JP2003006274A - Judgement system for suitable crop and its kind to be introduced, and cultivation ground and time based on climate similarity at each growth stage - Google Patents

Abstract

PROBLEM TO BE SOLVED: To rationally and efficiently create cultivation design and plan for crops to promote efficient agricultural production suitable for the weather environment of a predetermined area. SOLUTION: Using cultivation case data related to target crops and their kinds in a plurality of areas and weather environments in the plurality of areas, which are estimated with a real-time mesh generation method, an appearance state of a weather environment at each growth stage of target crops and their kinds is predicted. Based on climate similarity, success or failure of growing combinations of predetermined areas, crops and their kinds, and cultivation times is determined.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an information providing system, for example, in the field of agriculture, and more specifically to a system for determining suitable crops / suitable varieties, suitable cultivation areas, and suitable crop seasons.

[0002]

2. Description of the Related Art Examining the types, varieties, cropping seasons, and cultivars of crops that can be introduced in a certain area is mainly conducted by actually cultivating the crops and observing the growth state thereof. For some crops, there are criteria for introduction. Specifically, in fruit trees, it is judged whether or not to introduce fruit trees based on the growth limit temperature at a specific growth stage such as the flowering period, or climatic indicators such as the annual average temperature and precipitation during the entire cultivation period. There is. For paddy rice, the possibility of introduction is determined based on the meteorological environment at a specific growth stage, and the occurrence of cold damage based on the temperature during the heading period and the yield / quality prediction based on the temperature during the ripening period are performed. There is.

However, since there are many kinds of agriculturally cultivated crops and they are ecologically diverse, at present, there is no method for comprehensively judging whether or not to introduce all crops. In addition, trials and errors of various types, varieties, and seasons have been tried in various places to determine whether or not introduction is possible, but it is difficult to target all crops, and selection of crops suitable for introduction is difficult. The reality is that the basis cannot be clearly shown.
In addition, the crops that are under consideration for introduction are actually cultivated, and the judgments made based on the results also differ from year to year due to the differences in the weather environment, so many iterations are required, There is a problem that the accuracy is low.

[0004]

Therefore, the present invention aims to reasonably and efficiently perform crop planting design / planning in order to promote efficient agricultural production in accordance with the meteorological environment of a predetermined area. The purpose is to provide a method and a program for determining a suitable crop / variety, suitable cultivation area, suitable cultivation period, and the like.

[0005]

The present invention, which achieves the above object, includes the following. (1) Using the cultivation case data on the target crops and varieties in a plurality of regions and the meteorological environment in the plurality of regions estimated by the real-time mesh creation method, predict the appearance state of the meteorological environment for each growing stage for the target crops and varieties. However, it is characterized that the success or failure of cultivation is judged based on the climatic similarity for a combination of a predetermined area, a predetermined crop / cultivar, and a predetermined cropping season. Judgment method.

(2) On the basis of the above-mentioned climatic similarity, it is determined whether or not to cultivate a given crop / cultivar assuming a case where a predetermined crop / cultivar is sown or planted in a predetermined area at a predetermined crop season. (1) A method for determining a suitable crop / suitable variety, suitable cultivation area, and suitable cropping period according to (1).

(3) Based on the above-mentioned climatic similarity, it is characterized in that a suitable cultivation area for the crop / cultivar is determined on the assumption that a predetermined crop / cultivar is sown or planted at a predetermined cropping season. (1) A method for determining a suitable crop / suitable variety, suitable place for cultivation, and suitable season as described in (1).

(4) Based on the above-mentioned climatic similarity, it is characterized in that a suitable cropping period of the crop / cultivar is determined on the assumption that a predetermined crop / cultivar is sown or planted in a predetermined area ( 1) A method for determining a suitable crop / suitable variety, suitable area for cultivation, and suitable season as described in 1).

(5) The method for determining a suitable crop / suitable variety to be introduced, a suitable cultivation area, and a suitable season as described in (1), wherein the cultivation case data is related to a group of cultivars in which a plurality of varieties are put together.

(6) Using the cultivation case data on target crops and varieties in a plurality of regions and the meteorological environment in the plurality of regions estimated by the real-time mesh creation method, the appearance state of the meteorological environment for each growing stage for the target crops is determined. Prediction and determination of success / failure of cultivation based on climatic similarity for a predetermined area, a predetermined crop / cultivar, and a predetermined crop season combination. Term judgment program.

(7) On the basis of the above-mentioned climatic similarity, it is determined whether or not to cultivate a given crop / cultivar assuming a case where a given crop / cultivar is sown or planted in a given area at a given crop season. (6) Introduced suitable crop / suitable variety, suitable place for cultivation, suitable season determination program.

(8) Based on the above-mentioned climatic similarity, it is characterized in that a suitable cultivation area for a given crop / cultivar is determined on the assumption that a predetermined crop / cultivar is sown or planted at a predetermined crop season. (6) Introduced suitable crop / suitable variety, suitable cultivation area, suitable season determination program.

(9) Based on the above-mentioned climatic similarity, it is characterized in that a suitable cropping period of the crop / cultivar is determined on the assumption that a predetermined crop / cultivar is sown or planted in a predetermined area ( 6) Introduced suitable crop / suitable variety, suitable cultivation area, suitable season determination program.

(10) As the above-mentioned cultivation case data, data relating to a group of varieties in which a plurality of varieties are put together is used, and (6) the suitable crops / suitable varieties for introduction, suitable cultivating land,
Appropriate season determination program.

[0015]

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, suitable crops for introduction according to the present invention
A suitable variety, a suitable cultivation area, a suitable cropping period determination method, and a program (hereinafter referred to as "this system") will be described in detail with reference to the drawings.

The present system is configured as a program that operates according to the flowchart shown in FIG. This program can be operated by a computer device as software. First, in step 1 (indicated by “S-1” in FIG. 1), the user is
Cultivation case data is collected based on the public data of the test site. Here, as the public data, it is possible to use statistical data obtained from the Ministry of Agriculture, Forestry and Fisheries as shown in FIG. The statistical data shown in FIG. 2 is an example of cultivation case data reported by the Ministry of Agriculture, Forestry and Fisheries Statistics Information Department, and is described in the Growing Stage Guidebook by Vegetable Cropping Type (1998). Here, the cultivation case data deals with cultivation cases classified by year and by place of production / test site, and is described as annual case by point cultivation case data in FIG. 1.

In step 1, by collecting the cultivation case data, a database relating to crop seasons is created by associating crop types and varieties with the municipality of the production area, sowing, planting season and harvesting season. In the public data, if the description of a given cropping period can be uniquely determined for that cropping period, the total number of days starting from January 1 as the start date for growth stage breaks such as sowing, planting period and harvesting period. To create cultivation case data. On the other hand, in the public data, if there is a range for a given crop period, sowing,
January 1 for each of the beginning and end of each period or high season for the separation of growth stages such as planting period and harvesting period.
Two cultivation case data are created with the total number of days starting from the day. Therefore, the database created in step 1 includes cultivation case data regarding various kinds and types of crops.

Next, in step 2 (S-2), the normal climate value of the production area / test area is estimated. Here, if the production area / test site can be specified as the position of a so-called 1 km mesh defined in the third area section of the standard area mesh of the numerical map, the production area / test site is defined as the 1 km mesh. However, in the above-mentioned public data, when the production area / test site cannot be specified as a 1 km mesh, for example, the municipality name described in the public data is used as the national numerical information “Japanese city Use the “Latitude and longitude list” of the municipal office / office to identify the location of the office / office, and use the mesh as the production / test site.

In order to estimate the normal year climate value of the production area / test site, for example, the mesh statistical value of the normal year value observed by the Japan Meteorological Agency is used. In this mesh statistic, monthly averages of temperature, maximum and minimum values, precipitation, and monthly average values of snow depth are described. Monthly amount of solar radiation for 1km mesh is announced by the National Institute for Agro-Environmental Sciences.
If the mesh statistics at the desired production site / test site have not been published or are difficult to obtain, climate values are created based on the so-called topographic factor analysis method that has already been published. Therefore, in this system, climate value means temperature, precipitation,
It means various meteorological data such as snow depth and solar radiation.
The climate value obtained in step 2 includes at least one or more of these various weather data.

Next, in step 3 (S-3), the daily weather environment for each year is estimated from the monthly climate value of the production area / test site estimated in step 2 for each normal year. That is, step 3
Then, since the climate value obtained in step 2 is for the long-term monthly average and does not correspond to the day by year, first estimate the normal value of the daily meteorological environment at the production site / test site, and then Estimate actual daily values for a particular year. Specifically, for example, by applying a harmonic analysis method to the climate value obtained in step 2,
Estimate the normal value of the daily meteorological environment at the test site.

In step 3, next, the so-called real-time mesh creation method is applied to the obtained normal year value of the meteorological environment to estimate the daily actual meteorological environment of the production site / test site to which the arbitrary point belongs. To do. The real-time mesh creation method is the deviation of the daily meteorological environment of the production / test site to which an arbitrary point belongs from the average annual value of the daily meteorological environment observed at the local meteorological observation network (AMeDAS) observation points at the nearest several points. Is a method of estimating by adding a weighted average value inversely proportional to the distance to an arbitrary point to the normal value of the arbitrary point.

Next, in step 4 (S-4), a so-called growth stage prediction method is used to create a growth stage prediction formula.
Any conventionally known method and principle may be used as the growth stage prediction method. That is, in step 4, the growth stage of a predetermined crop is predicted using the cropping period information contained in the cultivation case data collected in step 1. In this example,
The prediction formula of the growth stage was created as follows.

A large number of case data are prepared for each type and variety of crops, with data relating to the crop season expressed as the total number of days starting on January 1st and the daily weather environment during the growing season. Then, the growth index for each growth stage such as 0 at the time of sowing or fixed planting and 1 at the time of harvesting is considered, and the growth rate which is an increment corresponding to the weather environment per day is considered.

Various already published models and calculation methods may be used to calculate the growth rate. The method used here is as follows. The growth rate is discretely defined with respect to the meteorological environment, and a spline curve is smoothly interpolated between them. Furthermore, since the growth rate changes smoothly with respect to the weather environment and does not change in a complicated manner, a constraint is added that the numerical differentiation of the growth rate with respect to the weather environment can be expressed by a linear expression. This restriction is due to many typographical errors and observation errors in the description of cultivation cases in statistical data and test site data, differences in cultivation methods, and differences in the climate environment based on the difference in the actual planting site and the position of the government office / hall. This is to minimize the effect of differences and seasons artificially changed by social factors. These relationships are expressed by the following equation (1). F (DVR _T ) = sqr {Σ {(DVI _Dk −1) ² } / n} + λ · sqr {Σ {(DVR _i −DVR _i−1 ) − (at + b)} ² / (m−1)} ... (1)

In the equation (1), DVR_TIs average daily
Growth rate, DVI for elephant environment T_{D k}Is raw for each cultivation case
Growth index and DVR at the end of the growing period_iFor the meteorological environment
I-th DVR defined discretely_T, N is case data
, M is the number of points discretely defined for the meteorological environment, λ
Is the weight of the degree of conformity of the constraint condition to the meteorological environment, the first on the right side
The Σ of the term is the total for the number of cultivation cases, and the Σ of the second term on the right side is the
It is an integration for discrete defined points of the elephant environment. That
And a and b are DVR_TNumerical differentiation of (DVR_i-DV
R _i-1) Of the linear regression with respect to the meteorological environment t
Sqr indicates that the square root is taken.

F (DVR _T ) on the left side indicates the evaluation value of the error of the prediction formula, and the smaller the value, the better, the sq of the first term on the right side.
r {Σ {(DVI _Dk −1) ² } / n} is the square root of the mean of the sum of squares of the prediction error of the harvest time of the case data, λ · of the second term.
sqr {Σ {(DVR _i −DVR _i−1 ) − (at + b)}
^{It can be said that 2} / (m-1)} is a numerical value that weights the degree of conformity of the growth rate to the constraint conditions for the meteorological environment.

The prediction error of the harvest time of the case data in the first term on the right side is obtained by smoothly interpolating the growth rate at about 10 points discretely defined in the meteorological environment with a cubic spline curve. It is a value obtained by subtracting 1 which is the correct value at the end of the growth stage from the growth index obtained by integrating the daily growth rate (DVR _day ) estimated from the meteorological environment during the cultivation period, and is defined by the equation (2). DVI _Dk −1 = (ΣDVR _day ) −1 (2)

In the expression (1), when the value of λ is small and the weight of the second term is light, the erroneous description, the observation error, the difference in the cultivation method, etc. included in the cultivation examples of the statistical data and the data of the test site, etc. the trying described as the difference DVR _T against the weather environment, growth rate (DVR _T) varies violently against weather environment. On the contrary, when the value of λ is large and the weight of the second term is high, the growth rate (DVR _T ) changes parabolicly with respect to the weather environment. Therefore, in reality, the value of λ is adjusted so as to be as small as possible in the range where the growth rate (DVR _T ) smoothly changes with respect to the weather environment. This will change considerably due to mistakes in the description contained in the cultivation examples of statistical data and test site data.

Ten points for discretely defining the meteorological environment
Growth rate in terms of degree (DVR _T) Has already been published
Matyas type random search with high degree of freedom in notation of error
It was decided using the improved algorithm of search method. The search is
Follow the steps below. The function f (w) to be minimized, the search area X and the upper limit of the number of searches
Determine the initial point w (⁰), K = 0, b (^k) = 0. b (^k) Centered normal random vector ξ (^k)
To live. w (^k) + ξ (^k) If ∈ X, go to. if,
Otherwise go to. Calculate the evaluation function. 1) f (w (^k) + ξ (^k)) <f (w (^k)) Then w (^{k + 1}) = w (^k) +
ξ (^k), B (^{k + 1}) = 0.4ξ (^k) + 0.2b (^k). 2) f (w (^k) + ξ (^k)) ≧ f (w (^k)) Holds and f (w (^k)-
ξ (^k)) <f (w (^k)) Holds, w (^{k + 1}) = w (^k) -ξ (^k)
 , B (^{k + 1}) = b (^k) -0.4ξ (^k). If not, w (
^{k + 1}) = w (^k), B (^{k + 1}) = 0.5b (^k). If k = M, search ends. If k <M, set k = k + 1
And go to.

The growth rate (D
FIG. 3 shows an example of the relationship between VR _T ) and the temperature of the meteorological environment. FIG. 3 shows the relationship between the growth rate (DVR _T ) and the temperature of various cultivars belonging to the typical cabbage cultivar group, which is said to have the most remarkable cropping type differentiation,
It shows the characteristics of each cabbage variety.

Next, in step 5 (S-5), step 4
Using the growth stage prediction formula obtained in step 2, the growth stage is predicted for each cultivation case of various types of crops and varieties, and the appearance state of the meteorological environment for each growth stage is investigated. For the growth stage prediction for each cultivation case, the growth index (DVI _Dk ) at the predicted end of the growth period (here, the harvest time) does not become exactly 1, so it is divided by the predicted value so that it becomes 1 at the harvest time. After normalization, it is divided into about 10 stages and the appearance state of the meteorological environment is investigated for each growth stage. That is, in step 5, using the cultivation case data collected in step 1, the meteorological environment encountered for each growth stage of a predetermined crop / cultivar is investigated.

Next, in step 6 (S-6), step 4
Using the growth stage prediction formula obtained in Section 1 and the above-mentioned mesh statistics and yearly values, we investigate the appearance of the climate environment at each growth stage for each crop / cultivar, crop season, and point under consideration for introduction. To do. Specifically, FIG. 4 shows the occurrence state of the meteorological environment for each breeding stage, which is a compilation of the cultivation case data of all the cabbage breeds shown in FIG. In Fig. 4, the Kin system No. 201, YR Nishiki Aki, Shutoku, Cold resistance Daigaku, and Early Ball were selected as the varieties with a lot of planting cases for each group of cabbages with a lot of planting.
% (20 to 80%) is shown for each growth stage in the order of the above variety from the left. From FIG. 4, it is possible to read the change in the temperature environment of the production area due to the change in the growth stage for each variety.

Next, in step 7 (S-7), the appearance state and introduction of the meteorological environment for each subdivided growth stage of the production area / test site, and the crop / varieties, cropping seasons, and points under consideration for introduction Consider the climatic similarities at each growth stage. When investigating climatic similarity, first calculate the progress of the growth stage when cultivated at the assumed cropping period based on the growth prediction formula, and as in the case of cultivation case data, the weather environment for each growth stage. To investigate the occurrence status of. The annual value of the meteorological environment is created by applying the real-time mesh creation method to this point and estimating the daily meteorological environment from the yearly Amedas observation values.

Specifically, cabbage gold system 2 in Fukuyama City, Hiroshima Prefecture
Fig. 5 shows the result of examining the optimum planting period of No. 01. In this example, cabbage gold system No. 201 in Fukuyama City, Hiroshima Prefecture
It shows the meteorological environment encountered at each growth stage when planted on the 5th. In addition, in FIG. 5, when the results are expressed using long-term annual values, the figure becomes too complicated. Therefore, the average value of the daily average temperature is used, and the average value for each subdivided growth stage is used. Is shown. The solid lines in the figure are August, July, September, June, May, 1
The figures show 0, April, November, March, December, February, and January, respectively.

Finally, in step 8 (S-8), from the results obtained in step 7, the occurrence states of the meteorological environment of various cultivation case data nationwide and the meteorological environment of the place where cultivation is assumed are grown. The potential for cultivation of the variety is determined by assessing the climatic similarity at all stages of. Here, the climatic similarity means that, for each period obtained by dividing the period from sowing or planting to harvesting from several to ten or more, at the point and the season to be examined, the climatic environment indicates the cultivation case data. It is determined that the distribution is higher in the central portion of the appearance frequency distribution of the meteorological environment. On the contrary, if it deviates from the range of the cultivation case data, it is determined that the climatic similarity is low.

In the case of FIG. 5 showing the normal value, the average daily temperature at each growth stage is 3 to 4 ° C. in standard deviation.
Considering that there is a degree of fluctuation, the average daily temperature for each year may be 6 to 8 ° C lower or higher than the solid line showing the normal value. Assuming such a range, 4-9
There is a high risk that monthly planting will exceed the high temperature limit immediately after planting or during the harvesting season. And, there is a high risk that the planting period from December to January exceeds the low temperature limit immediately after planting. However, the safety level is quite high from October to November. Therefore, October-November is estimated to be the optimum planting period. In addition, since Hiroshima Prefecture actually recommends such a season, it is determined that the determination by this method is extremely useful. The following three methods are used to determine the climatic similarity in more detail from the daily meteorological environment by year instead of grasping the outline with the normal value.

Climatic Similarity Judgment Method 1 Climatic similarity is defined from two viewpoints: judgment focusing on the appearance frequency distribution of cultivation case data and judgment focusing on the cumulative value of the appearance frequencies. The determination method 1 is a case where attention is paid to the appearance frequency distribution. That is, 0.0 exemplified in FIG.
The relative appearance frequency distribution of the daily air temperature at the specific growth stage subdivided in each of the study cases subdivided by 0.1 of 1.0 is the relative appearance frequency of the daily air temperature at the same growth stage in the production site case. When compared with the distribution, it is shown as in FIG.
In FIG. 6, white circles indicate the relative frequency of occurrence of production cases, and black circles indicate the relative frequency of appearance of study cases.

When focusing on the low temperature side of the relative appearance frequency distribution, the lowest temperature Tb of the study case is compared with the lowest temperature Ta of the production area case, and if Tb is higher than Ta, the difference (Tb-Ta) It is judged to have only safety. Furthermore, when focusing on the high temperature side, the maximum temperature Td in the study case and the maximum temperature Tc in the production area case
If Td is lower than Tc by comparing, it is judged that there is only the difference (Tc-Td) in safety. In Fig. 6, Tb on the low temperature side
Is 4 ° C higher than Ta, so it has a safety of 4 ° C.
Not safe because d is 1 ° C higher than Tc. Low temperature side T in the study case
If b is higher than Ta on the low temperature side of the production site and Td on the high temperature side of the study case is lower than Tc on the high temperature case of the production site, it is determined that the climatic similarity is high, and otherwise it is low. And it is judged that the greater this safety is, the higher the climatic similarity is.

The safety on the high temperature side and the safety on the low temperature side thus obtained are plotted for each of the subdivided growth stages as shown in FIG. If the obtained results are safe at any of the subdivided growth stages, as in Study Case 1 in FIG. 7, Study Case 1 can be introduced, and the climate can be used as a cropping season and a planting site. The similarity is determined to be high. On the other hand, in Study Case 2, the high temperature side is safe at any growth stage, but the low temperature side is not safe in the latter half of the growth stage, and Study Case 2 cannot be introduced compared with the cultivation case data, , Climatic similarity as a planted area is judged to be low. In Study Case 3, the low temperature side is safe at any growth stage,
Since the high temperature side is not safe in the middle stage of growth, this study case 3
Can not be introduced, and it is judged that the climatic similarity is low even in the cropping season and cropping area.

The meteorological environment plotted here may be different from the meteorological element used for predicting the growth stage. For example, the growth stage is predicted from the daily average temperature, but the cumulative appearance frequency of the production site case data may be determined by using the lowest temperature to determine the place where the production site is present and the lowest temperature of the season.

Climatic Similarity Discrimination Method 2 Judgment method 2 makes a decision by paying attention to the cumulative value of the appearance frequency, whereas decision method 1 focuses on the appearance frequency distribution of cultivation case data. That is, the relative appearance frequency distribution of the daily air temperature at the specific growth stage subdivided in the study cases subdivided by 0.1 from 0.0 to 1.0 illustrated in FIG. FIG. 8 shows the comparison with the cumulative value of the appearance frequency of the daily temperature at the same growth stage. Note that FIG.
In, the black circles show the relative value of the cumulative appearance frequency of the production area cases, and the white circles show the relative appearance frequency of the study cases.

The relative value of the cumulative value of the appearance frequency distribution is originally 0 to
It takes only 100%, but here 0% or less, 100
In order to apply it to% or more, the two straight lines in the figure which are extended by connecting the point where the relative value of the cumulative frequency is 0% and the point where the relative value is 100% are assumed in advance. Focusing on the low temperature side of the appearance frequency of the study case, the lowest temperature Ta of the study case is extended upward until it intersects with the relative value of the cumulative value of the occurrence frequency of the production site case, and is extended horizontally from the intersection to intersect the Y axis. If this point is Pa, it is judged that the safety is higher on the low temperature side as Pa is larger than 0%. If the same operation is performed on the high temperature side, P
b is obtained. On the high temperature side, the smaller the Pb is than 100%, the higher the safety on the high temperature side. In FIG. 8, Pa has a safety of more than 0% and about 5% on the low temperature side, but 110% on the high temperature side, which is significantly higher than 100%, and there is no safety.

If the low temperature side Pa of the study example is greater than 0% and the high temperature side Pb of the study example is less than 100%,
Climatic similarity is high, otherwise it is low.
Moreover, it can be judged that the greater the difference between the low temperature side Pa and 0% and the high temperature side Pb and 100%, the higher the climatic similarity.

The safety on the high temperature side and the safety on the low temperature side thus obtained are plotted for each of the subdivided growth stages in the same manner as in the judgment method 1. Then, similar to the judgment method 1, it is judged whether or not it is safe at any of the subdivided growth stages, and the climatic similarity is examined. The meteorological environment plotted here may be different from the meteorological element used for predicting the growth stage, as in the case of the determination method 1.

Climatic Similarity Judgment Method 3 In Judgment Method 3, in Judgment Methods 1 and 2, the growth period from sowing or planting to the harvest season is handled collectively, and it is subdivided into specific growth stages (DVI). While the climatic similarity was determined for the meteorological environment, the growth period was divided into several characteristic growth periods such as from sowing or planting to the flowering period and from the flowering period to the harvesting period, and the growth prediction was performed. This is a method of performing climatic similarity and then determining the climatic similarity in the same manner as the determination methods 1 and 2. This enables more detailed determination.

As described above, by using this system, it is possible to determine whether or not the crop / cultivar under consideration for introduction can be cultivated in a predetermined area. Further, when cultivating a crop or variety under consideration for introduction in a predetermined area, it is possible to determine an appropriate cropping period, and it is possible to judge a suitable sowing or planting time based on the result. Further, by using the present system, it is possible to determine the suitable cultivation area for the crop / variety, assuming that a predetermined crop / variety is sown or planted in a predetermined area.

Therefore, by using this system, the types of crops that can be introduced to agricultural production sites, organizations that formulate regional plans, agricultural producers or organizations that provide technical guidance to agricultural producers, It is possible to teach varieties, crop seasons, and even more suitable points for cultivation. Also,
By using this system, organizations that plan cropping and regional planning can learn more suitable regional plans by knowing the types of horticultural crops that can be introduced, variety, season, and more suitable sites for cultivation. Can be created and farmers can be properly instructed. Furthermore, by using this system, an organization related to distribution can determine the type, variety, and area suitable for a desired horticultural crop, and can easily plan the formation of a new production area.

[0048]

As described above in detail, according to the present invention, it becomes possible to reasonably and efficiently perform crop planting design / planning and the like. Therefore, according to the present invention, it is possible to promote efficient agricultural production according to the weather environment of a predetermined area.

[Brief description of drawings]

1 is a suitable crop / suitable variety, suitable cultivation area, according to the present invention,
It is a flow chart for explaining a suitable season determination method and a program.

FIG. 2 is a conceptual diagram showing an example of public data of production areas and test sites for collecting cultivation case data.

FIG. 3 Growth rate (DVR) for multiple cabbage varieties
It is a characteristic view which shows the relationship between _T ) and temperature.

FIG. 4 is a characteristic diagram showing a relationship between a growth stage and air temperatures encountered at a predetermined growth stage for a plurality of cabbage varieties.

[Fig. 5] Fig. 5 is a characteristic diagram showing a result of examining a suitable planting period for cabbage gold system No. 201 in Fukuyama City, Hiroshima Prefecture.

FIG. 6 is a characteristic diagram for explaining a climatic similarity determination method 1 in a specific subdivided growth stage.

FIG. 7 is a characteristic diagram for explaining determination of climatic similarity for the entire growing period.

FIG. 8 is a characteristic diagram for explaining a climatic similarity determination method 2 in a specific subdivided growth stage.

Claims (10)

[Claims] 1. Use of cultivation case data on target crops and varieties in a plurality of regions and meteorological environments in the plurality of regions estimated by a real-time mesh creation method,
Predict the appearance of the weather environment for each growth stage of the target crop / varieties, and determine the success or failure of cultivation based on the climatic similarity for a predetermined area, a predetermined crop / various combination, and a predetermined crop period. A method for determining an appropriate crop / suitable variety, suitable cultivation area, and suitable cropping period characterized by. 2. Judgment of whether or not to cultivate a given crop / cultivar on the basis of the above-mentioned climatic similarity on the assumption that a predetermined crop / cultivar is sown or planted in a predetermined area at a predetermined crop season. The suitable crop / suitable variety to be introduced according to claim 1,
Cultivation suitable land, suitable season determination method. 3. The suitable cultivating area of the crop / cultivar is determined based on the climatic similarity on the assumption that a predetermined crop / cultivar is sown or planted at a predetermined crop season. Item 1. A method for determining a suitable crop / suitable variety, suitable cultivation area, and suitable cropping period according to Item 1. 4. The optimum crop season of the crop / cultivar is determined based on the climatic similarity on the assumption that a predetermined crop / cultivar is sown or planted in a predetermined area. 1. A method for determining a suitable crop / suitable variety, suitable cultivation area, and suitable cropping season according to 1. 5. The method for determining a suitable crop / suitable variety to be introduced, a suitable cultivation area, and a suitable cropping season according to claim 1, wherein the cultivation case data is related to a group of cultivars in which a plurality of varieties are put together. 6. Cultivation case data on target crops and varieties in a plurality of regions and meteorological environments in the plurality of regions estimated by a real-time mesh creation method are used,
It is characterized by predicting the appearance state of the meteorological environment for each growing stage of the target crop and judging the success or failure of cultivation based on the climatic similarity for the combination of the specified area, the specified crop / cultivar, and the specified cropping period. Introducing a suitable crop / suitable variety, suitable cultivation area, suitable season determination program. 7. Determine whether or not to cultivate a given crop / cultivar based on the above-mentioned climatic similarity, assuming the case of sowing or planting a predetermined crop / cultivar in a predetermined area at a predetermined crop season. The suitable crop / suitable variety to be introduced according to claim 6,
Cultivation suitable land, suitable season determination program. 8. The area suitable for cultivation of the crop / cultivar is determined based on the climatic similarity on the assumption that a predetermined crop / cultivar is sown or planted at a predetermined crop season. Item 6. A program for determining a suitable crop / suitable variety, suitable place for cultivation, and suitable season for introduction. 9. The optimum crop season of the crop / cultivar is determined based on the climatic similarity on the assumption that a predetermined crop / cultivar is sown or planted in a predetermined area. 6. A program for judging suitable crops / various varieties, suitable cultivation areas, and suitable crop seasons described in 6. 10. The program according to claim 6, wherein the cultivation case data is related to a group of cultivars in which a plurality of cultivars are put together.