless) from the following equation (31). [Fruits Increase Index (1)] F,N =[Fruits Size Index] F,N -[Fruits Size Index] F,N-1 …(31)

[0086] However, since the flower cluster is assumed to have 10 fruits, it is corrected as shown in equation (32) below and expressed as the [Fruits Increase Index] (dimensionless). [Fruits Increase Index] F,N =([Fruits Increase Index(1)] F,N ·[Number of fruit set] F,N ) / 10 …(32)

[0087] Furthermore, the sum of the [Fruits Increase Index] of all flower clusters is defined as the [Total Fruits Increase Index] (dimensionless) (see equation (33) below). Note that NF represents the number of flower clusters.

[0088]

number

[0089] In this simulation, these values ​​are used to calculate the dry weight of the flower clusters, [Fruits DW] (gDW / plant). Note that, generally, [Fruits DW] does not increase after the [Fruits Growth Index] exceeds 1. Also, the initial value of [Fruits DW] is set to zero. [Fruits DW] F,N =[Fruits DW] F,N-1 +[Fruits ΔDW] F,N …(34A) Note: [Fruits ΔDW] F,N This can be expressed by the following equation (34B). [Fruits ΔDW] F,N =[Inflorescence distribution amount] N• [Fruits Increase Index] F,N / [Total Fruits Increase Index] N …(34B)

[0090] (e) Distribution ratio calculation The crop's [distribution rate] (dimensionless) is calculated as a value for the entire crop based on the crop's growth. The distribution rate is derived from the [flower cluster sink strength] (dimensionless), which is the ease with which the flower cluster receives the source, and the [total sink strength], which is the sum of the sink strengths of all organs.

[0091] The [Total Fruits Increase Index], [Total Leaf Increase Index], and [Total Stem Increase Index] are weighted based on experimental data from dismantling surveys and other sources. The [Fruit Distribution Adjustment Coefficient], [Leaf Distribution Adjustment Coefficient], and [Crown Distribution Adjustment Coefficient] are set to [α], [β], and [γ], respectively. From these, the flower cluster sink strength, leaf sink strength, crown sink strength, and total sink strength are calculated using the following formula. [Hanabusa sink strength] N =[Total Fruits Increase Index] N • [α] …(35A) [Leaf sink strength] N =[Total Leaf Increase Index] N ·[β] …(35B) [Crown sink strength] N =[Total Stem Increase Index] N ·[γ] …(35C) [Total sink strength] N =[Hanabusa sink strength] N +[Leaf sink strength] N +[Crown Sync Strength] N +[Root sink strength] …(35D)

[0092] The [flower cluster distribution rate], [root distribution rate], [leaf distribution rate], and [crown distribution rate] (dimensionless) are calculated from the following equations (36A) to (36D). [Flower cluster distribution rate] N =[Hanabusa sink strength] N [Total sink strength] N …(36A) [Root distribution ratio] N =0.05 …(36B) [Leaf distribution rate] N =[Leaf sink strength] N [Total sink strength] N …(36C) [Crown distribution rate] N =[Crown Sync Strength] N [Total sink strength] N …(36D)

[0093] (4) Calculation of Fruit Yield This simulation assumes that the target crops are not harvested all at once within the flower cluster, but rather gradually. The [Harvest Start Index] (dimensionless), which indicates that harvesting is possible, is set to 1 from the day after the [Fruits Increase Index] first exceeds 1.0, and to zero before that. The day the [Harvest Start Index] first becomes 1 is defined as the harvest start date. The [Harvest Start Index] is expressed as follows, with an initial value of zero. In other words, [Harvest Start Index] F,N-1 If = 1, [Harvest Start Index] F,N =1 …(37A) [FruitsSource Index] F,N-1 If ≥ 1.0, [Harvest Start Index] F,N =1 …(37B) In other cases, [Harvest Start Index] F,N =0 …(37C) This is the result.

[0094] Here, the total yield per flower cluster is given by [Fruits DW] (gDW / plant) on the harvest start date. Note that, generally, [Fruits DW] does not increase after the [Fruits Growth Index] exceeds 1, so it remains the same from the harvest start date onward. Assuming that each fruit is harvested over a period of time when the accumulated temperature reaches 60°C, the dry matter weight of the harvested fruit per 1°C of temperature, [Harvested Fruit Temperature DW] (gDW / plant·°C), is calculated as follows. The initial value of [Harvested Fruit Temperature DW] is set to -1.0, and once a valid value is set, it remains the same. In other words, [Harvested fruit temperature DW] F,N-1 If ≥ 0.0, [Harvested fruit temperature DW] F,N =[Harvested Fruit Temperature DW] F,N-1 …(38A) [Harvest Start Index] F,N = 1, and [number of fruits] F,N-1 If >0.0, [Harvested fruit temperature DW] F,N =[Fruits DW] F,N-1 / ([Number of fruits set] F,N-1 ·60) …(38B) In other cases, [Harvested fruit temperature DW] F,N = -1.0 …(38C) This is how it is calculated.

[0095] The dry weight of harvestable fruit before harvesting on the calculation day is defined as [Fruits unHarvest] (gDW / plant), and [Fruits unHarvest] is calculated as follows. Note that [Fruits unHarvest] is given the previous day's [Fruits DW] on the harvest start date, and decreases thereafter as the amount harvested increases. The initial value of [Fruits unHarvest] is -1.0, and the value before and after the harvest period is also -1.0. In other words, [Fruits unHarvest] F,N-1 If ≥ 0.0, [Fruits un Harvest] F,N =[Fruits unHarvest] F,N-1 -[Fruits HARVEST] F,N-1 …(39A) [Harvest Start Index] F,N-1 = 0, and [Harvest Start Index] F,N If = 1, [Fruits un Harvest] F,N =[Fruits DW] F,N-1 …(39B) In other cases, [Fruits un Harvest] F,N = -1.0 …(39C) This is the result.

[0096] Furthermore, if the harvesting period has started and there are harvestable fruits, the dry weight of the harvested fruits, [Fruits HARVEST] (gDW / plant), is calculated as follows. In other words, [Harvest Start Index] F,N =1, and [Fruits unHarvest] F,N If >0.0, [Fruits HARVEST] F,N =min([Harvested Fruit Temperature DW] F ·[temperature] N [Fruits unHarvest] F,N ) …(40A) In other cases, [Fruits HARVEST] F,N =0.0 …(40B)

[0097] Furthermore, if the total [Fruits HARVEST] of all flower clusters is defined as [Fruit Harvest Dry Weight] (gDW / plant), then [Fruit Harvest Dry Weight] can be expressed by the following formula (41).

[0098]

number

[0099] By multiplying this [fruit harvest dry weight] by the [fruit fresh weight] (g / gDW), the [fruit harvest bioweight] (g / plant) can be calculated as shown in equation (42). [Bioweight of harvested fruit] N =[Fruit harvest dry weight] N • [Fresh fruit weight] …(42)

[0100] Then, by multiplying the [biological weight of harvested fruit] by the [number of plants in the field] (plants), the [biological weight of harvested fruit in the field] (kg), which is the total yield for the field, can be obtained as shown in equation (43). [Field fruit harvest bioweight] N =0.001·[Fruit harvest bioweight] N • [Number of plants in the field] …(43)

[0101] The simulation unit 44 creates a growth model as described above using environmental data and parameters, and uses this growth model to perform growth simulations for each combination of variety, cultivation location, and cultivation information selected by the producer. It then transmits the simulation results (values ​​obtained from the simulation) for each combination to the output unit 45. The simulation unit 44 can also perform simulations for fruits and vegetables other than strawberries, such as tomatoes, strawberries, cucumbers, and bell peppers, by using parameters appropriate to the item and variety.

[0102] (5) Calculation of nutrient absorption The amount of nutrients absorbed is calculated by multiplying the increase in dry matter in the leaves, crown, fruit, and roots by the nutrient content. While nitrogen (N) is used as an example here, similar calculations can be used for other elements. [Leaf ΔN] M,N =[Leaf ΔDW] M,N ×[Leaf N%] M,N …(44A) [Stem ΔN] M,N =[Stem ΔDW] M,N ×[Stem N%] M,N …(44B) [Fruits ΔN] F,N=[Fruits DW] F,N ×[Fruits N%] F,N …(44C) [Root ΔN] M,N =[Root DW] M,N ×[Root N%] M,N …(44D)

[0103] Note that the [Leaf ΔN] in equation (44A) above M,N This represents the amount of nutrients absorbed by the leaf, [Leaf ΔDW] M,N This represents the increase in dry matter in the leaves, [Leaf N%] M,N This represents the nutrient content of the leaf. The same applies to the other equations (44B) to (44D).

[0104] Here, nutrient content ([Leaf N%] M,N The growth rate of an organ (e.g., β) can be expressed as a function of its growth stage (Growth index) using, for example, the following formula, where β and γ are constants. [Leaf N%] M,N =β L ×ln([Leaf Growth Index] M,N )+γ L …(45A) [Stem N%] M,N =β S ×ln([Stem Growth Index] M,N )+γ S …(45B) [Fruits N%] F,N =β F ×ln([Fruits Growth Index] F,N )+γ F …(45C) [Root N%] M,N =γ R …(45D)

[0105] Furthermore, the amount of nutrients absorbed by the leaves, crown, flower clusters, and roots can be expressed by the following formula.

[0106]

number

[0107] Furthermore, the amount of nutrients absorbed per plant [Total ΔN] N This can be expressed by the following equation (47). [Total ΔN] N =[Total Leaf ΔN] N +[Total Stem ΔN] N +[Total Fruits ΔN] N +[Total Root ΔN] N …(47)

[0108] Note: [Total ΔN] N This is the "nutrient absorption amount" estimated from the increase in dry weight per plant. By multiplying this by the planting density, it can be obtained as the "nutrient absorption amount per unit area." Furthermore, by dividing the "nutrient absorption amount" by the [fertilizer utilization efficiency], it can be obtained as the "fertilizer application amount."

[0109] (Regarding the processing of output section 45) When the output unit 45 receives the simulation results transmitted from the simulation unit 44, it generates a screen to display the simulation results. For example, the output unit 45 generates a screen that displays information as shown in Figure 11 as the simulation results. In this case, the screen shown in Figure 11 is displayed on the display unit 193 of the user terminal 70, so producers can check how each variety grows and what yield can be obtained when cultivated at each cultivation site and in what manner. In addition, since the simulation results for cultivating multiple items under different cultivation conditions are displayed side by side, producers can compare what kind of cultivation results can be obtained when the variety and cultivation conditions are different.

[0110] Furthermore, the output unit 45 can generate a screen, such as the one shown in Figure 12, which displays the shipment volume by flower cluster when the variety selected by the producer is cultivated at the selected cultivation site, and output it to the user terminal 70. By referring to the screen in Figure 12, the producer can check the trend of the harvest volume in the terminal flower cluster, second flower cluster, third flower cluster, etc. In Figure 12, the harvest volume of each flower cluster is displayed on separate graphs, but these graphs may be combined and displayed on a single graph. In this case, the bar graph showing the harvest volume of each flower cluster may be color-coded or otherwise displayed for clarity. Furthermore, the output unit 45 may also display the trend of the total yield on a graph, as shown in Figure 13(a), or the trend of the LAI on a graph, as shown in Figure 13(b). In addition, other growth-related information (for example, leaf area, flowering date by flower cluster, photosynthesis rate, growth rate (crown, leaves, fruit, fruit load), nutrient absorption rate (fertilizer rate), etc.) may be displayed numerically or graphically.

[0111] Furthermore, the output unit 45 may display, for example, the simulation results of the changes in the dry weight of leaves, crowns, and each flower cluster when varieties A, B, and C are cultivated at a certain cultivation site, as shown in Figure 14.

[0112] As described above, the output unit 45 may display simulation results in a comparable manner for each combination of variety, cultivation site, and cultivation information, as shown in Figure 11, or it may display simulation results for cultivating a certain variety at a certain cultivation site, as shown in Figures 12 to 13(b). Furthermore, as shown in Figure 14, it may display simulation results for cultivating different varieties at the same cultivation site in a comparable manner. Moreover, it may display simulation results for cultivating a certain variety at different cultivation sites in a comparable manner. In any case, it is possible to appropriately represent the characteristics of a variety compared to conventional catalogs that express the characteristics of a variety using words, images, average values, etc. Therefore, producers can appropriately select varieties and cultivation sites by checking the simulation results. In addition, they can understand how crops will grow in the future, so they can appropriately manage cultivation (securing workers, procuring materials, etc.).

[0113] (Regarding the processing in update section 46) When the update unit 46 receives actual values ​​(actual cultivation results) such as yield trends and LAI trends from the producer, it compares these actual values ​​with the corresponding simulation results and adjusts and updates various parameters so that the simulation results approach the actual values. In this way, the parameters are appropriately updated based on the actual values, so that appropriate simulation results can be obtained in subsequent simulations.

[0114] Furthermore, if the parameters are updated based on information input from all producers, there is a risk that the parameters may not be updated properly. For this reason, the update unit 46 may be configured to update the parameters based on the input information only when information is input from predetermined producers (highly reliable producers). In this case, the update unit 46 executes processing according to the flowchart shown in Figure 15.

[0115] In the process shown in Figure 15, first, in step S30, the update unit 46 waits until the producer inputs the actual cultivation results (measured values) via the user terminal 70. Once the actual cultivation results are input, the process moves to step S32, where the update unit 46 determines whether the input came from a predetermined producer (a highly reliable producer). If the determination in step S32 is negative, the entire process shown in Figure 15 is terminated. However, if it is positive, the process moves to step S34. In step S34, the update unit 46 updates the parameters so that the simulation results are closer to the actual cultivation results, stores them in the parameter DB 50, and terminates the entire process shown in Figure 15.

[0116] Furthermore, if the update unit 46 updates parameters using information entered by a particular producer, it may manage the updated parameters as parameters specific to that producer. This allows each producer to customize the parameters to match their actual cultivation results. Additionally, it may be possible to allow a producer to recall and use parameters from another producer.

[0117] Furthermore, producers may directly modify the parameters. If a producer modifies the parameters, the update unit 46 should update the parameter DB 50 according to the modifications.

[0118] As can be seen from the above description, the simulation unit 44 of this embodiment functions as a read unit that reads parameters indicating the characteristics of each crop variety from the storage unit (parameter DB 50). Furthermore, the simulation unit 44 of this embodiment functions as a prediction unit that creates a growth model for each variety based on the read parameters and information on the cultivation environment (environmental data), and then performs predictions regarding the growth of each variety from the growth model for each variety.

[0119] As described in detail above, according to this embodiment, the simulation unit 44 reads parameters indicating the characteristics of each crop variety from the parameter DB 50 and acquires environmental data corresponding to the cultivation site selected by the producer from the environmental data acquisition unit 42. The simulation unit 44 also creates a growth model for each variety based on the acquired environmental data and the read parameters, and executes a simulation using the created growth model. The output unit 45 then outputs the simulation results as information to support variety selection and cultivation management. Thus, in this embodiment, the results of a simulation on crop growth using growth models created for each variety and cultivation site are displayed as information to support variety selection and cultivation management. Therefore, compared to conventional catalogs that express variety characteristics using words, images, average values, etc., it is possible to appropriately represent the characteristics of each variety. Consequently, producers can appropriately select varieties and cultivation sites, and understand how the crops will grow in the future, thereby enabling them to appropriately manage cultivation.

[0120] Furthermore, in this embodiment, the update unit 46 updates the parameters DB 50 based on the actual cultivation results (yield, etc.) entered by the producer. This allows the parameters to be updated to appropriate values ​​based on cultivation results. In this case, the update unit 46 can improve the accuracy of the simulation by updating the parameters so that the simulation results approach the actual cultivation results.

[0121] Furthermore, in this embodiment, the update unit 46 updates the parameters based on the cultivation results entered by the producer (S34) when the producer is a predetermined producer (a producer with a reliability level of a certain or higher) (S32: affirmative). This increases the likelihood that the parameters will be updated appropriately.

[0122] Furthermore, in this embodiment, the output unit 45 performs simulations for each combination of variety, cultivation location, and cultivation information selected by the producer, and outputs the simulation results in a comparable manner. This allows the producer to compare the simulation results for multiple combinations of varieties, cultivation locations, and cultivation information to determine which varieties should be cultivated and how.

[0123] In the above embodiment, the case in which the server 10 is equipped with an update unit 46 has been described, but the server is not limited to this. In other words, the server 10 does not need to be equipped with an update unit 46.

[0124] In the above embodiment, the case in which the server 10 has the functions of the agricultural support device of the present invention has been described, but the user terminal 70 may also have the functions of the agricultural support device. In other words, the above processing may be realized by a standalone user terminal 70 operating on its own.

[0125] The above processing functions can be implemented by a computer. In this case, a program describing the processing content of the functions that the processing unit should have is provided. By executing this program on a computer, the above processing functions are implemented on the computer. The program describing the processing content can be recorded on a storage medium that can be read by a computer (except for carrier waves).

[0126] When distributing a program, it may be sold in the form of a portable storage medium such as a DVD (Digital Versatile Disc) or CD-ROM (Compact Disc Read Only Memory) on which the program is recorded. Alternatively, the program can be stored in the storage device of a server computer and transferred from the server computer to other computers via a network.

[0127] A computer that executes a program stores, for example, a program recorded on a portable storage medium or a program transferred from a server computer in its own storage device. Then, the computer reads the program from its own storage device and executes processing according to the program. Note that the computer can also directly read the program from the portable storage medium and execute processing according to the program. Also, each time a program is transferred from the server computer, the computer can sequentially execute processing according to the received program.

[0128] The above-described embodiments are preferred examples of the present invention. However, the present invention is not limited thereto, and various modifications can be made without departing from the gist of the present invention.

Explanation of Signs

[0129] 10 Server (Agricultural Support Device) 42 Environmental Data Acquisition Unit (Acquisition Unit) 44 Simulation Unit (Reading Unit, Prediction Unit) 45 Output Unit 50 Parameter DB (Storage Unit) 100 Agricultural System

Claims

1. A method for supporting agriculture that estimates information about agricultural products, Based on measured or predicted values ​​regarding the cultivation environment after a specified date, the growth rate of each leaf and each flower cluster in the crop is estimated, and the sink strength of each leaf and each fruit cluster is estimated from the estimated growth rates of each leaf and each flower cluster. Based on the estimated sink strengths, the sum of the sink strengths and the ratio of each sink strength to the sum are calculated, and the amount of dry matter allocated to each leaf and each flower cluster is estimated using the calculated ratio of each sink strength to the sum and the amount of photosynthesis of the crop calculated based on measured or predicted values ​​related to the cultivation environment. An agricultural support method characterized by having a computer perform the processing.

2. Obtain information on the variety of the aforementioned crop, The parameters indicating the characteristics of the acquired variety are read from the storage unit. Based on the parameters read from the storage unit and the measured or predicted values ​​related to the cultivation environment, a growth model corresponding to the variety is created, and the growth of the crop is predicted using the created growth model corresponding to the variety and the dry matter distribution amount of each leaf and each flower cluster. The agricultural support method according to claim 1, characterized in that the processing is performed by a computer.

3. In the process for predicting growth, Based on the dry matter distribution of each leaf, the weight of each leaf is calculated, and based on the calculated weight of each leaf, the leaf area of ​​each leaf is calculated. By inputting the calculated leaf area of ​​each leaf into the growth model corresponding to the aforementioned variety, the growth of the crop can be predicted. The agricultural support method according to claim 2, characterized in that the processing is performed by the computer.

Images