Cineraria cultivation process

Abstract

The invention discloses a florists cineraria cultivation technology, and belongs to the technical field of agricultural cultivation, and the florists cineraria cultivation technology comprises the following steps: S1, obtaining historical day-by-day meteorological data of a target cultivation area for at least five consecutive years; acquiring day-by-day weather forecast data of the future 30 days; s2, high-temperature heat damage risk elimination; s3, according to the target full-bloom stage date, utilizing an effective accumulated temperature model to reversely deduce a theoretical sowing date; expanding before and after the theoretical sowing date as an initial candidate window, and removing a high-temperature heat damage window to obtain a dynamic sowing time window; s4, based on historical sowing test data, establishing a germination rate and seedling survival rate prediction model; substituting dates in a dynamic seeding time window into the prediction model, and screening out dates of which the predicted germination rate is not lower than a first threshold value and the predicted survival rate is not lower than a second threshold value; s5, combining the screened date with a future short-term weather forecast to obtain a recommended sowing date; and S6, sowing at the recommended sowing date. The garment can adapt to climate changes.

Description

Technical Field

[0001] This invention belongs to the field of agricultural cultivation technology and relates to a cultivation process for cineraria. Background Technology

[0002] Cineraria, an ideal festive flower for winter and spring, has significant production value. However, cineraria seed germination and seedling growth are extremely sensitive to temperature: sowing too early can easily lead to heat damage, resulting in low germination rates and seedling death (e.g., a 0% survival rate is common when sowing at the end of July); sowing too late delays flowering, failing to meet the demand for festive flowers. Traditional cultivation relies heavily on fixed calendars or experience-based judgments, failing to fully consider interannual climate fluctuations and real-time weather changes, making it difficult to achieve large-scale production with high germination rates, high survival rates, and controllable flowering periods. Summary of the Invention

[0003] The purpose of this invention is to provide a cultivation process for Cineraria, which solves the above-mentioned problems.

[0004] The technical solution adopted in this invention is as follows: A cultivation technique for Cineraria includes the following steps: S1. Obtain historical daily meteorological data for the target cultivation area for at least five consecutive years, including daily average temperature, daily maximum temperature, daily minimum temperature, and number of consecutive days with high temperatures; S2. High-temperature heat damage risk elimination: Obtain daily weather forecast data for the next 30 days, including at least the forecast maximum temperature and the forecast average temperature. Based on the weather forecast data for the next 30 days, for each candidate sowing date, determine whether there will be a risk of high-temperature heat damage within a period of time after sowing from that date. If the preset high-temperature risk conditions are met, then the date is eliminated from the candidate sowing dates. The high temperature risk conditions include: the forecast maximum temperature for three consecutive days after sowing reaches or exceeds the first temperature threshold, or the forecast average temperature for five consecutive days after sowing reaches or exceeds the second temperature threshold. S3. Based on the target full bloom date, the theoretical sowing date is deduced using the effective accumulated temperature model. The effective accumulated temperature model is calculated based on the effective accumulated temperature constant required from sowing to full bloom and the historical average temperature for the same period. The theoretical sowing date is extended by 15 to 20 days before and after as an initial candidate window. After removing the high temperature heat damage window in step S2, the dynamic sowing time window is obtained. S4. Based on historical sowing test data, establish a prediction model for germination rate and seedling survival rate. The prediction model uses temperature-related factors at a certain period after sowing as independent variables to predict germination rate and survival rate. Substitute the dates in the dynamic sowing time window obtained in step S3 into the prediction model and select dates with predicted germination rate not lower than the first threshold and predicted survival rate not lower than the second threshold. S5. Combine the dates selected in step S4 with the short-term weather forecast. If there is no continuous low temperature or high temperature during the forecast period, the recommended sowing date is obtained. S6. On the recommended sowing date, sow the seeds of Cineraria in a substrate of peat moss and vermiculite mixed in a 1:1 volume ratio at a depth of 0.5-1cm. After sowing, water thoroughly and keep the substrate moist. After emergence, apply a diluted liquid fertilizer every 10-15 days. When the seedlings have 4-6 true leaves, transplant them into pots.

[0005] Furthermore, the first temperature threshold in step S2 is 36°C, and the second temperature threshold is 31°C. (The high-temperature risk condition also includes adjusting the threshold according to the heat tolerance of the Cineraria variety).

[0006] Furthermore, the effective accumulated temperature constant in step S3 is obtained by fitting historical test data from at least three different sowing dates. Specifically, the actual number of days from sowing to the opening of the first flower and the average daily temperature during this period are recorded for each batch. The daily effective temperature is calculated and summed, and the average value is taken as the effective accumulated temperature constant. Furthermore, the prediction model in step S4 is a logistic regression model, whose independent variables include the average temperature 30 days after sowing, the cumulative temperature difference between the average daily temperature and 35℃ within 10 days after sowing, the average temperature 15 days after sowing, and the number of days with the highest daily temperature exceeding 32℃ within 7 days after sowing; the model regression coefficients are obtained by fitting historical experimental data using the maximum likelihood estimation method.

[0007] Furthermore, the first threshold in step S4 is 90%, and the second threshold is 95%.

[0008] Furthermore, in step S5, the short-term weather forecast is for the next 10 days, and the continuous low temperature or high temperature weather is defined as three consecutive days with an average daily temperature below 10°C or above 32°C.

[0009] Furthermore, the aforementioned cineraria cultivation process also includes a dynamic feedback correction step: recording the germination time, germination rate, seedling survival rate, and actual flowering period after each actual sowing, storing the data in a database, and refitting the effective accumulated temperature constant and prediction model parameters after the new data accumulates to a certain amount, thereby achieving adaptive updating of the model.

[0010] Furthermore, the aforementioned cineraria cultivation technique is suitable for cineraria cultivation in subtropical monsoon climate zones.

[0011] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. A cultivation technique for Cineraria is mainly applicable to subtropical monsoon climate zones with unstable climates. Cineraria prefers cold and is intolerant of high temperatures. Subtropical monsoon climates are variable, and fixed sowing times cannot guarantee high germination rates, high seedling survival rates, and precise control over the flowering period. This application can combine historical climate data with real-time weather forecasts to dynamically select the optimal sowing window, ensuring high germination rates, high seedling survival rates, and precise control over the flowering period. 2. This invention uses an effective accumulated temperature model to back-calculate the sowing date, and combined with real-time weather forecasts, it can accurately control the flowering period to meet the flower demand of specific festivals such as the Spring Festival. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is a flowchart of a cineraria cultivation process. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described embodiments are merely some embodiments of the invention, and not all embodiments. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0014] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0015] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0016] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0017] Example 1 like Figure 1 As shown, this embodiment of the invention provides a cultivation process for Cineraria, suitable for cultivation in subtropical monsoon climate zones, including the following steps: S1. Obtain historical daily meteorological data for the target cultivation area for at least five consecutive years, including daily average temperature, daily maximum temperature, daily minimum temperature, and number of consecutive days with high temperatures; S2. High-temperature heat damage risk elimination: Obtain daily weather forecast data for the next 30 days, including at least the forecast maximum temperature and the forecast average temperature. Based on the weather forecast data for the next 30 days, for each candidate sowing date, determine whether there will be a risk of high-temperature heat damage within a period of time after sowing from that date. If the preset high-temperature risk conditions are met, then the date is eliminated from the candidate sowing dates. The high temperature risk conditions include: the forecast maximum temperature for three consecutive days after sowing reaches or exceeds the first temperature threshold, or the forecast average temperature for five consecutive days after sowing reaches or exceeds the second temperature threshold. The first temperature threshold in step S2 is 36°C, and the second temperature threshold is 31°C. (The high-temperature risk conditions also include adjusting the thresholds according to the heat tolerance of the Cineraria variety).

[0018] For each candidate sowing date Based on the 30-day forecast data, calculate the high-temperature risk discriminant function. : ; in, Candidate sowing date (a specific day); in, From the sowing day Starting from the first sky The forecast maximum temperature, in °C; in, Sowing Day Starting from the first sky The forecast average temperature, in °C; 36℃ and 31℃: These are the first and second temperature thresholds, determined based on high-temperature heat damage experimental data from a subtropical monsoon climate zone (the experiment was conducted from July of one year to May of the following year at a specific herbaceous flower base in a subtropical monsoon climate zone). The experiment showed that when the daily maximum temperature remained ≥36℃, cineraria seedlings would loom and die within 2-3 days (e.g., the group sown on July 31); when the daily average temperature remained ≥31℃, although it did not reach extreme high temperatures, the cumulative heat damage would still significantly reduce the survival rate. "Continuous days ≥2": This means that the maximum temperature is ≥36℃ for two consecutive days or more, which is consistent with the experimental observation of "continuous high temperature causing death". A 5-day average temperature ≥31℃ reflects the inhibitory effect of a sustained high-temperature environment on seed germination and seedling growth.

[0019] The computer's approach is as follows: for each candidate date Obtain the forecast temperature for the corresponding date from the 30-day forecast data; check if the highest temperature in the previous three days is ≥36℃ for two consecutive days: that is, take... The maximum value in the range, if this maximum value is ≥36℃ and the number of consecutive days is ≥2 (i.e., at least two consecutive days with a temperature ≥36℃), then condition one is met; check the average temperature of the following five days: calculate to If the average value is ≥31℃, then condition two is true; if either condition is true, then... If the date is not specified, it will be removed; otherwise, it will be retained.

[0020] S3. Based on the target full bloom date, the theoretical sowing date is deduced using the effective accumulated temperature model. The effective accumulated temperature model is calculated based on the effective accumulated temperature constant required from sowing to full bloom and the historical average temperature for the same period. The theoretical sowing date is extended by 15 to 20 days before and after as an initial candidate window. After removing the high temperature heat damage window in step S2, the dynamic sowing time window is obtained. The effective accumulated temperature constant in step S3 is obtained by fitting historical experimental data from at least three different sowing dates. Specifically, the actual number of days from sowing to the opening of the first flower and the average daily temperature during this period are recorded for each batch. The daily effective temperature is calculated and summed, and the average value is taken as the effective accumulated temperature constant.

[0021] The effective accumulated temperature model is a fundamental tool for predicting crop development stages; its core formula is: ; in, The effective accumulated temperature required from sowing to full bloom is measured in "days in degrees". in, The number of days from sowing to full bloom; in, : No. The average daily temperature of the day, expressed in °C; in, The baseline temperature for the growth and development of Cineraria is set at 5℃. Below this temperature, the plant stops growing and does not accumulate effective accumulated temperature.

[0022] Effective accumulated temperature refers to the cumulative value of daily effective temperature (daily average temperature minus the base temperature) from one developmental stage to another in a plant. It is calculated and summed using historical experimental data (e.g., recording daily temperatures from sowing to full bloom for a batch sown on September 16th in a certain region). Value; averaged from multiple batches, the effective accumulated temperature constant of Cineraria in this region was obtained. Daily.

[0023] The formula for calculating the sowing date based on the target flowering period is as follows: ; in, Target peak bloom date (e.g., February 17th, Chinese New Year); in, The average temperature for the same period in history (i.e., the period from the expected sowing date to the target flowering date), for example, the average temperature from September to November over the past five years, for example, about 14℃ in a certain region; in, The rounding sign ensures that the calculated number of days is an integer.

[0024] First calculate the average daily effective temperature Use the required total effective accumulated temperature Divide by the average daily effective temperature to obtain the theoretical number of days required; advance this number of days from the target peak flowering period to obtain the theoretical sowing date; due to fluctuations in actual temperature, extend the theoretical sowing date by 15 days before and after as an initial candidate window to accommodate interannual differences and model errors.

[0025] S4. Based on historical sowing test data, establish a prediction model for germination rate and seedling survival rate. The prediction model uses temperature-related factors at a certain period after sowing as independent variables to predict germination rate and survival rate. Substitute the dates in the dynamic sowing time window obtained in step S3 into the prediction model to select dates with a predicted germination rate not lower than a first threshold and a predicted survival rate not lower than a second threshold. The first threshold in step S4 is 90%, and the second threshold is 95%.

[0026] Germination rate prediction model: ; in, Predict germination rate, with a value range of 0 to 1.

[0027] in, The average temperature (°C) 30 days after sowing reflects the thermal conditions during seed germination and early seedling growth.

[0028] in, The cumulative difference between the average daily temperature and 35℃ within 10 days after sowing is calculated using the following formula: ; in, For the first time after sowing The actual or forecasted average temperature of the day. This index quantifies the cumulative effect of high-temperature stress: the closer the daily average temperature is to 35°C, the smaller the difference, the lower the cumulative value, and the milder the stress.

[0029] : Regression coefficients, obtained by fitting historical experimental data using maximum likelihood estimation.

[0030] Logistic regression will combine linear combinations Mapping to the 0-1 interval yields the germination probability; express The higher the value, the higher the germination rate (within a certain range); This indicates that the larger the cumulative high temperature value (i.e., the smaller the cumulative temperature difference, indicating more high-temperature days), the lower the germination rate; by substituting the predicted temperature for the candidate dates, the calculation is performed. ,like (First threshold), then screening is done by germination rate.

[0031] Survival prediction model: ; in, : Predict seedling survival rate, with a value range of 0 to 1; The average temperature (°C) 15 days after sowing reflects the heat conditions during the seedling rooting and early growth stages. The number of days with a maximum daily temperature exceeding 32℃ within 7 days after sowing; this indicator directly counts the frequency of high-temperature heat damage. : Regression coefficients, obtained through fitting; express An elevated level is beneficial for survival; This indicates that the more days of high temperatures, the lower the survival rate; for example, in a certain region, the survival rate of the seeding group sown on September 16th was only 32.21%. (The highest temperature exceeded 32℃ for 4 out of 7 days); and the group on September 28th The survival rate was >95%, consistent with the model prediction. The predicted temperature for the candidate dates was substituted into the calculation... ,like If the value is ≥0.95 (the second threshold), then the survival rate is used for screening.

[0032] S5. Combine the date selected in step S4 with the short-term weather forecast. If there is no continuous low temperature or high temperature during the forecast period, the recommended sowing date is obtained. The short-term weather forecast in step S5 is for the next 10 days. The continuous low temperature or high temperature is defined as 3 consecutive days with an average daily temperature below 10°C or above 32°C. S6. On the recommended sowing date, sow the seeds of Cineraria in a substrate of peat moss and vermiculite mixed in a 1:1 volume ratio at a depth of 0.5-1cm. After sowing, water thoroughly and keep the substrate moist. After emergence, apply a diluted liquid fertilizer every 10-15 days. When the seedlings have 4-6 true leaves, transplant them into pots.

[0033] The prediction model in step S4 is a logistic regression model, whose independent variables include the average temperature 30 days after sowing, the cumulative temperature difference between the daily average temperature and 35℃ within 10 days after sowing, the average temperature 15 days after sowing, and the number of days with the highest daily temperature exceeding 32℃ within 7 days after sowing; the model regression coefficients are obtained by fitting historical experimental data using the maximum likelihood estimation method.

[0034] Example 2 Based on Example 1, this embodiment of the cineraria cultivation process further includes a dynamic feedback correction step: recording the germination time, germination rate, seedling survival rate, and actual flowering period after each actual sowing, storing the data in a database, and refitting the effective accumulated temperature constant and prediction model parameters after a certain amount of new data has been accumulated, thereby achieving adaptive updating of the model. For example, in Example 1, all experimental data after sowing on a certain day of a certain month of a certain year (such as germination time, germination rate of 93.2%, seedling survival rate of 96.7%, and actual flowering period) are stored in the database; subsequently, for each sowing (around September each year), the same type of data is recorded, and after the data accumulates to 30 sets, the regression coefficients (a0, a1, a2, b0, b1, b2) of the effective accumulated temperature constant and prediction model are refitted to adapt to the interannual climate fluctuations of a certain region.

[0035] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A cultivation technique for Cineraria, characterized in that: Includes the following steps: S1. Obtain historical daily meteorological data for the target cultivation area for at least five consecutive years, including daily average temperature, daily maximum temperature, daily minimum temperature, and number of consecutive days with high temperatures; S2. High-temperature heat damage risk elimination: Obtain daily weather forecast data for the next 30 days, including at least the forecast maximum temperature and the forecast average temperature. Based on the weather forecast data for the next 30 days, for each candidate sowing date, determine whether there will be a risk of high-temperature heat damage within a period of time after sowing from that date. If the preset high-temperature risk conditions are met, then the date is eliminated from the candidate sowing dates. The high temperature risk conditions include: the forecast maximum temperature for three consecutive days after sowing reaches or exceeds the first temperature threshold, or the forecast average temperature for five consecutive days after sowing reaches or exceeds the second temperature threshold. S3. Based on the target full bloom date, the theoretical sowing date is deduced using the effective accumulated temperature model. The effective accumulated temperature model is calculated based on the effective accumulated temperature constant required from sowing to full bloom and the historical average temperature for the same period. The theoretical sowing date is extended by 15 to 20 days before and after as an initial candidate window. After removing the high temperature heat damage window in step S2, the dynamic sowing time window is obtained. S4. Based on historical sowing test data, establish a prediction model for germination rate and seedling survival rate. The prediction model uses temperature-related factors at a certain period after sowing as independent variables to predict germination rate and survival rate. Substitute the dates in the dynamic sowing time window obtained in step S3 into the prediction model and select dates with predicted germination rate not lower than the first threshold and predicted survival rate not lower than the second threshold. S5. Combine the dates selected in step S4 with the short-term weather forecast. If there is no continuous low temperature or high temperature during the forecast period, the recommended sowing date is obtained. S6. On the recommended sowing date, sow the seeds of Cineraria in a substrate of peat moss and vermiculite mixed in a 1:1 volume ratio at a depth of 0.5-1cm. After sowing, water thoroughly and keep the substrate moist. After emergence, apply a diluted liquid fertilizer every 10-15 days. When the seedlings have 4-6 true leaves, transplant them into pots.

2. The cultivation process for Cineraria according to claim 1, characterized in that: The first temperature threshold in step S2 is 36°C, and the second temperature threshold is 31°C.

3. The cultivation process for Cineraria according to claim 1, characterized in that: The effective accumulated temperature constant in step S3 is obtained by fitting historical test data from at least three different sowing dates. Specifically, the actual number of days from sowing to the opening of the first flower and the average daily temperature during this period are recorded for each batch. The daily effective temperature is calculated and summed, and the average value is taken as the effective accumulated temperature constant.

4. The cultivation process for Cineraria according to claim 2, characterized in that: The prediction model in step S4 is a logistic regression model, whose independent variables include the average temperature 30 days after sowing, the cumulative temperature difference between the daily average temperature and 35℃ within 10 days after sowing, the average temperature 15 days after sowing, and the number of days with the highest daily temperature exceeding 32℃ within 7 days after sowing; the model regression coefficients are obtained by fitting historical experimental data using the maximum likelihood estimation method.

5. The cultivation process for Cineraria according to claim 1, characterized in that: The first threshold in step S4 is 90%, and the second threshold is 95%.

6. The cultivation process for Cineraria according to claim 1, characterized in that: In step S5, the short-term weather forecast is for the next 10 days, and the continuous low temperature or high temperature weather is defined as three consecutive days with an average daily temperature below 10°C or above 32°C.

7. The cultivation process for Cineraria according to claim 1, characterized in that: It also includes a dynamic feedback correction step: record the germination time, germination rate, seedling survival rate and actual flowering period after each actual sowing, store the data in the database, and when the new data accumulates to a certain amount, refit the effective accumulated temperature constant and prediction model parameters to achieve adaptive updating of the model.

8. The cultivation process for Cineraria according to claim 1, characterized in that: Suitable for cultivation in subtropical monsoon climate zones.

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