Extreme air temperature adaptability farming system optimization method based on carbon flux response

Abstract

The invention relates to a carbon flux response-based extreme temperature adaptability farming system optimization method, which comprises the following steps of: establishing a carbon flux continuous observation system in a target farmland, and acquiring data; processing the collected data through a data quality control process, and constructing a comprehensive database; based on the comprehensive database, identifying an extreme temperature event of the target farmland and a corresponding carbon flux response rule, and obtaining the influence of different farming management measures on the farmland through a multi-factor field contrast test; constructing a multi-objective optimization function and multiple constraint conditions thereof; an intelligent optimization algorithm is adopted to solve the multi-objective optimization function, an optimal cultivation system is obtained, and the optimal cultivation system comprises an optimal cultivation processing mode, a planting system, a land surface covering mode and a water and fertilizer management mode under different climate scenes and production conditions. Compared with the prior art, the method has the advantages that adaptive cultivation system optimization is realized, and the toughness of a farmland ecosystem to cope with extreme climate events is improved.

Description

Technical Field

[0001] This invention relates to the fields of agricultural ecology and climate change response, and in particular to an optimization method for extreme temperature-adaptive farming systems based on carbon flux response. Background Technology

[0002] Against the backdrop of global climate change, the increasing frequency and intensity of extreme temperature events have become a major challenge for agricultural production. The carbon cycle processes in farmland ecosystems are extremely sensitive to temperature changes, and cropping systems, as a core element of agricultural management, directly affect soil carbon storage, crop carbon sequestration, and net carbon flux. Current research on optimizing cropping systems adapted to extreme temperatures mainly includes methods for adjusting sowing dates and varieties based on crop physiological responses, methods for optimizing management measures based on statistical models, and carbon footprint analysis methods based on life cycle assessment. Eddy covariance techniques are also increasingly widely used in farmland carbon flux observation, providing technical support for obtaining actual carbon budget data for farmland ecosystems.

[0003] Existing technologies have significant limitations in optimizing cropping systems under extreme temperature conditions. Methods based on crop physiological responses primarily focus on aboveground indicators, neglecting soil carbon cycling processes and failing to comprehensively assess the combined impact of cropping systems on the carbon balance of farmland ecosystems. While statistical model-based methods can handle multivariate relationships, they lack mechanistic explanations and have limited extrapolation capabilities for extreme temperature events beyond the training data. Methods based on carbon footprint assessment rely heavily on empirical coefficients and emission factors from the literature, resulting in significant errors when applied to different regions and failing to dynamically reflect the real-time impact of extreme temperatures on carbon cycling processes. Although eddy covariance techniques can obtain high-precision measured carbon flux data, current research primarily uses them for carbon budget accounting and has not yet effectively integrated them into decision support systems for cropping system optimization.

[0004] Based on the current state of technological development, the main problems in current research on cropping system optimization include: a lack of a cropping system evaluation system centered on measured carbon flux, making it impossible to accurately quantify the impact of different management measures on the actual carbon sink function of farmland; the failure to establish a quantitative prediction model for carbon flux response under extreme temperature conditions, making it difficult to characterize the nonlinear impact and lag effects of extreme temperature events on the carbon cycle process; the failure of optimization methods to effectively integrate the ecological mechanisms of the carbon cycle and agronomic practice constraints, resulting in a lack of practicality and operability of theoretically optimal schemes; the lack of a robust optimization framework that considers climate uncertainty, and the long-term stability of existing schemes under the background of climate change has not been fully verified; and the lack of a universal optimization methodology across regions and crop systems, limiting the promotion and application of research results. These technological bottlenecks restrict the scientific optimization and widespread application of extreme temperature adaptive cropping systems. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method for optimizing extreme temperature-adaptive farming systems based on carbon flux response.

[0006] The objective of this invention can be achieved through the following technical solutions: An optimization method for extreme temperature-adaptive farming systems based on carbon flux response, the method comprising: Establish a continuous carbon flux monitoring system in the target farmland; The carbon flux continuous observation system continuously observes at least three complete crop growing seasons, collecting carbon flux data, meteorological data, soil data, and crop management information; the collected data is processed through a data quality control process to construct a comprehensive database; Based on the comprehensive database, extreme temperature events and their corresponding carbon flux response patterns in the target farmland are identified. Through multi-factor field comparison experiments, the impact of different farming management measures on farmland carbon flux, crop yield, economic benefits and environmental benefits is obtained. A multi-objective optimization function that comprehensively considers carbon sequestration, crop yield, economic benefits and environmental benefits is constructed. Based on extreme temperature events in the target farmland and their corresponding carbon flux response patterns, multiple constraints are set to ensure the carbon sink function and production stability of the farmland. Based on the impact of different farming management measures on farmland carbon flux, crop yield, economic benefits and environmental benefits, as well as the extreme temperature events of the target farmland and their corresponding carbon flux response patterns, an intelligent optimization algorithm is adopted to globally search and solve the multi-objective optimization function by simulating nature or swarm intelligence, and obtain the optimal farming system. The optimal farming system includes the optimal farming treatment, planting system, land cover method and water and fertilizer management method under different climate scenarios and production conditions.

[0007] Furthermore, the continuous carbon flux monitoring system includes core measurement equipment, auxiliary meteorological observation equipment, soil observation devices, and crop management devices; wherein, The core measuring equipment is used to collect information on carbon concentration, water concentration, wind speed, and turbulent fluctuations in the target farmland. The auxiliary meteorological observation equipment is used to collect data on the temperature and humidity of the target farmland, upward shortwave radiation, downward shortwave radiation, longwave radiation, photosynthetically active radiation, and rainfall. The soil monitoring device is used to collect soil temperature, soil moisture, soil heat flux, and soil carbon flux of the target farmland. The crop management device is used to collect crop leaf area index, leaf photosynthetic rate, stomatal conductance, transpiration rate, and yield.

[0008] Furthermore, the process of processing the collected data through the aforementioned data quality control procedure includes: Raw data preprocessing: Remove outliers during instrument malfunctions, and perform coordinate rotation correction, WPL density correction, and frequency response correction; Flux data filtering: Low-quality data are removed based on preset frictional wind speed threshold, stability parameters, and integrated turbidity test. Missing data imputation: using marginal distribution sampling or artificial neural network methods to fill in missing data; Flux decomposition: Net ecosystem carbon exchange is obtained based on carbon flux data, and net ecosystem carbon exchange is decomposed into ecosystem respiration and gross primary productivity using nighttime flux extrapolation or light response curve fitting. Data integration: Archive data on ecosystem respiration, total primary productivity, carbon flux, meteorological data, soil data, and crop management information to form a comprehensive database containing timestamps, flux values, environmental variables, and management measures.

[0009] Furthermore, the extreme temperature events include extreme high temperature events and extreme low temperature events; The process of identifying the extreme temperature events in the target farmland includes: The 95th percentile of the daily maximum temperature in the comprehensive database is selected as the high temperature threshold. Based on this high temperature threshold, all daily maximum temperatures in the comprehensive database are traversed. If the daily maximum temperature exceeds the high temperature threshold for 3 or more consecutive days, the time period when the daily maximum temperature is higher than the high temperature threshold is defined as an extreme high temperature event. The intensity of the event is calculated and recorded. The 5th percentile of the daily minimum temperature in the comprehensive database is selected as the low temperature threshold. Based on this low temperature threshold, all daily minimum temperatures in the comprehensive database are traversed. If the daily minimum temperature exceeds the low temperature threshold for 3 or more consecutive days, the time period when the daily minimum temperature is higher than the threshold is defined as an extreme minimum event. The event intensity is calculated and recorded. Each extreme temperature event is divided into three phases: the pre-event phase, the event period, and the post-event phase, and these phases are recorded.

[0010] Furthermore, the intensity of the extreme high-temperature event is obtained by summing the differences between the highest daily temperature and the high-temperature threshold within the extreme high-temperature event; the intensity of the extreme low-temperature event is obtained by summing the differences between the lowest daily temperature and the low-temperature threshold within the extreme low-temperature event.

[0011] Furthermore, the process of identifying the corresponding carbon flux response patterns to extreme temperature events in target farmland includes: Using a nonlinear regression method, based on the comprehensive database, response curves of net ecosystem carbon exchange, ecosystem respiration, and gross primary productivity to temperature were fitted respectively. Combined with the extreme temperature events, the sensitivity and response characteristics of net ecosystem carbon exchange, ecosystem respiration, and gross primary productivity to temperature changes were identified. The growth period of the target farmland crop is divided into the sowing-emergence stage, the vegetative growth stage, the reproductive growth stage, and the maturity stage. Based on the extreme temperature events and the response curves of net ecosystem carbon exchange, ecosystem respiration, and total primary productivity to temperature, the fluctuation range and recovery capacity of carbon flux when encountering extreme temperatures at each stage of the growth period are obtained. By combining the sensitivity and response characteristics of net ecosystem carbon exchange, ecosystem respiration, and total primary productivity to temperature changes, as well as the fluctuation range and recovery capacity of carbon flux when encountering extreme temperatures at different stages of the growth period, the corresponding carbon flux response patterns for each extreme temperature event are obtained.

[0012] Furthermore, the multiple constraints for ensuring the carbon sequestration function and production stability of farmland include: Extreme heat carbon sink constraint: The average daily carbon flux during extreme heat events is higher than the minimum value of the carbon flux versus temperature response curve; Extreme low temperature carbon loss constraint: The average daily ecosystem respiration during extreme low temperature events is lower than the highest value of the ecosystem respiration versus temperature response curve; Annual carbon sink constraint: The cumulative net ecosystem carbon exchange is negative throughout the year; Yield stability constraint: The average annual crop yield and the standard deviation of annual yield are less than a preset percentage; Yield floor constraint: The crop yield is greater than the average yield floor obtained from multi-factor field comparison trials; Economic benefit constraint: The ratio of crop output value to input cost is greater than a predetermined multiple; Water resource constraints: Water resources used for farming are less than the regional water resource quota; Fertilizer application constraints: The amount of fertilizer applied during cultivation is less than the soil carrying capacity threshold.

[0013] Furthermore, the process of obtaining the impact of different tillage management practices on farmland carbon flux, crop yield, and economic benefits through the aforementioned multi-factor field comparison experiment includes: Different tillage treatments, planting systems, land cover methods, and water and fertilizer management methods are set up, and several tillage system groups are randomly combined to obtain them. Different farming systems were implemented in the target farmland, and carbon flux data, crop yield data, and environmental benefit data were collected. The economic benefits and carbon buffering effect index generated under different farming systems were calculated, and the impact of different farming management measures on farmland carbon flux, crop yield, and economic benefits was obtained.

[0014] Furthermore, the tillage methods include no-till, rotary tillage, deep loosening, and plowing; the planting systems include monoculture, intercropping, crop rotation, and seasonal fallow; the surface mulch methods include full straw return to the field, biodegradable mulch film, green manure mulch, and bare land; the water and fertilizer management methods include combinations of different irrigation systems, fertilizer types, and fertilizer application times, the irrigation systems include full irrigation, deficit irrigation, and rainfed irrigation, and the fertilizer types include chemical fertilizers, organic fertilizers, and compound fertilizers.

[0015] Furthermore, the intelligent optimization algorithm includes genetic algorithms, particle swarm optimization algorithms, or machine learning methods.

[0016] Compared with the prior art, the beneficial effects of the present invention include: 1. This invention addresses the problem of existing technologies prioritizing production over ecology or carbon sequestration over yield, focusing on a singular approach. By integrating carbon sequestration, crop yield, economic benefits, and environmental benefits through a multi-objective optimization function, it ensures both the carbon sequestration function of farmland and stable crop yields and economic benefits, achieving synergistic improvement in ecology, production, and economy. Based on measured data from at least three complete growing seasons, this invention constructs a database and combines it with multi-climate scenario simulations, avoiding the weak extrapolation capabilities of existing statistical models. This ensures the long-term stability and robustness of the optimal farming system, adapting to the increasing frequency and intensity of extreme temperature events. Through multi-factor field comparative experiments, this invention covers key management dimensions in real agricultural production, directly providing specific combination schemes based on the optimization results without additional secondary transformations, directly guiding field practice and solving the problem of the disconnect between theoretical optimality and agronomic practice. Using measured carbon flux as the core, this invention does not rely on regionally specific empirical coefficients. By adjusting the observation period and experimental parameters, it can be adapted to different farmland types, crop systems, and climate zones, overcoming the regional limitations of existing technologies and demonstrating strong adaptability.

[0017] 2. In this invention, by accurately identifying extreme temperature events and their carbon flux response patterns, and by setting targeted constraints on the objective function, the optimized farming system can effectively buffer the impact of extreme temperatures on the carbon cycle, reduce the degradation of carbon sink functions and yield fluctuations, and enhance the resilience and recovery capacity of farmland ecosystems.

[0018] 3. This invention uses eddy covariance technology to establish a continuous observation system, which, combined with core measurement equipment, soil observation devices and crop management devices, enables synchronous and high-precision acquisition of carbon flux, meteorological, soil and crop data. Compared with the traditional static box method, the timeliness and completeness of the data are improved. Attached Figure Description

[0019] Figure 1 This is a flowchart of the method of the present invention; Figure 2 The present invention presents the net ecosystem carbon exchange (NEE) response curves under extreme high-temperature events using different tillage treatments. Figure 3 This invention presents the GPP response curves of ecosystem respiration under extreme high-temperature events using different tillage treatments. Figure 4 The total primary productivity (Re) response curves for different tillage treatments under extreme high-temperature events are shown in the figure. Figure 5 The present invention presents the net ecosystem carbon exchange (NEE) response curves under extreme low temperature events using different tillage treatments. Figure 6 This invention presents the GPP response curves of ecosystem respiration under extreme low temperature events using different tillage treatments. Figure 7 The total primary productivity (Re) response curves for this invention under extreme low temperature events using different tillage treatments. Detailed Implementation

[0020] 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, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] Example 1 This embodiment discloses a method for optimizing extreme temperature-adaptive farming systems based on carbon flux response. The method is as follows: Figure 1 As shown, steps S1-S7 are included, and each step is described in detail below, including: Step S1: Establish a continuous carbon flux monitoring system in the target farmland using eddy covariance technology.

[0022] Step S2 involves continuously observing at least three complete crop growing seasons using a carbon flux continuous observation system, collecting carbon flux data, meteorological data, soil data, and crop management information.

[0023] The continuous carbon flux monitoring system includes core measurement equipment, auxiliary meteorological observation equipment, soil observation devices, and crop management devices; among them, The core measurement equipment is used to collect information on carbon concentration, water concentration, wind speed, and turbulent fluctuations in the target farmland; Auxiliary meteorological observation equipment is used to collect data on temperature and humidity, upward shortwave radiation, downward shortwave radiation, longwave radiation, photosynthetically active radiation, and rainfall of the target farmland. The soil monitoring device is used to collect soil temperature, soil moisture, soil heat flux, and soil carbon flux in the target farmland. Crop management devices are used to collect data on crop leaf area index, leaf photosynthetic rate, stomatal conductance, transpiration rate, and yield.

[0024] Specifically, the continuous carbon flux monitoring system in this embodiment is as follows: Core measuring equipment: An observation tower 3-5 meters high is set up in the central area of ​​the farmland. An open-circuit infrared gas analyzer (IRGA) is installed at the top of the tower to measure CO2 and H2O concentrations. A three-dimensional ultrasonic anemometer is also included to measure high-frequency pulsations of wind speed and virtual temperature. The sampling frequency is set to 10 Hz to ensure the capture of turbulent pulsation information. The tower height is set at 3-5 meters to ensure the sensor installation height is 1.5-2.5 meters above the crop canopy. The tower body adopts a steel frame or aluminum alloy structure, with a concrete foundation at least 80 cm deep to ensure stability.

[0025] Auxiliary meteorological observation: Temperature and humidity sensors (to measure air temperature and relative humidity), four-component radiation sensors (to measure upward and downward shortwave and longwave radiation), photosynthetically active radiation sensors (PAR), and precipitation sensors are deployed at different heights of the observation tower. The sampling frequency of meteorological elements is 0.1 Hz (recorded once every 10 seconds).

[0026] Soil monitoring equipment: Soil temperature probes (depths of 5cm, 10cm, 20cm, and 40cm), soil moisture sensors (depths of 10cm, 20cm, and 40cm), and soil heat flux plates (depth of 5cm) are buried at representative locations around the monitoring tower, with a sampling frequency of 0.1 Hz. Simultaneously, 4-6 automatic soil respiration measurement systems are installed in the farmland, using closed-circuit soil respiration chambers, measuring soil CO2 flux every 30 minutes.

[0027] Crop management device: measures photosynthetic physiological indicators such as leaf area index, leaf photosynthetic rate, stomatal conductance, and transpiration rate, and records yield.

[0028] Data Acquisition and Storage: A high-performance data acquisition unit is used to record all sensor data in real time. It is equipped with a large-capacity storage card and a remote communication module to achieve local data storage and automatic cloud upload and backup.

[0029] The observation coverage of a continuous carbon flux monitoring system is affected by factors such as wind speed, atmospheric stability, and observation altitude, and is generally within 100-200 times the observation altitude. Taking an observation altitude of 3 meters as an example, the main flux contribution area is a region with a radius of 300-600 meters. Therefore, it is necessary to ensure the homogeneity of the surface within this range and avoid interference from heterogeneous underlying surfaces such as buildings, roads, and water bodies.

[0030] In another embodiment, a static chamber method was used to determine net ecosystem carbon exchange (NEE) and ecosystem respiration (Re). Specifically, the static chamber consisted of a chamber body, a base, and a sampling system. The chamber body was made of either opaque material (for measuring Re) or transparent material (for measuring NEE). A small fan was installed inside to maintain air homogenization, and a temperature sensor was installed to monitor temperature changes within the chamber. The base was buried in the soil 7-10 days in advance at a depth of 5-8 cm. During sampling, the chamber body was placed on the base to create a sealed space.

[0031] Step S3: Process the collected data through the data quality control process to build a comprehensive database.

[0032] The process of processing collected data through data quality control procedures includes: Raw data preprocessing: Remove outliers during instrument malfunctions, and perform coordinate rotation correction, WPL density correction, and frequency response correction; Flux data filtering: Low-quality data are removed based on preset frictional wind speed threshold, stability parameters, and integrated turbidity test. Missing data imputation: using marginal distribution sampling or artificial neural network methods to fill in missing data; Flux decomposition: Net ecosystem carbon exchange (NEE) is obtained based on carbon flux data. The NEE is decomposed into ecosystem respiration (GPP) and total primary productivity (Re) using nighttime flux extrapolation or light response curve fitting. Data integration: Archive data on ecosystem respiration, total primary productivity, carbon flux, meteorological data, soil data, and crop management information to form a comprehensive database containing timestamps, flux values, environmental variables, and management measures.

[0033] Step S4: Based on the comprehensive database, identify extreme temperature events in the target farmland and their corresponding carbon flux response patterns.

[0034] Extreme temperature events include extreme high temperature events and extreme low temperature events; The process of identifying extreme temperature events in target farmland includes: The 95th percentile of the daily maximum temperature in the comprehensive database is selected as the high temperature threshold. Based on this high temperature threshold, all daily maximum temperatures in the comprehensive database are traversed. If the daily maximum temperature exceeds the high temperature threshold for 3 or more consecutive days, the time period when the daily maximum temperature is higher than the high temperature threshold is defined as an extreme high temperature event. The intensity of the event is calculated and recorded. The 5th percentile of the daily minimum temperature in the comprehensive database is selected as the low temperature threshold. Based on this low temperature threshold, all daily minimum temperatures in the comprehensive database are traversed. If the daily minimum temperature exceeds the low temperature threshold for 3 or more consecutive days, the time period when the daily minimum temperature is higher than the minimum threshold is defined as an extreme minimum event. The intensity of the event is calculated and recorded. Each extreme temperature event is divided into three phases: the pre-event phase, the event period, and the post-event phase, and these phases are recorded.

[0035] The intensity of an extreme high-temperature event is obtained by summing the differences between the highest daily temperature and the high-temperature threshold within the extreme high-temperature event; the intensity of an extreme low-temperature event is obtained by summing the differences between the lowest daily temperature and the low-temperature threshold within the extreme low-temperature event.

[0036] The specific formula is as follows: Extreme heat events: in, Let the maximum daily temperature be on day i. This is the 95th percentile of the daily maximum temperature during the observation period.

[0037] Extreme low temperature events: in, This represents the 5th percentile of the daily minimum temperature during the observation period. The minimum temperature on day i is denoted as .

[0038] The process of identifying the corresponding carbon flux response patterns of extreme temperature events in target farmland includes: Using a nonlinear regression method and based on a comprehensive database, response curves of net ecosystem carbon exchange, ecosystem respiration, and gross primary productivity to temperature were fitted, respectively. Combined with extreme temperature events, the sensitivity and response characteristics of net ecosystem carbon exchange, ecosystem respiration, and gross primary productivity to temperature changes were identified. The growth period of the target farmland crop is divided into the sowing-emergence stage, the vegetative growth stage, the reproductive growth stage, and the maturity stage. By combining the response curves of extreme temperature events and net ecosystem carbon exchange, ecosystem respiration, and total primary productivity with temperature, the fluctuation range and recovery capacity of carbon flux when extreme temperatures occur at each stage of the growth period are obtained. By combining the sensitivity and response characteristics of net ecosystem carbon exchange, ecosystem respiration, and total primary productivity to temperature changes, as well as the fluctuation range and recovery capacity of carbon flux when encountering extreme temperatures at different stages of the growth period, the corresponding carbon flux response patterns for each extreme temperature event can be obtained.

[0039] Identifying the critical vulnerable periods most sensitive to carbon flux response, such as the sensitivity of the flowering and pollination period to high temperature stress and the vulnerability of the seedling stage to low temperature damage, and clarifying the differences in sensitivity at different growth stages can guide the precise implementation of adaptive management measures such as sowing date adjustment and variety selection.

[0040] Step S5 involves conducting a multi-factor field comparison experiment to obtain the impact of different farming management practices on farmland carbon flux, crop yield, economic benefits, and environmental benefits.

[0041] The process of obtaining the impact of different farming management practices on farmland carbon flux, crop yield, and economic benefits through multi-factor field comparison experiments includes: Different tillage treatments, planting systems, land cover methods, and water and fertilizer management methods are set up, and several tillage system groups are randomly combined to obtain them. Different farming systems were implemented in the target farmland, and carbon flux data, crop yield data, and environmental benefit data were collected. The economic benefits and carbon buffering effect index generated under different farming systems were calculated, and the impact of different farming management measures on farmland carbon flux, crop yield, and economic benefits was obtained.

[0042] Tillage treatments included no-till, rotary tillage, deep loosening, and plowing, and the impact of soil disturbance on carbon emissions and carbon sequestration was compared. Figures 2-7 This refers to the response curves of carbon flux and extreme temperatures under different farming practices, demonstrating the changes in net ecosystem carbon exchange (NEE), ecosystem respiration (GPP), and total primary productivity (Re) under different farming practices and extreme temperatures. The cropping systems included monoculture, intercropping, crop rotation, and seasonal fallow, and the regulatory role of crop configuration on the carbon balance of the system was evaluated. Surface mulch methods include full straw return to the field mulch, biodegradable mulch film mulch, green manure live mulch and bare land. The study investigates the effects of mulch measures on soil temperature, humidity and carbon cycle. Water and fertilizer management methods include combinations of different irrigation regimes, fertilizer types, and fertilizer application times. This study aims to reveal the comprehensive effect of water and fertilizer coupling on carbon flux and record the impact of different water and fertilizer combinations on soil and water resources. Irrigation regimes include full irrigation, deficit irrigation, and rainfed irrigation. Fertilizer types include chemical fertilizers, organic fertilizers, and compound fertilizers.

[0043] Step S6: Construct a multi-objective optimization function that comprehensively considers carbon sequestration, crop yield, economic benefits and environmental benefits, and set multiple constraints to ensure the carbon sink function and production stability of the farmland based on extreme temperature events of the target farmland and their corresponding carbon flux response patterns.

[0044] The multi-objective optimization function can be expressed as follows: in, F For the overall score, , , and The sum of these values ​​is 1, and they represent the weighting parameters for carbon sequestration, crop yield, economic benefits, and environmental benefits, respectively. These parameters can be changed according to the application scenario, such as in a carbon sink priority scenario. , , and The values ​​are 0.5, 0.3, 0.15 and 0.05 respectively, and 0.2, 0.5, 0.25 and 0.05 respectively in the production priority scenario.

[0045] in, Net ecosystem carbon exchange, For the standard deviation of output, This represents the average output.

[0046] Environmental benefits are obtained by weighted summation of water conservation rate, soil erosion control rate, and greenhouse gas emission reduction.

[0047] Multiple constraints on ensuring the carbon sequestration function and production stability of farmland include: Extreme high-temperature carbon sink constraint: The average daily carbon flux during an extreme high-temperature event is higher than the minimum value of the carbon flux versus temperature response curve; in this embodiment, the specific values ​​of the constraints for net ecosystem carbon exchange (NEE), ecosystem respiration (GPP), and gross primary productivity (Re) are as follows: Figures 2-4 The threshold line is shown in the image; Extreme low temperature carbon loss constraint: The average daily ecosystem respiration during an extreme low temperature event is lower than the highest value of the ecosystem respiration versus temperature response curve; in this embodiment, the specific values ​​of the constraints for net ecosystem carbon exchange (NEE), ecosystem respiration (GPP), and total primary productivity (Re) are as follows: Figures 5-7 The extreme low temperature threshold line is shown below; Annual carbon sink constraint: The cumulative net ecosystem carbon exchange is negative throughout the year; Yield stability constraint: The average annual crop yield and the standard deviation of annual yield are less than a preset percentage; Yield floor constraint: The crop yield is greater than the average yield floor obtained from multi-factor field comparison trials; Economic benefit constraint: The ratio of crop output value to input cost is greater than a predetermined multiple; Water resource constraints: Water resources used for farming are less than the regional water resource quota; Fertilizer application constraints: The amount of fertilizer applied during cultivation is less than the soil carrying capacity threshold.

[0048] Step S7: Based on the impact of different farming management practices on farmland carbon flux, crop yield, economic benefits, and environmental benefits, as well as the extreme temperature events and corresponding carbon flux response patterns of the target farmland, an intelligent optimization algorithm is used to globally search for and solve a multi-objective optimization function by simulating nature or swarm intelligence to obtain the optimal farming regime. The optimal farming regime includes the optimal farming treatment, planting system, land cover method, and water and fertilizer management method under different climate scenarios and production conditions.

[0049] Intelligent optimization algorithms include genetic algorithms, particle swarm optimization, or machine learning methods.

[0050] For complex optimization problems involving multiple variables, nonlinearity, and multiple constraints, genetic algorithms can be used to simulate the natural selection process for global optimization, or particle swarm optimization can be employed to quickly converge to the optimal solution through group collaboration. For high-dimensional complex systems, machine learning methods can also be introduced, such as random forest algorithms that improve prediction accuracy by integrating multiple decision trees, or artificial neural network models that learn the complex nonlinear relationships between carbon flux and multiple factors. Through iterative calculations, the algorithm ultimately outputs the optimal combination of farming methods, planting systems, cover methods, and water and fertilizer management schemes under different climate scenarios and production conditions, providing quantitative decision-making basis for practical applications.

[0051] Example 2 Based on Embodiment 1, this embodiment provides an electronic device, including: one or more processors and a memory, wherein the memory stores one or more programs, the one or more programs including instructions for executing the aforementioned method for optimizing extreme temperature-adaptive farming systems based on carbon flux response.

[0052] At the hardware level, the electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to implement the aforementioned method for optimizing extreme temperature-adaptive farming systems based on carbon flux response. Of course, besides software implementation, this invention does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution entity of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.

[0053] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0054] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0055] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing extreme temperature-adaptive farming systems based on carbon flux response, characterized in that, The method includes: Establish a continuous carbon flux monitoring system in the target farmland; The carbon flux continuous observation system continuously observes at least three complete crop growing seasons, collecting carbon flux data, meteorological data, soil data, and crop management information; the collected data is processed through a data quality control process to construct a comprehensive database; Based on the comprehensive database, extreme temperature events and their corresponding carbon flux response patterns in the target farmland are identified. Through multi-factor field comparison experiments, the impact of different farming management measures on farmland carbon flux, crop yield, economic benefits and environmental benefits is obtained. A multi-objective optimization function that comprehensively considers carbon sequestration, crop yield, economic benefits and environmental benefits is constructed. Based on extreme temperature events in the target farmland and their corresponding carbon flux response patterns, multiple constraints are set to ensure the carbon sink function and production stability of the farmland. Based on the impact of different farming management measures on farmland carbon flux, crop yield, economic benefits and environmental benefits, as well as the extreme temperature events of the target farmland and their corresponding carbon flux response patterns, an intelligent optimization algorithm is adopted to globally search and solve the multi-objective optimization function by simulating nature or swarm intelligence, and obtain the optimal farming system. The optimal farming system includes the optimal farming treatment, planting system, land cover method and water and fertilizer management method under different climate scenarios and production conditions.

2. The method for optimizing extreme temperature-adaptive farming systems based on carbon flux response according to claim 1, characterized in that, The continuous carbon flux monitoring system includes core measurement equipment, auxiliary meteorological observation equipment, soil observation devices, and crop management devices; among which, The core measuring equipment is used to collect information on carbon concentration, water concentration, wind speed, and turbulent fluctuations in the target farmland. The auxiliary meteorological observation equipment is used to collect data on the temperature and humidity of the target farmland, upward shortwave radiation, downward shortwave radiation, longwave radiation, photosynthetically active radiation, and rainfall. The soil monitoring device is used to collect soil temperature, soil moisture, soil heat flux, and soil carbon flux of the target farmland. The crop management device is used to collect crop leaf area index, leaf photosynthetic rate, stomatal conductance, transpiration rate, and yield.

3. The method for optimizing extreme temperature-adaptive farming systems based on carbon flux response according to claim 1, characterized in that, The process of processing the collected data through the aforementioned data quality control process includes: Raw data preprocessing: Remove outliers during instrument malfunctions, and perform coordinate rotation correction, WPL density correction, and frequency response correction; Flux data filtering: Low-quality data are removed based on preset frictional wind speed threshold, stability parameters, and integrated turbidity test. Missing data imputation: using marginal distribution sampling or artificial neural network methods to fill in missing data; Flux decomposition: Net ecosystem carbon exchange is obtained based on carbon flux data, and net ecosystem carbon exchange is decomposed into ecosystem respiration and gross primary productivity using nighttime flux extrapolation or light response curve fitting. Data integration: Archive data on ecosystem respiration, total primary productivity, carbon flux, meteorological data, soil data, and crop management information to form a comprehensive database containing timestamps, flux values, environmental variables, and management measures.

4. The method for optimizing extreme temperature-adaptive farming systems based on carbon flux response according to claim 1, characterized in that, The extreme temperature events include extreme high temperature events and extreme low temperature events; The process of identifying the extreme temperature events in the target farmland includes: The 95th percentile of the daily maximum temperature in the comprehensive database is selected as the high temperature threshold. Based on this high temperature threshold, all daily maximum temperatures in the comprehensive database are traversed. If the daily maximum temperature exceeds the high temperature threshold for 3 or more consecutive days, the time period when the daily maximum temperature is higher than the high temperature threshold is defined as an extreme high temperature event. The intensity of the event is calculated and recorded. The 5th percentile of the daily minimum temperature in the comprehensive database is selected as the low temperature threshold. Based on this low temperature threshold, all daily minimum temperatures in the comprehensive database are traversed. If the daily minimum temperature exceeds the low temperature threshold for 3 or more consecutive days, the time period when the daily minimum temperature is higher than the threshold is defined as an extreme minimum event. The event intensity is calculated and recorded. Each extreme temperature event is divided into three phases: the pre-event phase, the event period, and the post-event phase, and these phases are recorded.

5. The method for optimizing extreme temperature-adaptive farming systems based on carbon flux response according to claim 4, characterized in that, The intensity of the extreme high-temperature event is obtained by summing the differences between the highest temperature of each day within the extreme high-temperature event and the high-temperature threshold; the intensity of the extreme low-temperature event is obtained by summing the differences between the lowest temperature of each day within the extreme low-temperature event and the low-temperature threshold.

6. The method for optimizing extreme temperature-adaptive farming systems based on carbon flux response according to claim 4, characterized in that, The process of identifying the corresponding carbon flux response patterns of extreme temperature events in target farmland includes: Using a nonlinear regression method, based on the comprehensive database, response curves of net ecosystem carbon exchange, ecosystem respiration, and gross primary productivity to temperature were fitted respectively. Combined with the extreme temperature events, the sensitivity and response characteristics of net ecosystem carbon exchange, ecosystem respiration, and gross primary productivity to temperature changes were identified. The growth period of the target farmland crop is divided into the sowing-emergence stage, the vegetative growth stage, the reproductive growth stage, and the maturity stage. Based on the extreme temperature events and the response curves of net ecosystem carbon exchange, ecosystem respiration, and total primary productivity to temperature, the fluctuation range and recovery capacity of carbon flux when encountering extreme temperatures at each stage of the growth period are obtained. By combining the sensitivity and response characteristics of net ecosystem carbon exchange, ecosystem respiration, and total primary productivity to temperature changes, as well as the fluctuation range and recovery capacity of carbon flux when encountering extreme temperatures at different stages of the growth period, the corresponding carbon flux response patterns for each extreme temperature event are obtained.

7. The method for optimizing extreme temperature-adaptive farming systems based on carbon flux response according to claim 6, characterized in that, The multiple constraints for ensuring the carbon sequestration function and production stability of farmland include: Extreme heat carbon sink constraint: The average daily carbon flux during extreme heat events is higher than the minimum value of the carbon flux versus temperature response curve; Extreme low temperature carbon loss constraint: The average daily ecosystem respiration during extreme low temperature events is lower than the highest value of the ecosystem respiration versus temperature response curve; Annual carbon sink constraint: The cumulative net ecosystem carbon exchange is negative throughout the year; Yield stability constraint: The average annual crop yield and the standard deviation of annual yield are less than a preset percentage; Yield floor constraint: The crop yield is greater than the average yield floor obtained from multi-factor field comparison trials; Economic benefit constraint: The ratio of crop output value to input cost is greater than a predetermined multiple; Water resource constraints: Water resources used for farming are less than the regional water resource quota; Fertilizer application constraints: The amount of fertilizer applied during cultivation is less than the soil carrying capacity threshold.

8. The method for optimizing extreme temperature-adaptive farming systems based on carbon flux response according to claim 1, characterized in that, The process of obtaining the impact of different farming management practices on farmland carbon flux, crop yield, and economic benefits through the aforementioned multi-factor field comparison experiment includes: Different tillage treatments, planting systems, land cover methods, and water and fertilizer management methods are set up, and several tillage system groups are randomly combined to obtain them. Different farming systems were implemented in the target farmland, and carbon flux data, crop yield data, and environmental benefit data were collected. The economic benefits and carbon buffering effect index generated under different farming systems were calculated, and the impact of different farming management measures on farmland carbon flux, crop yield, and economic benefits was obtained.

9. The method for optimizing extreme temperature-adaptive farming systems based on carbon flux response according to claim 1, characterized in that, The tillage methods include no-till, rotary tillage, deep loosening, and plowing; the planting systems include monoculture, intercropping, crop rotation, and seasonal fallow; the surface mulch methods include full straw return to the field, biodegradable mulch film, green manure mulch, and bare land; the water and fertilizer management methods include combinations of different irrigation systems, fertilizer types, and fertilizer application times, the irrigation systems include full irrigation, deficit irrigation, and rainfed irrigation, and the fertilizer types include chemical fertilizers, organic fertilizers, and compound fertilizers.

10. The method for optimizing extreme temperature-adaptive farming systems based on carbon flux response according to claim 1, characterized in that, The intelligent optimization algorithm includes genetic algorithms, particle swarm optimization algorithms, or machine learning methods.

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