Paddy field ammonia volatilization amount calculation method
By screening key influencing factors and constructing segmented scenarios and multi-factor coupling models, the complexity and insufficient accuracy of ammonia volatilization calculation in paddy fields have been solved, achieving high-precision dynamic measurement of ammonia volatilization, which is suitable for precision agricultural management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-14
- Publication Date
- 2026-03-13
AI Technical Summary
Existing methods for calculating ammonia volatilization in paddy fields are complex to operate, have poor adaptability, and lack sufficient accuracy. They do not fully consider the combined effects of multiple factors, stage differences, and lag effects, making it difficult to meet the needs of precision agricultural management.
By screening key influencing factors, constructing segmented scenarios, establishing a multi-factor coupled total ammonia volatilization model, and constructing a lag distribution model, high-precision calculation of ammonia volatilization for each time period can be achieved.
It significantly improves the accuracy of ammonia volatilization measurement, and is especially suitable for short-term dynamic measurement, meeting the needs of precision agricultural management.
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Abstract
Description
Technical Field
[0001] This invention belongs to the field of agricultural environment and ecological technology, and specifically relates to a method for calculating ammonia volatilization in paddy fields. Background Technology
[0002] Ammonia volatilization is one of the main pathways of nitrogen loss in paddy fields. Accurately measuring the amount of ammonia volatilization in paddy fields is of great significance for optimizing nitrogen fertilizer application schemes, reducing environmental pollution, and improving nitrogen use efficiency.
[0003] Currently, the main methods for calculating ammonia volatilization in paddy fields fall into two categories: direct observation and model estimation. Direct observation involves collecting and quantifying volatilized ammonia using a closed device set up in the field. However, this method is complex, time-consuming, and labor-intensive, and is greatly affected by environmental conditions, making it difficult to achieve large-scale, long-term ammonia volatilization measurement.
[0004] Model estimation methods calculate ammonia volatilization by constructing mathematical models that correlate key factors. Existing models often focus on single or a few factors such as soil pH, temperature, and nitrogen fertilizer application, neglecting the combined effects of factors like paddy field water management patterns (such as flooding, drying, and alternating shallow and deep water layers), soil texture, and crop growth stage. Furthermore, they fail to consider the phased differences and lag effects of each factor's impact on ammonia volatilization. This results in poor adaptability of existing models under different paddy field ecological conditions, insufficient accuracy in calculating ammonia volatilization over short periods (such as daily), and difficulty in meeting the practical needs of precision agriculture management. Summary of the Invention
[0005] The technical problem to be solved by this invention is to overcome the problems of existing methods for calculating ammonia volatilization in paddy fields, such as complex operation, poor adaptability, insufficient calculation accuracy, and failure to fully consider the combined effects of multiple factors, stage differences, and lag effects. This invention provides a scientific, reasonable, accurate, and widely applicable method for calculating ammonia volatilization in paddy fields.
[0006] The technical solution of the present invention is as follows: A method for calculating ammonia volatilization in paddy fields includes the following steps: Step 1, Screening of Influencing Factors and Construction of Segmented Scenarios: Screen typical monitoring events of ammonia volatilization in paddy fields, select key influencing factors, including nitrogen fertilizer application rate, soil pH value, field water depth, soil temperature, and crop growth period, use clustering algorithm to analyze the comprehensive impact of each factor on ammonia volatilization, determine the segmentation threshold of each factor, and construct different segmented scenarios by combining them in pairs. The aim is to systematize and modularize the complex field environment. Data quality is ensured by screening typical monitoring events; nitrogen fertilizer application rate, soil pH, field water depth, soil temperature, and crop growth stage are selected as key influencing factors; clustering algorithms (such as K-means) are used to objectively determine the segment thresholds for each factor (e.g., nitrogen fertilizer application rate of 30 kg / mu, soil pH of 6.5, etc.); finally, by combining the states of these factors in pairs under different segments, multiple discrete and representative "segmented scenarios" are constructed, laying the foundation for subsequent model parameter differentiation.
[0007] Step 2, Construction of the total ammonia volatilization model: Establish a basic model of total ammonia volatilization under single-factor dominance, with nitrogen fertilizer application rate as the core variable, introduce soil pH and temperature correction coefficients, combine water management mode and the dynamic influence of crop growth period to correct the basic model parameters, and construct a multi-factor coupled model of total ammonia volatilization in paddy fields. A comprehensive model for estimating total ammonia volatilization that reflects the synergistic effects of multiple factors is established. First, a basic model (such as an exponential growth model) driven solely by nitrogen fertilizer application rate is established, defining the theoretical framework and limits for total ammonia volatilization. Then, correction coefficients for soil pH, temperature, water depth, and growth period are introduced to dynamically adjust the parameters of the basic model (such as limits and growth rates), thus forming a comprehensive model coupled with multiple factors, enabling the model to flexibly respond to different field environments and management practices.
[0008] Step 3, construction of the lag allocation model: Based on the time lag characteristics of the ammonia volatilization process, the calculation time step is determined, and a lag allocation weight function for the total ammonia volatilization is constructed. This function includes the dynamic response coefficients of each influencing factor. Based on the principle of superposition, a lag allocation model is constructed to allocate the total ammonia volatilization in each time period to the corresponding time step according to the weight, so as to obtain the ammonia volatilization amount for each time period. The calculated total ammonia volatilization is rationally allocated to each calculation time step (e.g., daily scale) to capture the dynamic changes in the ammonia volatilization process. This step is based on the physical fact that ammonia volatilization is not instantaneous but has a time lag, and a weighting function incorporating dynamic response coefficients such as soil temperature and pH is constructed. Then, based on the principle of superposition, the volatilization generated by each fertilization event on each subsequent day is superimposed to finally calculate the daily ammonia volatilization, thereby achieving high-precision simulation over short time periods (daily).
[0009] Step 4, Model parameter calibration: Select monitoring data of a single nitrogen fertilizer application event without other interfering factors, calibrate the parameters of the lag allocation weight function, substitute the calibrated lag allocation parameters, and calibrate the parameters of the total ammonia volatilization model using multi-scenario monitoring data according to the constructed segmented scenarios, and determine the optimal parameter combination under each segmented scenario. Determine the optimal values of the unknown parameters in the model to ensure its accuracy and reliability. This step adopts a step-by-step calibration strategy: First, calibrate the parameters of the lag assignment model using clean, single-fertilization event data; then, substitute the calibrated lag assignment parameters into a complete dataset covering multiple scenarios to calibrate an optimal set of parameter combinations for the aggregate model constructed in step 2 under different "segmented scenarios".
[0010] Step 5, Ammonia Volatilization Calculation: Input the actual monitoring data of the target paddy field, match the corresponding segmented scenarios, call the calibrated total amount model and lag distribution model, and calculate the time-period and total ammonia volatilization of the target paddy field.
[0011] The established and calibrated model system is applied to actual prediction. By inputting real-time or planned data of the target paddy field, the system automatically matches the closest "segmented scenario" and calls the corresponding calibrated model parameters under that scenario to run the total amount model and the lagged allocation model respectively, and finally outputs the ammonia volatilization and total ammonia volatilization of the paddy field for each time period (such as daily).
[0012] Furthermore, in step 1, the key influencing factor also includes soil organic matter content, to more comprehensively reflect the potential impact of soil fertility on the ammonia volatilization process.
[0013] Furthermore, in step 1, the clustering algorithm is the K-means clustering algorithm. This algorithm can efficiently extract objective and reliable segmentation thresholds from the measured data.
[0014] Furthermore, in step 1, the segmented thresholds include: nitrogen fertilizer application rate of 30 kg / mu, soil pH value of 6.5, field water depth of 5 cm, and soil temperature of 25℃.
[0015] Furthermore, in step 2, the basic model for total ammonia volatilization under the single-factor dominance is as follows:
[0016] Among them, V base V represents the basic total amount of ammonia volatilization, in kg / mu. max is the ammonia volatilization limit, kg / mu; k is the ammonia volatilization growth rate coefficient; P is the nitrogen fertilizer application rate, kg / mu.
[0017] Further, in step 2, the multi-factor coupled total ammonia volatilization model is as follows:
[0018] in, This is the soil pH correction factor, which is positively correlated with soil pH. This is a temperature correction factor, which is positively correlated with soil temperature; This is a correction factor for water depth, which first increases and then decreases as water depth increases; This is the fertility period correction factor, which ranges from 0.8 to 1.5 depending on the fertility period.
[0019] Further, in step 3, the lag allocation weight function is:
[0020] in, The weights of ammonia volatilization generated by nitrogen fertilizer application on day i to day t are: T(i) is the soil temperature on day i, °C; pH is the soil pH value on day i; a and b are weighting coefficients.
[0021] This function can effectively simulate the change in evaporation rate over time, which first increases and then decreases.
[0022] Furthermore, in step 3, the ammonia volatilization amount on day t is:
[0023] Where v(t) is the ammonia volatilization on day t, kg / mu; V base (i) represents the total ammonia volatilization on day i, in kg / mu.
[0024] The daily quantity is calculated using the v(t) formula. Through parameter calibration, optimal values for the weighting coefficients a and b can be obtained. a The value of b is 0.03, and the value of b is 0.15.
[0025] The beneficial effects of this invention are as follows: The calculation method proposed in this invention comprehensively considers the combined effects of multiple factors such as nitrogen fertilizer application, soil characteristics, water management, temperature and crop growth period. It reflects the stage differences in the influence of each factor by constructing segmented scenarios, and captures the time lag effect of ammonia volatilization using a lag distribution model, which significantly improves the accuracy of ammonia volatilization measurement, and is especially suitable for short-term dynamic measurement. Detailed Implementation
[0026] The specific embodiments of the present invention will be further described in detail below with reference to examples. These examples are for illustrative purposes only and are not intended to limit the scope of the invention.
[0027] This embodiment utilizes measured data from a paddy field experimental base in the middle and lower reaches of the Yangtze River to verify the feasibility and effectiveness of the method and apparatus of the present invention. The soil type of the experimental base is stagnant paddy soil, and the planted variety is conventional japonica rice. The experimental period is the rice growing season (May-October) in 2022 and 2023. The monitoring indicators include nitrogen fertilizer application rate, soil pH value (0-20cm soil layer), field water depth, and soil temperature (0-10cm soil layer). At the same time, the ammonia volatilization is measured using a closed static chamber method, and the monitoring frequency is once a day.
[0028] The specific calculation steps are as follows: Step 1: Screening of influencing factors and construction of segmented scenarios. 120 monitoring events of ammonia volatilization in paddy fields that meet the conditions were screened. After removing interference events such as rainfall and water exchange, 86 valid events were retained.
[0029] Nitrogen fertilizer application rate (P), soil pH value (pH), field water depth (W), soil temperature (T), and crop growth stage (G, divided into tillering stage, jointing stage, booting stage, and grain filling stage) were selected as key influencing factors. The segment thresholds for each factor were determined using the K-means clustering algorithm: nitrogen fertilizer application rate 30 kg / mu, soil pH value 6.5, field water depth 5 cm, and soil temperature 25℃.
[0030] By combining pairs of scenarios to construct segmented scenarios, a total of 32 scenario combinations were obtained (4 growth stages × 2 nitrogen fertilizer levels × 2 pH levels × 2 water depth levels × 2 temperature levels).
[0031] Step 2, Construction of the total ammonia volatilization model. The basic model for total ammonia volatilization under single-factor dominance is as follows:
[0032] Among them, V base V represents the basic total amount of ammonia volatilization, in kg / mu. max is the ammonia volatilization limit, kg / mu; k is the ammonia volatilization growth rate coefficient; P is the nitrogen fertilizer application rate, kg / mu.
[0033] By introducing correction factors for soil pH, temperature, water depth, and growth period, a multi-factor coupled total ammonia volatilization model is constructed:
[0034] in, This is the soil pH correction factor, which is positively correlated with soil pH. This is a temperature correction factor, which is positively correlated with soil temperature; This is a correction factor for water depth, which first increases and then decreases as water depth increases; This is the fertility period correction factor, which ranges from 0.8 to 1.5 depending on the fertility period.
[0035] Step 3, constructing the lagged allocation model: using daily as the time step, construct the lagged allocation weight function:
[0036] in, The weights of ammonia volatilization generated by nitrogen fertilizer application on day i are assigned to day t; T(i) is the soil temperature on day i, °C; pH(i) is the soil pH value on day i; a and b are weighting coefficients.
[0037] Based on the principle of superposition, the ammonia volatilization amount on day t is:
[0038] Where v(t) is the ammonia volatilization on day t, kg / mu; V base (i) represents the total ammonia volatilization on day i, in kg / mu.
[0039] Step 4: Model parameter calibration. Twenty monitoring events with single nitrogen fertilizer application and no interference were selected. The parameters a and b of the lag allocation weight function were calibrated using the least squares method. The goodness of fit between the simulated and measured values of the weight function was 0.78.
[0040] Based on segmented scenarios of 86 valid monitoring events, the parameters of the total quantity model were calibrated for each scenario. The optimal solution, with some scenario parameters, is shown in the table below:
[0041] Step 5: Calculation and verification of ammonia volatilization. Input the actual monitoring data of rice growth period in the experimental base in 2022, and use the method of this invention to calculate the daily ammonia volatilization. The verification results show that the goodness of fit between the measured and simulated values of ammonia volatilization is 0.94, the average absolute error is 0.08 kg / mu, and the root mean square error is 0.12 kg / mu.
[0042] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A method for calculating ammonia volatilization in paddy fields, characterized in that, Includes the following steps: Step 1, Screening of Influencing Factors and Construction of Segmented Scenarios: Screen typical monitoring events of ammonia volatilization in paddy fields, select key influencing factors, including nitrogen fertilizer application rate, soil pH value, field water depth, soil temperature, and crop growth period, use clustering algorithm to analyze the comprehensive impact of each factor on ammonia volatilization, determine the segmentation threshold of each factor, and construct different segmented scenarios by combining them in pairs. Step 2, Construction of the total ammonia volatilization model: Establish a basic model of total ammonia volatilization under single-factor dominance, with nitrogen fertilizer application rate as the core variable, introduce soil pH and temperature correction coefficients, combine water management mode and the dynamic influence of crop growth period to correct the basic model parameters, and construct a multi-factor coupled model of total ammonia volatilization in paddy fields. Step 3, construction of the lag allocation model: Based on the time lag characteristics of the ammonia volatilization process, the calculation time step is determined, and a lag allocation weight function for the total ammonia volatilization is constructed. This function includes the dynamic response coefficients of each influencing factor. Based on the principle of superposition, a lag allocation model is constructed to allocate the total ammonia volatilization in each time period to the corresponding time step according to the weight, so as to obtain the ammonia volatilization amount for each time period. Step 4, Model parameter calibration: Select monitoring data of a single nitrogen fertilizer application event without other interfering factors, calibrate the parameters of the lag allocation weight function, substitute the calibrated lag allocation parameters, and calibrate the parameters of the total ammonia volatilization model using multi-scenario monitoring data according to the constructed segmented scenarios, and determine the optimal parameter combination under each segmented scenario. Step 5, Ammonia Volatilization Calculation: Input the actual monitoring data of the target paddy field, match the corresponding segmented scenarios, call the calibrated total amount model and lag distribution model, and calculate the time-period and total ammonia volatilization of the target paddy field.
2. The method for calculating ammonia volatilization in paddy fields according to claim 1, characterized in that, In step 1, the key influencing factors also include soil organic matter content.
3. The method for calculating ammonia volatilization in paddy fields according to claim 1, characterized in that, In step 1, the clustering algorithm is the K-means clustering algorithm.
4. The method for calculating ammonia volatilization in paddy fields according to claim 1, characterized in that, In step 1, the segmented thresholds include: nitrogen fertilizer application rate of 30 kg / mu, soil pH value of 6.5, field water depth of 5 cm, and soil temperature of 25℃.
5. The method for calculating ammonia volatilization in paddy fields according to claim 1, characterized in that, In step 2, the basic model for total ammonia volatilization under the single-factor dominance is as follows: ； in, This represents the basic total amount of ammonia volatilization, expressed in kg / mu. This represents the ammonia volatilization limit, expressed in kg / acre. k This is the coefficient for the growth rate of ammonia volatilization; P This refers to the amount of nitrogen fertilizer applied, in kg / mu.
6. The method for calculating ammonia volatilization in paddy fields according to claim 5, characterized in that, In step 2, the multi-factor coupled total ammonia volatilization model is as follows: ; in, This is the soil pH correction factor, which is positively correlated with soil pH. This is a temperature correction factor, which is positively correlated with soil temperature; This is a correction factor for water depth, which first increases and then decreases as water depth increases; This is the fertility period correction factor, which ranges from 0.8 to 1.5 depending on the fertility period.
7. The method for calculating ammonia volatilization in paddy fields according to claim 1, characterized in that, In step 3, the lag allocation weight function is: ; in, The weight of ammonia volatilization generated by nitrogen fertilizer application on day i is assigned to day t; T(i) is the soil temperature on day i, °C; pH(i) is the soil pH value on day i; a and b are weighting coefficients.
8. The method for calculating ammonia volatilization in paddy fields according to claim 7, characterized in that, In step 3, the ammonia volatilization on day t is: ; Where v(t) is the ammonia volatilization amount on day t, kg / mu; The total ammonia volatilization on day i is expressed in kg / mu.
9. The method for calculating ammonia volatilization in paddy fields according to claim 7 or 8, characterized in that, In step 4, the weighting coefficient a The value of b is 0.03, and the value of b is 0.15.