Prediction method and prediction program

Abstract

To make it possible to accurately predict arrival time of a growth stage.SOLUTION: A prediction method for predicting time to reach a predetermined growth stage in a cultivation period of an overwinter crop including a snowy day in a growth period, in which a computer acquires an actual measurement value or an estimated value of air temperature on a day on which there is no snowfall in the cultivation period of the overwinter crop and acquires the actual measurement value or an estimated value of surface layer soil temperature on a day on which there is snowfall in the cultivation period of the overwinter crop (S10, S12), and inputs the acquired actual measurement value or estimated value of the air temperature on the day on which there is no snowfall and the acquired actual measurement value or estimated value of the surface layer soil temperature on the day on which there is snowfall as the actual measurement value or estimated value of air temperature of each day relative to the prediction model for predicting time to reach the predetermined growth stage based on the actual measurement value or the estimated value of the air temperatures of each day in the cultivation period, and predicts time to reach the predetermined growth stage in the cultivation period of the overwinter crop (S14 to S18).SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present invention relates to a prediction method and a prediction program. [Background technology]

[0002] When cultivating crops, it is necessary to decide the timing of fertilization and harvesting according to the development of the crops, and farmers conduct field surveys to make these decisions. In field surveys, farmers generally go to the field, visually judge the growth status of the crops, and confirm the progress of the crops' development. However, field surveys are time-consuming and labor-intensive, which can be a burden for farmers and others who are facing issues such as an aging workforce and labor shortages.

[0003] Therefore, it is preferable to be able to predict when and to what extent crops will develop without conducting field surveys. Recently, a method (called an accumulated temperature model) has been known in which the day on which the integrated value of temperature from the starting date exceeds a threshold value is used as the predicted date (for example, the day on which a predetermined developmental stage is reached) (see, for example, Patent Documents 1 and 2). Also, a method (called a DVR model) has been known in which the predicted date is the day on which the integrated value of the developmental rate (DVR), obtained by inputting temperature, day length, etc. into a certain function, exceeds a predetermined value (see, for example, Non-Patent Document 1 and Patent Document 3). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2019-030253 [Patent Document 2] Japanese Patent Application Laid-Open No. 2018-164408 [Patent Document 3] Patent Publication No. 2021-190108 [Non-patent literature]

[0005] [Non-Patent Document 1] Nakazono (2014) Estimation model for wheat developmental stages. Japanese Journal of Crop Science, 83, 249-259 Summary of the Invention [Problem to be solved by the invention]

[0006] Using the above method, it is possible to predict the date on which a crop will reach a specific developmental stage (such as heading or maturity) based on meteorological factors such as temperature. However, in the case of winter crops whose growing season includes days of snowfall, inputting the temperature of the area where the crop is grown into an accumulated temperature model or DVR model does not reflect the effects of snowfall, and if the snow accumulation period is long or if snow accumulation and melting occur repeatedly, it may not be possible to accurately predict the date on which the crop will reach a developmental stage.

[0007] An object of the present invention is to make it possible to accurately predict the time when a predetermined developmental stage will be reached. [Means for solving the problem]

[0008] The prediction method of the present invention is a prediction method for predicting the time when a predetermined developmental stage will be reached during a cultivation period of a winter crop that includes days on which snow falls during the growing season, and is a prediction method performed by a computer to obtain actual measured values ​​or estimated values ​​of air temperature on days without snow during the cultivation period of the winter crop, obtain actual measured values ​​or estimated values ​​of surface soil temperature on days with snow during the cultivation period of the winter crop, and input the obtained actual measured values ​​or estimated values ​​of air temperature on days without snow and the obtained actual measured values ​​or estimated values ​​of surface soil temperature on days with snow as the actual measured values ​​or estimated values ​​of air temperature for each day during the cultivation period into a prediction model that predicts the time when the predetermined developmental stage will be reached during the cultivation period of the winter crop.

[0009] In addition, the prediction program of the present invention is a prediction program that causes a computer to predict the time when a predetermined developmental stage will be reached during a cultivation period of a winter crop that includes days on which snow falls during the growing season, and that causes the computer to execute a process of obtaining actual measured values ​​or estimated values ​​of air temperature on days without snow during the cultivation period of the winter crop, obtaining actual measured values ​​or estimated values ​​of surface soil temperature on days with snow during the cultivation period of the winter crop, and inputting the obtained actual measured values ​​or estimated values ​​of air temperature on days without snow and the obtained actual measured values ​​or estimated values ​​of surface soil temperature on days with snow as the actual measured values ​​or estimated values ​​of air temperature for each day during the cultivation period into a prediction model that predicts the time when the predetermined developmental stage will be reached during the cultivation period of the winter crop. [Effects of the Invention]

[0010] The prediction method and prediction program of the present invention have the effect of being able to accurately predict the time when a predetermined developmental stage will be reached. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram schematically illustrating the configuration of a developmental stage prediction system according to one embodiment. [Figure 2] FIG. 2 is a diagram showing the progression of developmental stages of winter-sown wheat. [Figure 3] FIG. 3(a) is a diagram showing an example of the hardware configuration of a server, and FIG. 3(b) is a functional block diagram of the server. [Figure 4] FIG. 4 is a diagram illustrating an example of the acquired information DB. [Figure 5] FIG. 5 is a diagram schematically illustrating information acquired by the information acquisition unit. [Figure 6] FIG. 6 is a flowchart showing the processing of the server. [Figure 7] FIG. 7 is a flowchart showing the process of step S12 in FIG. [Figure 8]FIG. 8 is a diagram showing the acquired information DB updated by the model input information determination unit. [Figure 9] FIG. 9 is a diagram (part 1) for explaining a method for estimating the temperature of the surface soil. [Figure 10] FIG. 10 is a diagram (part 2) for explaining the method for estimating the temperature of the surface soil. [Figure 11] 11(a) and 11(b) are diagrams for explaining an example and a comparative example in a year with little snow. [Figure 12] 12(a) and 12(b) are diagrams for explaining an example and a comparative example in a year with heavy snowfall. [Figure 13] Figure 13(a) is a graph showing the results of predicting the heading date multiple times as in the comparative example, and Figure 13(b) is a graph showing the results of predicting the heading date multiple times as in this embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, one embodiment of the developmental stage prediction system will be described in detail with reference to FIGS.

[0013] Fig. 1 is a diagram schematically illustrating the configuration of a developmental stage prediction system 100 according to one embodiment. As shown in Fig. 1, the developmental stage prediction system 100 of this embodiment includes a server 10 and a user terminal 70. The server 10 and the user terminal 70 are connected to a network 80 such as the Internet, enabling data exchange between the server 10 and the user terminal 70.

[0014] The user terminal 70 is an information processing device that can be used by users such as farmers, such as a PC (Personal Computer), smartphone, or tablet terminal. The user terminal 70 transmits information entered by the user to the server 10, and receives and displays information transmitted from the server 10.

[0015] The server 10 is an information processing device installed in a data center or the like, which predicts the days when crops will reach each development stage (arrival dates) and outputs the results to the user terminal 70.

[0016] Here, the server 10 of this embodiment is assumed to predict the date of reaching a developmental stage when cultivating winter crops whose growing season includes snowfall days (e.g., autumn-sown wheat in the Tohoku and Hokuriku regions, autumn-sown barley, etc.). Figure 2 shows the developmental stages of autumn-sown wheat. As shown in Figure 2, the developmental stages of autumn-sown wheat progress in the following order: sowing stage → germination stage → snow cover stage → snowmelt stage → emergence stage → panicle formation stage → flag leaf stage → heading stage → flowering stage → maturity stage. Fertilization is performed during the panicle formation stage and heading stage, so it is preferable to be able to predict the time of reaching these stages. Furthermore, since harvesting occurs after the maturity stage, it is also preferable to be able to predict the time of reaching the maturity stage. Furthermore, since weather before and after the flowering stage affects yield, it is also preferable to be able to predict the time of reaching the flowering stage. Therefore, the server 10 of this embodiment predicts and outputs at least one of the dates of reaching the panicle formation stage, heading stage, flowering stage, and maturity stage, which are indicated by solid-line frames in Figure 2.

[0017] FIG. 3( a) shows an example of the hardware configuration of the server 10. As shown in FIG. 3( a), the server 10 includes a central processing unit (CPU) 90, a read-only memory (ROM) 92, a random access memory (RAM) 94, storage (here, a solid-state drive (SSD) or a hard disk drive (HDD)) 96, a network interface 97, and a portable storage medium drive 99. These components of the server 10 are connected to a bus 98. In the server 10, the CPU 90 executes a program (including a prediction program) stored in the ROM 92 or the storage 96, or a program read by the portable storage medium drive 99 from the portable storage medium 91, thereby realizing the functions of the components shown in FIG. 3( b). Note that the functions of the components shown in FIG. 3( b) may be realized by an integrated circuit such as an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA).

[0018] Fig. 3(b) shows a functional block diagram of the server 10. As shown in Fig. 3(b), the server 10 functions as an information acquisition unit 20, a model input information determination unit 22, and a prediction unit 24 by the CPU 90 executing a program.

[0019] The information acquisition unit 20 acquires information such as day length and temperature during the cultivation period of winter crops and stores it in the acquired information DB 30. Here, the acquired information DB 30 manages each piece of information, linked to a "number," such as "date," "day length," "temperature," "snow accumulation," "underground temperature," "underground temperature measurement depth," "surface soil temperature," and "information to be input into the model." Note that FIG. 5 is a diagram that schematically illustrates an overview of the information acquired by the information acquisition unit 20.

[0020] "No." is the row number in the acquired information DB 30, and "Date" is the year, month, and day information for each day during the cultivation period. Note that the date corresponding to No. 1 is the sowing date and the starting date of cultivation. "Day length" is the day length for each day during the cultivation period, and "Temperature" is the average daily temperature for each day (t3 in Figure 5). "Snow accumulation" is the amount of snow accumulation for each day (H in Figure 5), a value measured at, for example, 9:00 a.m. Note that if each day is in the past, the temperature and snow accumulation are actual measured values, and if each day is in the future, the temperature and snow accumulation are predicted or past values. Predicted and past values ​​are obtained from the Japan Meteorological Agency database or mesh agricultural weather data from the National Agriculture and Food Research Organization (NARO). "Underground temperature" is the average value of the temperature measured each day by a thermometer placed underground (t1 in Figure 5), and "underground temperature measurement depth" is the depth from the ground surface of the thermometer placed underground (D1 in Figure 5). If the underground temperature has not been measured, the "underground temperature" and "underground temperature measurement depth" columns in Figure 4 will be left blank "-". "Surface soil temperature" is the temperature actually measured using a thermometer at a predetermined sowing depth for winter crops from the ground surface (D2 in Figure 5, for example, 2 cm). If the surface soil temperature has not been measured, the "surface soil temperature" column in Figure 4 will be left blank "-".

[0021] "Information to be input to the model" is information to be input as a temperature value to the prediction model described later. The information to be input to the model is determined by the model input information determination unit 22 described later based on the information stored in the acquired information DB 30 (air temperature, snow accumulation, underground temperature, underground temperature measurement depth, surface soil temperature, etc.).

[0022] The model input information determination unit 22 determines the information to be input as temperature values ​​into the prediction model based on the temperature, snowfall, underground temperature, underground temperature measurement depth, surface soil temperature, etc. for each day stored in the acquired information DB 30.

[0023] The prediction unit 24 calculates a growth index starting from the sowing date (starting date) using an integrated temperature model or a DVR (Developmental Rate) model. The prediction unit 24 also predicts the date on which the growth index reaches a value determined for each growth stage as the time (arrival date) when each growth stage is reached. The prediction unit 24 outputs the predicted arrival date for each growth stage to the user terminal 70.

[0024] Here, the cumulative temperature model is a model that accumulates the temperature (daily average temperature) T for each day. In this cumulative temperature model, the accumulated value is the growth index, and the day when the accumulated value reaches a value determined for each growth stage is predicted to be the day when each growth stage is reached.

[0025] The DVR model is a model that uses the DVR for each day calculated from, for example, any of the following formulas (1) to (3). DVR = (T - Tb) / Tu ... (1) DVR=1 / (1+exp(-a(T-Tb)) / D …(2) DVR=(1-exp(-b(L-Lu)) / (1+exp(-a(T-Tb)) / D …(3)

[0026] Here, T is the temperature (daily average temperature) (°C) for each day, Tb is the critical temperature (°C), Tu is the accumulated temperature (°C) required to reach a certain developmental stage, L is the day length (hours) for each day, Lu is the critical day length (hours), D is the minimum number of days (days) to reach a certain developmental stage, and a and b are coefficients. In areas where DVR can be accurately calculated using day length, the above formula (3) should be used. Furthermore, when the above formulas (1) and (2) are used, the information acquisition unit 20 does not need to acquire the "day length" information in FIG. 4.

[0027] When the DVR model is used, the DVR from the starting date is integrated using the following formula (4) to obtain the DVI (Developmental Index), which is used as the development index. DVI = ΣDVR …(4)

[0028] The day when the DVI reaches a value determined for each developmental stage is predicted as the day when that developmental stage is reached.

[0029] In this embodiment, the model input information determination unit 22 determines information to be input as the temperature value of each day in the above-described integrated temperature model and DVR model.

[0030] (Regarding Server 10 processing) Next, a description will be given of the flow of processing performed by the server 10. Fig. 6 is a flowchart showing the flow of processing performed by the server 10.

[0031] 6 starts, first, in step S10, the information acquisition unit 20 acquires information such as day length and temperature during the cultivation period of winter crops, and stores it in the acquired information DB 30. Here, the information acquisition unit 20 acquires information manually entered by workers, etc., actual measured values ​​entered from devices such as thermometers, and predicted values ​​and past values ​​that can be acquired from databases of the Japan Meteorological Agency and mesh agricultural meteorological data from the National Agriculture and Food Research Organization, etc., and stores them in the acquired information DB 30 (FIG. 4).

[0032] Next, in step S12, the model input information determination unit 22 executes a process of determining information to be input as a temperature value into the prediction model based on the information stored in the acquired information DB 30 of Fig. 4. In this step S12, a process is executed in accordance with the flowchart of Fig. 7.

[0033] When the processing of FIG. 7 starts, first, in step S50, the model input information determination unit 22 selects unselected rows in the acquired information DB 30 of FIG. 4, starting from the top.

[0034] Next, in step S52, the model input information determination unit 22 determines whether the amount of snow in the selected row is greater than 0 cm, i.e., whether there is snow. If there is no snow and the determination in step S52 is negative, the process proceeds to step S54.

[0035] In step S54, the model input information determination unit 22 determines that the information to be input as the temperature value into the prediction model is the "temperature" value stored in the acquired information DB 30 of FIG. 4 (see, for example, row No. 1 in FIG. 8). Thereafter, the model input information determination unit 22 proceeds to step S74, where it determines whether or not all rows in the acquired information DB 30 of FIG. 4 have been selected. If the determination in step S74 is negative, the process returns to step S50.

[0036] On the other hand, if the determination in step S52 is positive, that is, if the amount of snow is greater than 0 cm (if there is snow), the model input information determination unit 22 proceeds to step S56. In step S56, the model input information determination unit 22 determines whether or not an actual measurement value of "surface soil temperature" is present in the selected row. If the determination in step S56 is positive, the process proceeds to step S58, where the model input information determination unit 22 determines that the information to be input as the air temperature value into the prediction model is the "surface soil temperature" stored in the acquired information DB 30 of FIG. 4 (see, for example, row No. k in FIG. 8). Here, it can be said that the surface soil temperature is treated as the temperature where the crops are placed. Thereafter, the process proceeds to step S74.

[0037] On the other hand, if the determination in step S56 is negative, that is, if the selected row does not have a measured value of surface soil temperature, the process proceeds to step S60, where the model input information determination unit 22 determines whether the selected row has a measured value of subsurface temperature. If the determination in step S60 is negative (if the selected row does not have a measured value of subsurface temperature), the process proceeds to step S62.

[0038] When the process proceeds to step S62, the model input information determination unit 22 determines that the information to be input as the temperature value into the prediction model is 0°C (see, for example, row No. m in FIG. 8). In this case, because there is snow, it can be said that the temperature where the crops are placed is estimated to be 0°C. Then, the process proceeds to step S74.

[0039] On the other hand, if the determination in step S60 is positive, that is, if the selected row has an actual measured underground temperature value (see row No. n in FIG. 4), the process proceeds to step S64, where the model input information determination unit 22 determines whether the air temperature in the selected row is equal to or higher than 0° C. If the determination in step S64 is positive, the process proceeds to step S66, where the model input information determination unit 22 assumes that the boundary between snow and soil is 0° C. and estimates the temperature of the surface soil using the following method.

[0040] As shown in Figure 9, if the measured underground temperature is t1, the measured depth of the underground temperature is D1, the temperature of the surface soil to be estimated is t2, and the depth of the surface soil is D2, then since temperature and depth are proportional, it can be expressed as in the following equation (5). t1:t2=D1:D2 …(5)

[0041] Therefore, from the above equation (5), t2 can be expressed by the following equation (6). t2=(t1×D2) / D1 …(6)

[0042] For example, if the underground temperature measured at a depth of 30 cm (= 0.3 m) is 6°C, the temperature t2 of the surface soil at a depth of 2 cm (0.02 m) is: t2 = (6 × 0.02) / 0.3 = 0.4°C It is estimated that...

[0043] Next, in step S68, the model input information determination unit 22 sets the information to be input as the air temperature value into the prediction model to the surface soil temperature estimated in step S66 (see row No. n in FIG. 8). Then, the process proceeds to step S74.

[0044] On the other hand, if the judgment in step S64 is negative, i.e., if the air temperature is below 0°C, the process proceeds to step S70, and the model input information determination unit 22 estimates the temperature of the surface soil in the following manner, taking into account the insulating effect of snow.

[0045] As shown in FIG. 10, if the air temperature is t3, the amount of snowfall is H, and the insulation coefficient of snow is K, the boundary temperature t4 can be expressed by the following equation (7) taking into account the insulation effect of snow. t4=t3×H×K …(7)

[0046] For example, if the air temperature t3 is -6°C, the amount of snow is 50cm (0.5m), and the insulation coefficient K is 0.2, the boundary temperature t4 is t4=(-6)×0.5×0.2=-0.6℃ This becomes:

[0047] Furthermore, if the measured underground temperature is t1, the measured depth of the underground temperature is D1, and the depth of the surface soil is D2, the temperature t2 of the surface soil can be expressed by the following equation (8). t2=t4×(D2 / D1)+(t1×D2) / D1 …(8)

[0048] For example, if D1 is 30cm (=0.3m), t1 is 6℃, and D2 is 2cm (=0.02m), then t2 is: t2 =(-0.6)×(0.28 / 0.30)+(6)×(0.02 / 0.3) =-0.16℃ It is estimated that...

[0049] 7, when the process proceeds to the next step S72, the model input information determination unit 22 sets the information to be input as the air temperature value into the prediction model to the surface soil temperature estimated in step S70 (see line No. p in FIG. 8), and then the process proceeds to step S74.

[0050] If the determination in step S74 is affirmative, that is, if all rows in the acquired information DB 30 in FIG. 4 have been selected, all the processing in FIG. 7 ends, and the process proceeds to step S14 in FIG.

[0051] Returning to Fig. 6, when proceeding to step S14, the prediction unit 24 selects dates in order from the starting date, inputs information on the selected dates (day length in Fig. 8 and information to be input into the model) into the prediction model, and calculates the growth index (accumulated temperature and DVI) from the starting date to the selected date. At this time, the temperature value input into the prediction model is a temperature (temperature where the crops are placed) determined depending on the presence or absence of snow, etc., so the growth index can be calculated with high accuracy taking into account the presence or absence of snow.

[0052] Next, in step S16, the prediction unit 24 determines whether the growth index (accumulated temperature or DVI) has reached a predetermined value determined for each growth stage. If this determination is affirmative, the process proceeds to step S18, where the prediction unit 24 predicts that the selected date will be the date on which the predetermined growth stage will be reached. Thereafter, the process proceeds to step S20. On the other hand, if the determination in step S16 is negative, the process proceeds to step S20 without passing through step S18.

[0053] When the process proceeds to step S20, the prediction unit 24 determines whether all dates in the acquired information DB 30 have been selected. If the determination in step S20 is negative, the process returns to step S14, and the processing and determination in steps S14 to S20 are repeated. On the other hand, if the determination in step S20 is positive, all processing in FIG. 6 is terminated. Note that the prediction unit 24 outputs the prediction result to the user terminal 70 when the processing in FIG. 6 is completed.

[0054] (Example) Examples will be described below.

[0055] FIG. 11(a) is a graph showing the results of calculating the DVI for each day in a year with little snow (a low-snow year) by inputting (substituting) the measured temperature values ​​for each day during the cultivation period of the autumn-sown barley "Minorimugi" into the DVR model (the above formula (1)) to calculate the DVR and then calculating the DVI for each day (comparative example), and the results of calculating the DVI for each day using values ​​that take snow accumulation into account as the temperature values ​​input into the DVR model, as in this embodiment. FIG. 11(b) is a table summarizing the heading date predicted using the comparative example, the heading date predicted using this embodiment, and the actual heading date (investigated heading date). In the comparative example and this embodiment, the heading date was predicted as the day on which the DVI reached 1.

[0056] As shown in FIGS. 11(a) and 11(b), in a year with little snow, the heading date could be predicted with high accuracy in both the comparative example and the present embodiment.

[0057] On the other hand, Figure 12(a) is a graph showing the results of calculating the DVI for each day in a snowy year (heavy snow year) by inputting (substituting) the temperature for each day during the cultivation period of the autumn-sown barley "Minorimugi" into the DVR model (the above formula (1)) to calculate the DVR and then calculating the DVI for each day (comparative example), and the results of calculating the DVI for each day by using a value that takes snow accumulation into account as the temperature value input into the DVR model as in this embodiment. Also, Figure 12(b) is a table summarizing the heading date predicted using the comparative example, the heading date predicted using this embodiment, and the actual heading date (investigated heading date).

[0058] As shown in Figure 12(b), in years with heavy snowfall, the present embodiment was able to predict the heading date with greater accuracy than the comparative example. Also, as can be seen from the graph in Figure 12(a), a difference begins to appear between the DVI of the comparative example and the DVI of the present embodiment during periods of snowfall (February and March), so it can be said that the accuracy of predicting the heading date has improved by calculating the growth index taking snowfall into consideration, as in the present embodiment.

[0059] Fig. 13(a) is a graph showing the relationship between the predicted heading date and the actual heading date (investigated heading date) as a result of multiple predictions of the heading date as in the above comparative example, while Fig. 13(b) is a graph showing the relationship between the predicted heading date and the actual heading date (investigated heading date) as a result of multiple predictions of the heading date as in this embodiment.

[0060] From Figures 13(a) and 13(b), it can be seen that the present embodiment (Figure 13(b)) is better able to suppress the variation (difference from the dashed line) between the actual heading date and the predicted heading date.

[0061] It is believed that the method of this embodiment can accurately predict not only the heading date but also the arrival date of other developmental stages. Furthermore, it is believed that the arrival date of each developmental stage can be accurately predicted in the same manner as above, even if a DVR model or an accumulated temperature model other than Equation (1) is used.

[0062] As described above in detail, in this embodiment, the information acquisition unit 20 and the model input information determination unit 22 acquire actual or estimated values ​​of air temperature on days without snow during the cultivation period of winter crops, and also acquire actual or estimated values ​​of surface soil temperature on days with snow (S10, S12). Furthermore, the prediction unit 24 inputs the air temperature value on days without snow into a prediction model (integration model or DVR model), and inputs the surface soil temperature value as the air temperature value on days with snow, and predicts the time when a predetermined development stage will be reached. This allows for accurate prediction of the time when a predetermined development stage will be reached, taking into account the conditions under which the crop is placed during snowfall.

[0063] Furthermore, in this embodiment, the model input information determination unit 22 can estimate the temperature of the surface soil on a snowy day to be 0°C (S62 in Fig. 7). This allows the estimated temperature of the surface soil under the snow to be an appropriate value without calculation, even if the temperature of the surface soil is not actually measured.

[0064] In this embodiment, the model input information determination unit 22 estimates the temperature of the surface soil on a snowy day based on the underground temperature and the depth underground at which the underground temperature is actually measured (S66, S70). This allows the temperature of the surface soil on a snowy day to be estimated with high accuracy.

[0065] Furthermore, in this embodiment, when estimating the temperature of the surface soil on a snowy day, the model input information determination unit 22 estimates it differently depending on whether the air temperature is 0°C or higher or below 0°C (S66, S70). For example, when the air temperature is 0°C or higher, the model input information determination unit 22 estimates the temperature of the surface soil on a snowy day by assuming that the temperature of the ground is 0°C, and when the air temperature is below 0°C, the model input information determination unit 22 estimates the temperature of the ground based on the air temperature and uses the estimation result to estimate the temperature of the surface soil on a snowy day. This allows for accurate estimation of the temperature of the surface soil on a snowy day depending on the air temperature. In particular, in regions where the air temperature is 0°C or higher on snowy days (e.g., the Tohoku and Hokuriku regions), it is possible to accurately estimate the temperature of the surface soil and accurately predict the day on which each developmental stage will be reached.

[0066] In this embodiment, the model input information determination unit 22 estimates the temperature of the surface soil at the sowing depth of the overwintering crop, thereby making it possible to estimate the temperature of the place where the crop is placed after sowing.

[0067] In the above embodiment, as shown in FIG. 7, when there is snow, the method of estimating the surface soil temperature is changed depending on whether there is an actual measurement of the surface soil temperature or the underground temperature. However, this is not limited to this. For example, when there is snow, the surface soil temperature may be uniformly estimated to be 0°C. In this case, there is no need to install a thermometer underground, which can reduce costs and the amount of processing. Furthermore, the determination in step S64 may be omitted, and steps S66 and S68 may be executed regardless of whether the air temperature is 0°C or higher.

[0068] In the above embodiment, the case where an accumulated temperature model or a DVR model is used to estimate the day when each developmental stage will be reached has been described, but the present invention is not limited to this. That is, a model capable of estimating the day when a developmental stage will be reached based on other temperatures may also be used. The temperature value input to such a model may also be information that takes into account the presence or absence of snow (air temperature or surface soil temperature).

[0069] In the above embodiment, the overwintering crop is described as autumn-sown wheat or autumn-sown barley, but the present invention is not limited to this and may be any other overwintering crop.

[0070] The above processing functions can be realized by a computer. In this case, a program is provided that describes the processing contents of the functions that the processing device should have. By executing the program on a computer, the above processing functions are realized on the computer. The program that describes the processing contents can be recorded on a computer-readable storage medium (excluding carrier waves).

[0071] When distributing a program, it is sold in the form of a portable storage medium on which the program is recorded, such as a DVD (Digital Versatile Disc) or a CD-ROM (Compact Disc Read Only Memory).The program can also be stored in the storage device of a server computer and transferred from the server computer to other computers via a network.

[0072] 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. The computer then reads the program from its own storage device and executes processing in accordance with the program. Note that the computer can also read the program directly from a portable storage medium and execute processing in accordance with that program. The computer can also execute processing in accordance with the program received each time a program is transferred from the server computer.

[0073] The above-described embodiment is a preferred example of the present invention, but the present invention is not limited to this and can be modified in various ways without departing from the spirit of the present invention. [Explanation of symbols]

[0074] 10 Servers 20 Information acquisition department 22 Model input information determination unit 24 Prediction Department 30 Acquisition information DB 70 User terminal 90 CPU (computer) 100 Developmental Stage Prediction System

Claims

1. A method for predicting when a winter crop will reach a predetermined developmental stage during a cultivation period that includes snowfall days during the growing season, comprising: Acquire actual or estimated values ​​of temperature on days without snow during the cultivation period of the winter crop, Obtaining an actual measured value or an estimated value of the temperature of the surface soil on a day with snow during the cultivation period of the winter crop; a prediction model that predicts the time when a predetermined developmental stage will be reached based on the actual measured or estimated values ​​of the air temperature for each day during a cultivation period, inputting the acquired actual measured or estimated values ​​of the air temperature for the day without snow and the acquired actual measured or estimated values ​​of the surface soil temperature for the day with snow as the actual measured or estimated values ​​of the air temperature for each day, and predicting the time when the wintering crop will reach the predetermined developmental stage during the cultivation period; A prediction method characterized in that the processing is performed by a computer.

2. The method of claim 1, wherein the estimated temperature of the surface soil on a snowy day is 0°C.

3. 2. The prediction method according to claim 1, wherein the temperature of the surface soil on a day when snow is present is estimated based on the underground temperature and the depth underground at which the underground temperature is actually measured.

4. The prediction method according to claim 3, characterized in that the method of estimating the temperature of the surface soil on a snowy day differs depending on whether the air temperature is 0°C or higher or below 0°C.

5. If the air temperature is 0°C or higher, the temperature of the surface soil on the day when snow is present is estimated assuming that the temperature of the ground surface is 0°C; The prediction method according to claim 4, characterized in that, when the air temperature is below 0°C, the temperature of the ground surface is estimated based on the air temperature, and the estimated result is used to estimate the temperature of the surface soil on the day when snow is present.

6. 2. The prediction method according to claim 1, wherein the temperature of the surface soil is the temperature at the sowing depth of the overwintering crop.

7. A prediction program that causes a computer to predict when a winter crop will reach a predetermined developmental stage during a cultivation period of the winter crop, the cultivation period including days of snowfall during the growing season, Acquire actual or estimated values ​​of temperature on days without snow during the cultivation period of the winter crop, Obtaining an actual measured value or an estimated value of the temperature of the surface soil on a day with snow during the cultivation period of the winter crop; a prediction model that predicts the time when a predetermined developmental stage will be reached based on the actual measured or estimated values ​​of the air temperature for each day during a cultivation period, inputting the acquired actual measured or estimated values ​​of the air temperature for the day without snow and the acquired actual measured or estimated values ​​of the surface soil temperature for the day with snow as the actual measured or estimated values ​​of the air temperature for each day, and predicting the time when the wintering crop will reach the predetermined developmental stage during the cultivation period; A prediction program that causes the computer to execute a process.

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