A corn planting decision system based on accumulated temperature matching throughout the entire growth period for stable yield protection

Abstract

This invention discloses a maize planting decision-making system based on accumulated temperature matching throughout the entire growth period, belonging to the field of agricultural decision support technology. It includes a temperature reconstruction module for reconstructing the daily average temperature sequence and dividing it into vegetative and reproductive growth stages; an accumulated temperature calculation module for calculating the accumulated temperature supply for both stages; a sowing date back-calculation module for iteratively solving the sowing date interval with the harvest deadline as a constraint; a suitability evaluation module for constructing a comprehensive sowing suitability index sequence based on daily soil moisture content and surface temperature; and a sowing date optimization module for selecting the date corresponding to the maximum index value as the recommended sowing date. This system combines accumulated temperature back-calculation with multi-factor suitability quantitative evaluation of the sowing period, enabling the determination of maize sowing timing from both the perspectives of heat condition satisfaction and soil environmental suitability, thus improving the reliability of decision-making.

Description

Technical Field

[0001] This invention relates to the field of agricultural decision support technology, specifically to a corn planting decision system based on accumulated temperature matching throughout the entire growth period for stable yield protection. Background Technology

[0002] The choice of maize planting date directly affects the matching degree of light and temperature resources throughout the entire growth period, thus determining the final yield and quality. Current planting decisions largely rely on the experience of agricultural workers or forward extrapolation based on a single accumulated temperature index. This involves accumulating accumulated temperature from a preset planting date to determine whether the growth process can be completed before the first frost. This method requires pre-determining several candidate planting dates and then verifying each candidate date with forward accumulated temperature, resulting in a large computational load and a tendency to miss the critical planting window where heat conditions are just met. In practical scenarios with rapid variety updates and large interannual climate fluctuations, forward calculations based on a limited number of candidate planting dates cannot fully capture all feasible planting date ranges, often leading to conservative planting date selection and wasting available light and heat resources.

[0003] Some existing systems incorporate soil moisture or temperature factors when determining the sowing period, but these are often treated as independent indicators for later screening, without simultaneously quantifying soil conditions and accumulated temperature supply capacity at the sowing period within a unified evaluation framework. This separate approach lacks coupling analysis between the optimal window for meeting heat conditions and the suitable window for soil environment. The actually recommended sowing date may be feasible in terms of accumulated temperature conditions, but it may be limited in terms of soil moisture or surface temperature at the sowing stage, thus affecting seedling quality and uniformity.

[0004] Therefore, the first problem that needs to be solved in this field is how to automatically and completely reverse-engineer all feasible sowing dates that meet the accumulated temperature requirements of the variety throughout its entire growth period, using the harvest deadline as a hard constraint, thus avoiding the blindness and omission risks of forward trial calculations. The second problem that needs to be solved is how to incorporate soil moisture content and surface temperature into the same quantitative index within the already solved sowing date range, to achieve a synergistic evaluation of thermal conditions and soil environmental conditions, so that the final recommended sowing date achieves a balance between accumulated temperature protection and sowing and emergence conditions. Summary of the Invention

[0005] This invention provides a maize planting decision system based on accumulated temperature matching throughout the entire growth period. The purpose is to solve the problems of existing forward accumulated temperature calculation methods that easily miss critical planting windows and cannot provide a complete range of feasible planting dates. At the same time, it overcomes the defect of separating soil moisture and temperature evaluation from accumulated temperature decision-making. The recommended planting date is determined through reverse deduction and the comprehensive suitability index to achieve the goal of stable yield.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The present invention provides a corn planting decision system based on accumulated temperature matching throughout the entire growth period. This system, through precise reconstruction of historical meteorological data, scientific division of corn physiological development stages, and precise matching of accumulated temperature supply and demand, can reversely deduce the recommended planting date that meets the accumulated temperature requirements throughout the entire growth period and is suitable for planting conditions, thereby effectively avoiding the risk of insufficient accumulated temperature or frost due to improper planting time, and achieving stable corn yield.

[0007] The system includes a temperature reconstruction module, an accumulated temperature calculation module, a sowing date reverse calculation module, a suitability evaluation module, and a sowing date optimization module. The temperature reconstruction module is used to reconstruct the daily average temperature sequence based on the historical meteorological reanalysis data of the target plot, and to divide the entire growth period into vegetative growth stage and reproductive growth stage based on the physiological development time parameters of the maize variety. The accumulated temperature calculation module is used to calculate the first accumulated temperature supply and the second accumulated temperature supply in the reproductive growth stage based on the accumulated temperature demand thresholds of the vegetative growth stage and the reproductive growth stage, respectively. The sowing date reverse calculation module is used to input the first accumulated temperature supply and the second accumulated temperature supply into the sowing date reverse calculation model. The sowing date reverse calculation model uses a preset harvest deadline as a constraint boundary and iteratively solves the sowing date interval that meets the accumulated temperature demand of the vegetative growth stage and the reproductive growth stage. The suitability evaluation module is used to construct a comprehensive sowing suitability index sequence based on the daily soil moisture content sequence and the daily surface temperature sequence within the sowing date interval. The sowing date optimization module is used to select the date corresponding to the maximum value of the comprehensive sowing suitability index within the sowing date interval as the recommended sowing date.

[0008] As a technical solution of this invention, the temperature reconstruction module, when reconstructing the daily average temperature sequence, first extracts the daily maximum and minimum temperatures of the grid points where the target plot is located from historical meteorological reanalysis data, and converts them into a preliminary daily average temperature sequence using trigonometric interpolation. Then, using the measured daily average temperatures of multiple meteorological stations within a preset radius around the target plot as calibration references, the preliminary daily average temperature sequence is corrected for errors, resulting in a corrected daily average temperature sequence. This combination of spatial downscaling and multi-source calibration effectively improves the local representativeness and accuracy of the temperature sequence, providing a reliable foundation for accumulated temperature calculation.

[0009] As a further improvement of the present invention, the temperature reconstruction module obtains the physiological development time parameters of the maize variety when dividing the vegetative growth stage and the reproductive growth stage. These physiological development time parameters include the first cumulative light and heat hours from emergence to tassel differentiation, the second cumulative light and heat hours from tassel differentiation to silking, and the third cumulative light and heat hours from silking to physiological maturity. The time period corresponding to the first cumulative light and heat hours is divided into the vegetative growth stage, and the time period corresponding to the sum of the second and third cumulative light and heat hours is divided into the reproductive growth stage. This stage division based on the variety-specific light and heat hours makes the setting of accumulated temperature requirements more consistent with the variety's own developmental rhythm.

[0010] Preferably, when calculating the accumulated temperature supply, the accumulated temperature calculation module uses a preset fertility baseline temperature as the lower limit for accumulated temperature calculation. It accumulates the daily effective temperatures above this fertility baseline temperature in the corrected daily average temperature sequence to obtain an active accumulated temperature sequence. Then, it weights and accumulates the active accumulated temperature sequence according to the first growth degree-day coefficient corresponding to the vegetative growth stage to obtain the first accumulated temperature supply. Finally, it weights and accumulates the active accumulated temperature sequence according to the second growth degree-day coefficient corresponding to the reproductive growth stage to obtain the second accumulated temperature supply. By introducing growth degree-day coefficients for different fertility stages, the difference in the contribution of the same effective temperature to the development process in the vegetative and reproductive growth stages can be reflected, making the calculation of the accumulated temperature supply more precise.

[0011] As another technical solution of the present invention, the sowing date reverse calculation module, when solving the sowing date interval, takes the preset harvest deadline as the starting iteration point and decreases day by day along the time axis, sequentially calculating the first accumulated temperature cumulative value for the number of days required for the reproductive growth stage backward from the current iteration point, and the second accumulated temperature cumulative value for the sum of the number of days required for the vegetative growth stage and the reproductive growth stage. When the first accumulated temperature cumulative value first reaches the accumulated temperature requirement threshold of the reproductive growth stage and the second accumulated temperature cumulative value first reaches the sum of the accumulated temperature requirement thresholds of the vegetative growth stage and the reproductive growth stage, the date corresponding to the current iteration point is marked as the sowing deadline, and the iteration continues forward until the date when the first accumulated temperature cumulative value or the second accumulated temperature cumulative value is lower than the corresponding threshold is marked as the sowing start date. The sowing start date and the sowing deadline constitute the sowing date interval. This reverse iteration mechanism ensures that the accumulated temperature of corn throughout its entire growth period can be fully met before the harvest deadline, fundamentally avoiding insufficient grain filling and yield reduction caused by low temperatures in the later stages of growth.

[0012] The preset harvest deadline is determined by a safety buffer period of several days, based on the average first frost date of the target plot's location over the years. This safety buffer period is calculated by combining the dehydration rate of the corn variety with the efficiency of the harvesting operation. This setting ensures that the harvest deadline not only considers the climate guarantee rate but also takes into account the variety's dehydration characteristics and actual production operations, guaranteeing grain quality and harvest feasibility.

[0013] The preset growth baseline temperature is obtained by querying the full growth period baseline temperature parameters recorded in the variety approval announcement of the target maize variety. The first growth degree-day coefficient and the second growth degree-day coefficient are determined by accumulated temperature-development rate regression analysis on the historical staggered sowing test data of the target maize variety. These parameters are all derived from authoritative announcements or actual experiments, ensuring the scientific nature and variety-specificity of the system's built-in thresholds.

[0014] In a preferred embodiment of the present invention, before constructing the comprehensive sowing suitability index sequence, the suitability evaluation module, centered on the target plot, extracts the daily soil volumetric water content at multiple depths within the sowing date interval from the soil moisture monitoring network, and integrates the soil volumetric water content at multiple depths into the daily soil moisture content sequence using inverse distance weighted interpolation. Furthermore, it extracts the daily average surface temperature within the sowing date interval from the automatic weather station data of the target plot, and uses cubic spline interpolation to complete the missing date data, obtaining the daily surface temperature sequence. Through the fusion and interpolation completion of multi-source data, complete and spatially continuous daily soil moisture and temperature data are obtained, improving the reliability of the sowing suitability evaluation.

[0015] Furthermore, when constructing the comprehensive sowing suitability index sequence, the suitability evaluation module divides the daily soil moisture content sequence for each day within the sowing date interval by a preset upper limit for suitable sowing moisture content to obtain a soil moisture suitability component; it subtracts a preset lower limit for suitable sowing temperature from the daily surface temperature sequence for each day within the sowing date interval and divides it by a preset suitable sowing temperature range to obtain a soil temperature suitability component; then, it multiplies the soil moisture suitability component and the soil temperature suitability component for the same day to obtain the comprehensive sowing suitability index for that day. This process is repeated for all days within the sowing date interval to form the comprehensive sowing suitability index sequence. This multiplicative comprehensive index can sensitively reflect the synergistic restrictive effect of soil moisture and temperature on sowing and seedling emergence. Any unsuitable condition will lead to a significant decrease in the index, ensuring that the selected dates simultaneously achieve optimal conditions across multiple dimensions.

[0016] When selecting recommended sowing dates, the sowing date optimization module performs a monotonicity test on the comprehensive sowing suitability index sequence and extracts candidate dates corresponding to all local maxima in the sequence. The candidate date with the highest comprehensive sowing suitability index is then selected as the date corresponding to the maximum comprehensive sowing suitability index and output as the recommended sowing date. By extracting local maxima and then selecting the optimal date, the influence of abnormal index peaks caused by short-term weather fluctuations can be avoided. This ensures that the final recommended sowing date combines accumulated temperature satisfaction, sowing suitability, and temporal stability, providing a clear basis for sowing decisions to ensure stable corn yields.

[0017] The technical effects and advantages provided by the present invention in the above technical solution are as follows: A reverse-engineering module for sowing dates is employed. Starting from a preset harvest deadline, the iteration proceeds forward along the time axis, decreasing daily. It sequentially calculates the first accumulated temperature value (representing the number of days required for the reproductive growth stage) and the second accumulated temperature value (representing the sum of the days required for the vegetative and reproductive growth stages). When the first accumulated temperature value first reaches the accumulated temperature requirement threshold for the reproductive growth stage and the second accumulated temperature value first reaches the sum of the two stage thresholds, the current iteration point is marked as the sowing deadline. The iteration continues until either the first or second accumulated temperature value falls below the corresponding threshold, which is then used as the sowing start date, thus automatically forming a complete sowing date range. This reverse iterative solution method eliminates the need for pre-setting candidate sowing dates, directly traversing all sowing dates where the heat conditions are continuously met from the harvest deadline. This avoids omissions of critical sowing dates in multiple forward iterations, ensuring a complete characterization of the available light and heat resources boundary.

[0018] After obtaining the sowing date range, a suitability evaluation module is used to divide the daily soil moisture content sequence within the range by the preset upper limit of suitable sowing moisture content to obtain the soil moisture suitability component. The daily surface temperature sequence is subtracted from the preset lower limit of suitable sowing temperature and then divided by the suitable sowing temperature range to obtain the soil temperature suitability component. The two components for the same day are then multiplied to obtain the comprehensive sowing suitability index. By incorporating soil moisture and temperature into the same dimensionless index and calculating it daily within the feasible accumulated temperature date range, a continuous quantitative expression of the suitability of the soil environment within the heat condition satisfaction zone is achieved. This allows the sowing date optimization module to extract local maxima from the index sequence and select the date corresponding to the maximum value as the recommended sowing date. This determined sowing period not only ensures that the growth process is completed before the harvest deadline in terms of accumulated temperature conditions, but also provides a better combination of soil moisture and surface temperature at the sowing stage, which is conducive to seedling emergence and early population establishment, reducing the risk of delayed emergence and seedling loss caused by insufficient soil moisture or low temperature during the sowing period. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0020] Figure 1 This is a schematic diagram of the corn planting decision system based on accumulated temperature matching throughout the entire growth period; Figure 2 This is a flowchart of the construction process for the comprehensive index sequence of sowing suitability; Figure 3 This is a flowchart for determining the recommended sowing date based on the comprehensive sowing suitability index; Figure 4 This is a regression diagram of accumulated temperature and development rate during the vegetative growth and reproductive growth stages of maize. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] See Figure 1 This invention provides a maize planting decision system based on accumulated temperature matching throughout the entire growth period, the overall implementation scheme of which is as follows: The temperature reconstruction module reconstructs the daily average temperature sequence based on the historical meteorological reanalysis data of the target plot, and divides the vegetative growth stage and reproductive growth stage within the entire growth period based on the physiological development time parameters of the maize variety.

[0023] The accumulated temperature calculation module calculates the first accumulated temperature supply for the vegetative growth stage and the second accumulated temperature supply for the reproductive growth stage based on the accumulated temperature demand thresholds for the vegetative growth stage and the reproductive growth stage, respectively.

[0024] The sowing date reverse calculation module inputs the first accumulated temperature supply and the second accumulated temperature supply into the sowing date reverse calculation model. The sowing date reverse calculation model uses the preset harvest deadline as the constraint boundary and iteratively solves the sowing date range that meets the accumulated temperature requirements of the vegetative growth stage and the reproductive growth stage.

[0025] The suitability evaluation module constructs a comprehensive index sequence for sowing suitability based on the daily soil moisture content sequence and the daily surface temperature sequence within the sowing date interval.

[0026] The sowing date optimization module selects the date corresponding to the maximum value of the comprehensive sowing suitability index from the sowing date range as the recommended sowing date.

[0027] In practice, the temperature reconstruction module extracts the daily maximum and minimum temperatures of the grid points containing the target plot from historical meteorological reanalysis data. The historical meteorological reanalysis data uses a daily dataset with a spatial resolution of 0.25°×0.25°, and the grid points containing the target plot are obtained by matching the latitude and longitude coordinates of the target plot's center. The extracted daily maximum temperature sequence is denoted as... The daily minimum temperature sequence is recorded as .

[0028] Trigonometric interpolation is used to convert daily maximum and minimum temperatures into a preliminary sequence of daily average temperatures. Trigonometric interpolation assumes that the daily temperature variation curve can be approximated as a sine wave with the minimum temperature as the baseline. The preliminary sequence of daily average temperatures includes the average temperature of each day. Calculated by the following formula: in, This represents the daily maximum temperature extracted from historical meteorological reanalysis data, expressed in degrees Celsius. This represents the daily minimum temperature extracted from historical meteorological reanalysis data, expressed in degrees Celsius. Pi, with a value of 3.1415926; coefficient This is the analytical result of integrating the standard sine function over the positive half-cycle to obtain the mean. A preliminary daily average temperature sequence is generated by iterating through all historical dates and applying trigonometric interpolation.

[0029] Using the measured daily average temperatures from multiple meteorological stations within a predetermined radius around the target site as calibration references, the preliminary daily average temperature sequence is corrected for errors. The predetermined radius is set to 50 kilometers based on the average spatial density of meteorological stations in the target site area. Measured daily average temperature data from all meteorological stations with continuous observation records within the predetermined radius are collected, and the arithmetic mean of the measured daily average temperatures from all meteorological stations on the same calendar date is calculated to form a multi-station average temperature sequence. The preliminary daily average temperature sequence and the multi-station average temperature sequence are aligned by date, and the difference between the preliminary daily average temperature sequence value and the multi-station average temperature sequence value for each date is calculated to obtain the daily deviation sequence. The daily deviation sequence is smoothed using a central moving average with a moving window length of 7 days to obtain a smoothed deviation sequence. The smoothed deviation sequence value for the corresponding date is subtracted from the daily value of the preliminary daily average temperature sequence to obtain the corrected daily average temperature sequence. When there is no valid measured data from surrounding meteorological stations on a certain date, the measured daily average temperature from the meteorological station closest to the target site is used instead of the multi-station average for deviation calculation.

[0030] In the temperature reconstruction module, the vegetative growth stage and reproductive growth stage throughout the entire growth period are divided based on the physiological development time parameters of maize varieties. These physiological development time parameters are obtained from the maize variety approval announcement or variety rights application documents. These parameters include the first cumulative light and heat hours from emergence to tassel differentiation, the second cumulative light and heat hours from tassel differentiation to silking, and the third cumulative light and heat hours from silking to physiological maturity. The first, second, and third cumulative light and heat hours are all expressed in units of light and heat time.

[0031] When dividing the vegetative growth stage into the reproductive growth stage, any date within the historical sowing period of the target plot is used as the simulation starting point. Daily light and heat duration is calculated daily using a corrected daily average temperature sequence and a daily sunshine duration sequence calculated from the latitude of the target plot. Daily light and heat duration is the product of daily effective temperature and photoperiod response factor. Daily effective temperature is the corrected daily average temperature minus the growth baseline temperature of the maize variety; when the corrected daily average temperature is less than or equal to the growth baseline temperature, the daily effective temperature is set to zero. The photoperiod response factor is set to 1 when the daily sunshine duration is greater than the critical day length of the maize variety, and the ratio of daily sunshine duration to the critical day length of the maize variety when the daily sunshine duration is less than or equal to the critical day length. Starting from the simulation starting point, daily light and heat duration is accumulated daily. When the accumulated value first reaches the first cumulative light and heat duration, the period from the simulation starting point to that date is marked as the vegetative growth stage. After the end date of the vegetative growth stage, the daily light and heat hours continue to be accumulated. When the cumulative value increases by the sum of the second and third cumulative light and heat hours for the first time, the period from the day after the end date of the vegetative growth stage to that date is marked as the reproductive growth stage.

[0032] In practice, the accumulated temperature calculation module retrieves the preset growth baseline temperature from the variety approval announcement. The variety approval announcement of the target maize variety records the baseline temperature parameter for the entire growth period, which is expressed in degrees Celsius. If the variety approval announcement of a target maize variety does not explicitly record the baseline temperature parameter for the entire growth period, then 10℃ is used as the preset growth baseline temperature. This 10℃ value is based on the general setting value of the lower limit temperature for calculating active accumulated temperature as specified in the "Technical Regulations for Regional Trials of Maize Varieties".

[0033] The accumulated temperature calculation module obtains the corrected daily average temperature sequence and the date ranges for the vegetative growth stage and the reproductive growth stage from the temperature reconstruction module. The start date of the vegetative growth stage corresponds to the seedling emergence date, and the end date corresponds to the date of completion of tassel differentiation; the start date of the reproductive growth stage is the day after the end date of the vegetative growth stage, and the end date corresponds to the date of physiological maturity.

[0034] For each day in the corrected daily average temperature sequence, if the corrected daily average temperature is higher than the preset fertility baseline temperature, the corrected daily average temperature is subtracted from the preset fertility baseline temperature to obtain the effective temperature for that day; if the corrected daily average temperature is less than or equal to the preset fertility baseline temperature, the effective temperature for that day is zero. The effective temperatures of each day during the vegetative growth stage are accumulated to obtain the accumulated temperature for the vegetative growth stage; the effective temperatures of each day during the reproductive growth stage are accumulated to obtain the accumulated temperature for the reproductive growth stage.

[0035] The first accumulated temperature supply is obtained by weighting and summing the active accumulated temperature sequence based on the first growth degree-day coefficient corresponding to the vegetative growth stage. The first growth degree-day coefficient is denoted as... First accumulated temperature supply The formula for calculation is: in, This refers to the sequence number of the date within the vegetative growth stage. Take from 1 to positive integers, This refers to the total number of days included in the vegetative growth stage. Indicates the first stage within the vegetative growth phase The corrected daily average temperature, in degrees Celsius; This indicates the preset fertility baseline temperature, in degrees Celsius. Indicates taking The maximum value between 0 and 0 is used as the first... The effective temperature of the day; The first growth degree daily coefficient is dimensionless.

[0036] The second accumulated temperature supply is obtained by weighting and summing the active accumulated temperature sequence based on the second growth degree-day coefficient corresponding to the reproductive growth stage. The second growth degree-day coefficient is denoted as... Second accumulated temperature supply The calculation formula is ,in This refers to the sequence number of the date within the reproductive growth stage. Take from 1 to positive integers, This refers to the total number of days included in the reproductive growth phase. Indicates the first stage of reproductive growth The corrected daily average temperature. This is the second growth rate daily coefficient, dimensionless.

[0037] The first and second growth degree-day coefficients were determined through accumulated temperature-development rate regression analysis using historical staggered sowing trial data of the target maize variety. The historical staggered sowing trial data came from staggered sowing trials conducted for the target maize variety in the target ecological zone for at least three consecutive years, with no fewer than five sowing periods each year. The trials recorded the emergence date, tassel differentiation completion date, silking date, and physiological maturity date for each sowing period treatment. The accumulated temperature of the vegetative growth stage and the accumulated temperature of the reproductive growth stage were calculated from the experimental data for each sowing period treatment. The accumulated temperature of the vegetative growth stage was the cumulative daily effective temperature from the emergence date to the tassel differentiation completion date, and the accumulated temperature of the reproductive growth stage was the cumulative daily effective temperature from the silking date to the physiological maturity date. The calculation method for the daily effective temperature was the same as described above. The development rate of the vegetative growth stage and the development rate of the reproductive growth stage were calculated separately for each sowing period treatment. The development rate was the quotient of 1 divided by the duration of the corresponding stage. Using the accumulated temperature during the vegetative growth stage as the independent variable and the developmental rate during the vegetative growth stage as the dependent variable, a univariate linear regression was performed using the least squares method. The regression equation is: developmental rate equals slope multiplied by accumulated temperature plus intercept. The slope obtained from the regression analysis was determined as the daily coefficient of the first growth degree. Using the accumulated temperature during the reproductive growth stage as the independent variable and the developmental rate during the reproductive growth stage as the dependent variable, a univariate linear regression was performed using the least squares method. The slope obtained from the regression analysis was determined as the second growth degree coefficient. If the coefficient of determination in the regression analysis is lower than 0.85, the number of years or sowing periods in the historical staggered sowing test data should be increased and the regression should be repeated until the coefficient of determination reaches 0.85 or higher.

[0038] See Figure 4 In the graph, the horizontal axis represents accumulated temperature, in degrees Celsius per day (°C·d), and the vertical axis represents developmental rate, in units of units per day (1 / d). Green dots represent scatter plots of accumulated temperature and developmental rate during the vegetative growth stage, while brown triangles represent scatter plots of accumulated temperature and developmental rate during the reproductive growth stage. The solid green line is the linear regression curve for the daily coefficient of the first growth degree during the vegetative growth stage, and the dashed brown line is the linear regression curve for the daily coefficient of the second growth degree during the reproductive growth stage.

[0039] The data points for the vegetative growth stage are distributed between approximately 260℃·d and 560℃·d of accumulated temperature, with the developmental rate increasing linearly from approximately 0.06 L / d to 0.12 L / d. The data points for the reproductive growth stage are distributed between approximately 530℃·d and 930℃·d of accumulated temperature, with the developmental rate increasing linearly from approximately 0.09 L / d to 0.14 L / d. While there is slight overlap between the data points for the two stages, the overall distribution is clear. The regression lines linearly fitted the relationship between the developmental rate and accumulated temperature for both growth stages, reflecting the regularity of the developmental rate increasing with increasing accumulated temperature.

[0040] In practice, the sowing date reverse calculation module determines the preset harvest deadline. This preset harvest deadline is determined by a safety buffer period of several days prior to the average first frost date in the target area. The average first frost date is obtained from meteorological observation records of the target area. Specifically, the dates on which the lowest daily temperature first fell below 0°C each autumn in the past thirty years within the administrative county where the target area is located are extracted, and the arithmetic mean of these dates is calculated. The date corresponding to this arithmetic mean is taken as the average first frost date. The safety buffer period is calculated jointly by the dehydration rate of the corn variety and the harvesting efficiency. The dehydration rate of the corn variety is obtained from the kernel dehydration rate parameter recorded in the variety approval announcement, expressed as a percentage per day. The harvesting efficiency is extracted from the annual statistical report of the agricultural mechanization management department in the target area. Specifically, the extracted value is the weighted average of the corn harvester's operating area per unit time in the township where the target area is located over the past three years, converted to mu per day. Safety buffer period. The calculation formula is: in, This indicates the field grain moisture content of a maize variety at physiological maturity, expressed as a percentage. It is obtained from the grain moisture content at maturity recorded in the variety approval announcement. If it is not recorded in the variety approval announcement, 30% is used as the default value. This default value is based on the biological lower limit of grain moisture content at physiological maturity of maize. This indicates the upper limit threshold of grain moisture content suitable for mechanical harvesting, expressed as a percentage. It is set to 25%, based on the maximum moisture content requirement for direct grain harvesting operations as specified in the "Technical Regulations for Mechanized Harvesting of Corn". This indicates the dehydration rate of corn varieties, expressed as a percentage per day. The area of ​​the target plot is expressed in mu (a Chinese unit of area, approximately 0.165 acres), and is obtained from the land ownership registration information of the target plot. This indicates harvesting efficiency, expressed in acres per day. The average date of the first frost is subtracted by the number of safety buffer days. The date obtained is recorded as the preset harvest deadline. .

[0041] The sowing date deduction module obtains the accumulated temperature requirement thresholds for the reproductive growth stage and the vegetative growth stage from the accumulated temperature calculation module. The accumulated temperature requirement threshold for the reproductive growth stage is denoted as... The accumulated temperature requirement threshold for the vegetative growth stage is denoted as... The sum of the accumulated temperature requirements for the vegetative growth stage and the reproductive growth stage is denoted as... The annual average daily effective temperature is calculated based on the corrected daily average temperature series. Average daily effective temperature The calculation method is as follows: After aligning all years of the corrected daily average temperature series by date, calculate the multi-year average temperature for each calendar date. Then, subtract the preset fertility baseline temperature from this multi-year average temperature. Accumulate the portions with differences greater than 0 and divide by the number of days in the year to obtain the annual average daily effective temperature. Utilizing the accumulated temperature requirement threshold during the reproductive growth stage. Divide by the average daily effective temperature Then, round the quotient up to obtain the number of days required for the reproductive growth stage. ; Utilizing the accumulated temperature requirement threshold during the vegetative growth stage Divide by the average daily effective temperature Then, round the quotient up to obtain the number of days required for the vegetative growth stage. .

[0042] The sowing date reverse calculation module uses a preset harvest deadline. Starting from the time point, the iterations decrease daily along the time axis. In each iteration, the current iteration date is denoted as... , The initial value is Each time, the value decreases by one day. The sowing date backwards module calculates the first accumulated temperature value: extracted from the date series... Backtracking The corrected daily average temperature for each day within the specified interval is calculated. For each day, the corrected daily average temperature is subtracted from the preset fertility baseline temperature. If the difference is greater than 0, it is retained; otherwise, it is set to zero. These daily effective values ​​are then summed to obtain the first accumulated temperature value. The sowing date reverse calculation module calculates the second accumulated temperature value: extracted from... Backtracking The corrected daily average temperature for each day within the daily interval is calculated in the same way, and the daily effective temperature is accumulated to obtain the second accumulated temperature value.

[0043] When, in a certain iteration, the first accumulated temperature value is greater than or equal to the accumulated temperature requirement threshold for the reproductive growth stage, Furthermore, the second accumulated temperature value under the same iteration is greater than or equal to the sum of the accumulated temperature requirement thresholds of the vegetative growth stage and the reproductive growth stage for the first time. At that time, the broadcast period reverse calculation module will determine the current iteration date. Marked as the sowing deadline After marking the sowing deadline, the sowing date backwards module continues from the current iteration date. The process iterates forward day by day, continuously calculating the first and second accumulated temperature values, until in a certain iteration the first accumulated temperature value falls below the accumulated temperature requirement threshold for the reproductive growth stage. Alternatively, the second accumulated temperature value may be less than the sum of the accumulated temperature thresholds for the vegetative growth stage and the reproductive growth stage. Then mark the date of the previous iteration as the sowing start date. Based on the start date of sowing With the sowing deadline This constitutes the sowing date range.

[0044] In specific implementation, please refer to Figure 2 Before constructing the comprehensive sowing suitability index sequence, the suitability evaluation module obtains the daily soil moisture content sequence within the sowing date interval. Using the center latitude and longitude coordinates of the target plot as a reference, soil moisture monitoring stations covering the target plot are located from the soil moisture monitoring network. The soil moisture monitoring network is an automatic soil moisture observation network deployed by the provincial or municipal agricultural meteorological operational system. Each observation station provides soil volumetric water content at multiple depths at fixed times each day. These depths include 10 cm, 20 cm, 30 cm, 40 cm, and 50 cm depths below the surface. The soil volumetric water content data for each depth is expressed in cubic meters per cubic meter.

[0045] When extracting daily soil volumetric water content at multiple depths within the sowing date interval from the soil moisture monitoring network, the process first retrieves all available soil moisture monitoring stations within a 15-kilometer radius centered on the target plot. The 15-kilometer search radius is chosen because the spatial autocorrelation distance of soil moisture is typically between 10 and 20 kilometers, ensuring that at least two observation stations are found. For each available soil moisture monitoring station, the soil volumetric water content records at each depth are extracted for each day within the sowing date interval. If a station lacks a record at a specific depth on a particular day, the average of the valid records from the day before and after that station at the same depth is used to fill the gap; if there are no valid records from either day before or after, the station is removed from the fusion calculation for that day.

[0046] The inverse distance weighted interpolation method was used to integrate the soil volumetric water content of multiple depth layers into a daily soil moisture content sequence. For each date within the sowing date interval, the soil volumetric water content of each depth layer on the same date was first extracted from all available soil moisture monitoring stations. For each available soil moisture monitoring station, the soil volumetric water content of each depth layer was weighted and summed according to the weight coefficient of each depth layer to obtain the comprehensive soil volumetric water content of a station. The weight coefficients of the five depth layers were set to 0.35, 0.30, 0.20, 0.10 and 0.05 respectively from the 10 cm depth layer to the 50 cm depth layer. The weight coefficients were set based on the fact that the corn sowing layer is mainly concentrated from the surface to a depth of 20 cm, the sowing depth is usually 3 cm to 5 cm, and the 10 cm depth layer is closest to the sowing zone. The weight coefficients decreased with increasing depth. Then, the inverse distance weight was calculated based on the spatial distance between each available soil moisture monitoring station and the center of the target plot. The calculation formula is ,in This represents the serial number of the available soil moisture monitoring stations. Take from 1 to positive integers, This represents the total number of available soil moisture monitoring stations; Indicates the first The spatial distance between available soil moisture monitoring stations and the center of the target plot, in kilometers; Indicates the first The spatial distance between each available soil moisture monitoring station and the center of the target plot is expressed in kilometers. The exponent 2 is the power parameter for the inverse distance weighting; a power parameter of 2 indicates that the weighting is based on the inverse proportion of the square of the Euclidean distance. The comprehensive soil volumetric water content of each available soil moisture monitoring station is weighted and summed according to the inverse distance weighting to obtain the daily soil moisture content value of the target plot on that date. This process is repeated for all dates within the sowing date interval to form a daily soil moisture content sequence. The unit for each value in the daily soil moisture content sequence is cubic meters per cubic meter.

[0047] In practical implementation, the suitability assessment module obtains daily surface temperature sequences from automatic weather station data. The automatic weather station data originates from regional automatic weather stations located in or near the target site's township, and the observed elements include hourly surface temperature. From the target site's automatic weather station data, 24 hourly surface temperature observations are extracted for each day within the sowing date interval. The arithmetic mean of these 24 hourly surface temperature observations is calculated to obtain the daily average surface temperature. If the daily average surface temperature for a particular day is missing due to instrument malfunction or data transmission interruption, cubic spline interpolation is used to fill in the missing date data. The cubic spline interpolation method uses the effective daily average surface temperature of the two days before and after the missing date as interpolation nodes, constructs a cubic spline function, and calculates the interpolated result of the daily average surface temperature for the missing date using the cubic spline function. If the missing date is located at the beginning or end of the sowing date interval, and there are less than two days before or after it, the number of days with valid data is used as the interpolation node, and linear extrapolation is used to calculate the missing value. After cubic spline interpolation, a complete daily land surface temperature series is obtained. The unit of each value in the daily land surface temperature series is degrees Celsius.

[0048] After obtaining the daily soil moisture content sequence and daily surface temperature sequence, the suitability evaluation module constructs a comprehensive sowing suitability index sequence. The soil moisture suitability component is obtained by dividing the daily soil moisture content sequence for each day within the sowing date range by a preset upper limit for suitable sowing moisture content. The preset upper limit for suitable sowing moisture content is set to 0.30 cubic meters per cubic meter. This setting is based on the upper limit of soil moisture content during the corn sowing period specified in the "Corn Sowing Technical Specifications." Exceeding this upper limit results in excessively wet soil, preventing the sowing equipment from operating normally. The upper limit of the soil moisture suitability component's value range is 1. When the value of a certain day in the daily soil moisture content sequence is greater than or equal to 0.30 cubic meters per cubic meter, the soil moisture suitability component is set to 1.

[0049] The soil temperature suitability component is obtained by subtracting the preset lower limit of the suitable sowing temperature from the daily surface temperature sequence within the sowing date range, and then dividing by the preset suitable sowing temperature range. The preset lower limit of the suitable sowing temperature is set at 8℃, and the preset suitable sowing temperature range is set at 12℃. The preset lower limit of the suitable sowing temperature is set at 8℃ because the minimum soil temperature required for maize seed germination, as specified in the "Maize Sowing Technical Specifications," is 8℃ to 10℃; 8℃ is taken as a conservative lower limit. The preset suitable sowing temperature range is set at 12℃ because the upper limit of the suitable soil temperature during the maize sowing period is usually 20℃, and the suitable temperature range is obtained by subtracting the lower limit of 8℃ from the upper limit of 20℃. The upper limit of the temperature suitability component is 1, and there is no limit at the lower end. When the value of a certain day in the daily surface temperature sequence is less than or equal to the preset lower limit of the suitable temperature for sowing (8℃), the numerator is negative and the temperature suitability component is negative. When the value of a certain day in the daily surface temperature sequence is greater than or equal to the preset upper limit of the suitable temperature for sowing (20℃), the temperature suitability component takes a value greater than or equal to 1, and at this time the temperature suitability component is truncated to 1.

[0050] The comprehensive sowing suitability index for a given day is obtained by multiplying the soil moisture suitability component and the soil temperature suitability component. Both components are dimensionless, and the product is also dimensionless. The range of values ​​depends on the individual values ​​of the two components, with a maximum value of 1 indicating optimal sowing conditions and a minimum value that can be negative, indicating unsuitable sowing conditions. By iterating through all dates within the sowing date interval, the soil moisture suitability component, the soil temperature suitability component, and their product are calculated daily, forming a sequence of comprehensive sowing suitability indices that corresponds one-to-one with each date within the sowing date interval.

[0051] In specific implementation, please refer to Figure 3 The sowing date selection module obtains the sowing suitability comprehensive index sequence from the suitability evaluation module. The sowing suitability comprehensive index sequence contains each date within the sowing date range and the corresponding sowing suitability comprehensive index value, and the sowing suitability comprehensive index sequence is arranged in ascending order of date.

[0052] The sowing date optimization module performs a monotonicity test on the sowing suitability comprehensive index sequence. The monotonicity test is performed as follows: starting from the second date in the sowing suitability comprehensive index sequence, each date is sequentially taken as the current date, up to the second-to-last date. For each current date, the sowing suitability comprehensive index value for that date is obtained, along with the sowing suitability comprehensive index values ​​for the preceding and following dates adjacent to the current date. The sowing suitability comprehensive index value for the current date is denoted as... The comprehensive index value of sowing suitability for the previous adjacent date is denoted as The comprehensive index value of sowing suitability for the next adjacent date is denoted as .Compare and , Size relationship: If Greater than and Greater than If the current date is a local maximum, it is added to the candidate date set. After completing the monotonicity test for all dates within the sowing suitability comprehensive index sequence, the first and last dates of the sowing suitability comprehensive index sequence are tested separately. For the first date, the sowing suitability comprehensive index value for the first date is obtained. The comprehensive index value of sowing suitability on the second date. ,like Greater than If the first date is determined to be a local maximum, it is added to the candidate date set. For the last date, the comprehensive index value of the sowing suitability for that date is obtained. The combined index value of sowing suitability for the second-to-last date. ,like Greater than If the last date is determined to be a local maximum point, it will be added to the candidate date set.

[0053] After forming a candidate date set, the sowing date optimization module extracts the comprehensive sowing suitability index value corresponding to each candidate date from the set. It then compares the values ​​of all candidate dates to find the maximum value. The candidate date with a comprehensive sowing suitability index value equal to this maximum value is designated as the date corresponding to the maximum sowing suitability index. If multiple candidate dates have the same maximum sowing suitability index value, the earliest of these candidate dates is selected as the date corresponding to the maximum sowing suitability index. The rationale for choosing the earliest date is that, under the same sowing suitability conditions, earlier sowing provides corn with a more abundant full growth period. The sowing date optimization module uses the date corresponding to the determined maximum sowing suitability index as the recommended sowing date and outputs it.

[0054] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A maize planting decision-making system based on accumulated temperature matching throughout the entire growth period, characterized in that, include: The temperature reconstruction module is used to reconstruct the daily average temperature sequence based on the historical meteorological reanalysis data of the target plot, and to divide the vegetative growth stage and reproductive growth stage within the entire growth period based on the physiological development time parameters of the maize variety. The accumulated temperature calculation module is used to calculate the first accumulated temperature supply of the daily average temperature sequence in the vegetative growth stage and the second accumulated temperature supply in the reproductive growth stage, respectively, based on the accumulated temperature demand thresholds of the vegetative growth stage and the reproductive growth stage. The sowing date reverse calculation module is used to input the first accumulated temperature supply and the second accumulated temperature supply into the sowing date reverse calculation model. The sowing date reverse calculation model uses a preset harvest deadline as a constraint boundary and iteratively solves the sowing date range that satisfies the accumulated temperature requirements of the vegetative growth stage and the reproductive growth stage. The suitability evaluation module is used to construct a comprehensive sowing suitability index sequence based on the daily soil moisture content sequence and daily surface temperature sequence within the sowing date interval. The sowing date optimization module is used to select the date corresponding to the maximum value of the comprehensive sowing suitability index from the sowing date range as the recommended sowing date.

2. The maize stable yield guarantee planting decision system based on accumulated temperature matching throughout the entire growth period as described in claim 1, characterized in that, The process of reconstructing the daily average temperature series based on historical meteorological reanalysis data of the target site includes: The daily maximum and minimum temperatures of the grid points where the target plot is located are extracted from the historical meteorological reanalysis data, and the daily maximum and minimum temperatures are converted into a preliminary daily average temperature sequence using trigonometric interpolation. Using the measured daily average temperature of multiple meteorological stations within a preset radius around the target plot as a calibration reference, the preliminary daily average temperature sequence is corrected for errors to obtain the corrected daily average temperature sequence.

3. The maize stable yield guarantee planting decision system based on accumulated temperature matching throughout the entire growth period as described in claim 2, characterized in that, The physiological development time parameters based on maize varieties are used to divide the entire growth period into vegetative growth stages and reproductive growth stages, including: Obtain physiological development time parameters of maize varieties, including the first cumulative light and heat hours from emergence to tassel differentiation, the second cumulative light and heat hours from tassel differentiation to silking, and the third cumulative light and heat hours from silking to physiological maturity. The time period corresponding to the first cumulative light and heat hours is divided into the vegetative growth stage, and the time period corresponding to the sum of the second cumulative light and heat hours and the third cumulative light and heat hours is divided into the reproductive growth stage.

4. The maize stable yield guarantee planting decision system based on accumulated temperature matching throughout the entire growth period as described in claim 3, characterized in that, The step of calculating the first accumulated temperature supply of the daily average temperature sequence in the vegetative growth stage and the second accumulated temperature supply in the reproductive growth stage, based on the accumulated temperature demand thresholds for the respective vegetative growth stage and reproductive growth stage, includes: Using the preset fertility baseline temperature as the lower limit for accumulated temperature calculation, the daily effective temperatures in the corrected daily average temperature sequence that are higher than the fertility baseline temperature are accumulated to obtain the active accumulated temperature sequence. The active accumulated temperature sequence is weighted and accumulated based on the first growth degree-day coefficient corresponding to the vegetative growth stage to obtain the first accumulated temperature supply, and the active accumulated temperature sequence is weighted and accumulated based on the second growth degree-day coefficient corresponding to the reproductive growth stage to obtain the second accumulated temperature supply.

5. The maize stable yield guarantee planting decision system based on accumulated temperature matching throughout the entire growth period according to claim 4, characterized in that, The step of inputting the first accumulated temperature supply and the second accumulated temperature supply into the sowing date reverse calculation model, wherein the sowing date reverse calculation model uses a preset harvest deadline as a constraint boundary, and iteratively solves for the sowing date interval that satisfies the accumulated temperature requirements of the vegetative growth stage and the reproductive growth stage, including: Starting from the preset harvest deadline, the iteration proceeds forward along the time axis, decreasing day by day. The first accumulated temperature value for the number of days required to backtrack from the current iteration point to the reproductive growth stage is calculated, as well as the second accumulated temperature value for the sum of the number of days required to backtrack from the vegetative growth stage and the reproductive growth stage. When the first accumulated temperature value first reaches the accumulated temperature requirement threshold of the reproductive growth stage and the second accumulated temperature value first reaches the sum of the accumulated temperature requirement thresholds of the vegetative growth stage and the reproductive growth stage, the date corresponding to the current iteration point is marked as the sowing deadline, and the iteration continues forward until the date when the first accumulated temperature value or the second accumulated temperature value is lower than the corresponding threshold is marked as the sowing start date, and the sowing start date and the sowing deadline constitute the sowing date interval.

6. The maize stable yield guarantee planting decision system based on accumulated temperature matching throughout the entire growth period as described in claim 5, characterized in that, The step of constructing a comprehensive sowing suitability index sequence based on the daily soil moisture content sequence and daily surface temperature sequence within the sowing date interval includes: Divide the daily soil moisture content sequence for each day within the sowing date interval by the preset upper limit of suitable sowing moisture content to obtain the soil moisture suitability component. Subtract the preset lower limit of the suitable temperature for sowing from the daily surface temperature sequence of each day within the sowing date interval, and then divide by the preset suitable temperature range for sowing to obtain the soil temperature suitability component. Multiply the soil moisture suitability component and the soil temperature suitability component for the same day to obtain the comprehensive sowing suitability index for that day. By iterating through all dates within the sowing date interval, a comprehensive sowing suitability index sequence is formed.

7. The maize stable yield guarantee planting decision system based on accumulated temperature matching throughout the entire growth period as described in claim 6, characterized in that, The step of selecting the date corresponding to the maximum value of the comprehensive sowing suitability index from the sowing date range as the recommended sowing date includes: The monotonicity test is performed on the comprehensive sowing suitability index sequence, and candidate dates corresponding to all local maxima in the comprehensive sowing suitability index sequence are extracted; The candidate date with the highest comprehensive sowing suitability index among the candidate dates is determined as the date corresponding to the maximum value of the comprehensive sowing suitability index, and this date is output as the recommended sowing date.

8. The maize stable yield guarantee planting decision system based on accumulated temperature matching throughout the entire growth period according to claim 7, characterized in that, Before constructing the comprehensive sowing suitability index sequence based on the daily soil moisture content sequence and daily surface temperature sequence within the sowing date interval, the method further includes: Centered on the target plot, the daily soil volumetric water content of multiple depth layers within the sowing date interval is extracted from the soil moisture monitoring network, and the inverse distance weighted interpolation method is used to integrate the soil volumetric water content of multiple depth layers into the daily soil moisture content sequence. The daily average surface temperature within the sowing date interval is extracted from the automatic weather station data of the target plot, and the missing date data is filled in using cubic spline interpolation to obtain the daily surface temperature sequence.

9. A maize planting decision-making system based on accumulated temperature matching throughout the entire growth period, as described in claim 5, is characterized in that... The preset harvest deadline is determined by a safety buffer number of days in advance based on the average first frost date of the target plot area. The safety buffer number is calculated by combining the dehydration rate of the corn variety and the harvesting efficiency.

10. A maize planting decision-making system based on accumulated temperature matching throughout the entire growth period, as described in claim 4, is characterized in that... The preset growth baseline temperature is obtained by querying the full growth period baseline temperature parameters recorded in the variety approval announcement of the target maize variety. The first growth degree day coefficient and the second growth degree day coefficient are determined by accumulated temperature-development rate regression analysis on the historical stage planting test data of the target maize variety.

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