A soil fertilization optimization method combining straw returning and organic material addition

Abstract

The present application relates to the field of agricultural data analysis, and more particularly to a soil fertilization optimization method fusing straw returning and organic material adding, which comprises: obtaining soil fertilization ratio basic data set; performing integral gap evaluation on carbon and nitrogen release rate kinetics time dislocation characteristics to obtain carbon and nitrogen kinetics dislocation penalty factor; performing space coordination compensation evaluation on the spatial microenvironment physical coupling characteristics of the plough layer to obtain microenvironment coordination compensation factor; performing nonlinear bounded coupling correction on the carbon and nitrogen kinetics dislocation penalty factor, the microenvironment coordination compensation factor and the basic static nitrogen supply amount to obtain optimized dynamic effective nitrogen supply amount; and performing constraint solving and cost minimization processing on the optimized dynamic effective nitrogen supply amount to obtain a straw and organic material matching scheme, thereby solving the problem that the existing soil fertilization ratio algorithm is difficult to identify the early effective nitrogen deficiency caused by the key seedling stage microbial nitrogen competition and the matching amount error.

Description

Technical Field

[0001] This invention relates to the field of agricultural data analysis technology, and in particular to a method for soil fertility optimization that integrates straw return to the field with the addition of organic materials. Background Technology

[0002] In modern agricultural production and the construction of ecological farmland, returning straw to the field and adding organic materials are important technical means to improve soil fertility, enhance the structure of the topsoil, and promote the resource utilization of agricultural waste. Returning straw to the field increases the source of soil organic carbon, improves soil aggregate structure, and enhances water and fertilizer retention capacity. Organic materials such as livestock and poultry manure, commercial organic fertilizer, and compost can supplement nitrogen, phosphorus, potassium, and organic matter, providing a continuous source of nutrients for crop growth. Therefore, in farmland fertilization planning, it is usually necessary to determine the ratio of straw to organic materials per unit area of ​​farmland based on crop nutrient requirements, the amount of straw returned to the field, and the nutrient content of external organic materials, in order to achieve the comprehensive goals of soil fertilization, nutrient supply, and resource recycling. Existing soil fertility improvement methods typically rely on stoichiometric equilibrium models covering the entire growth cycle. The basic approach involves using the theoretical total nitrogen requirement of the target crop throughout its growth period as a constraint. Based on the nominal nitrogen content of straw, the nominal mineralization rate over the entire growth cycle, and the nominal nitrogen content and mineralization rate of organic matter, the theoretical nitrogen supply from straw and organic matter over the entire growth cycle are calculated separately, and then linearly superimposed. When the superimposed theoretical nitrogen supply reaches or exceeds the crop's nitrogen requirement over the entire growth cycle, the existing system usually determines that the fertilization scheme meets the crop's nutrient needs and outputs the straw return and organic matter application amounts accordingly.

[0003] However, in actual field environments, the degradation and nutrient release of straw and organic materials after entering the soil are not static, synchronous, or linear processes. High-carbon, low-nitrogen straw stimulates rapid reproduction of soil microorganisms in the early stages of its return to the field. As these microorganisms decompose the straw and release carbon, they simultaneously assimilate available nitrogen from the environment, thus achieving a certain degree of nitrogen fixation during the crop seedling stage. At the same time, the nitrogen release process of organic materials has its own release rate and mineralization lag characteristics, and may not be synchronized with the microbial nitrogen requirement induced by straw carbon release during the critical seedling stage. Therefore, even if the total nitrogen supply throughout the entire growth period theoretically meets the requirements, crops may still experience short-term nitrogen deficiency, yellowing seedlings, weak seedlings, or slow growth in the early stages due to competition for available nitrogen by microorganisms.

[0004] Furthermore, the actual degradation rate of straw after returning it to the field is significantly affected by the physical microenvironment of the topsoil. For example, the higher the degree of mechanical crushing of straw, the larger its specific surface area in contact with soil and microorganisms, which is more conducive to microbial attachment and degradation; the closer the soil volumetric moisture content is to the suitable activity range of microorganisms, the more conducive it is to the decomposition of organic matter and the release of nutrients. Conversely, when straw is not crushed sufficiently or the topsoil moisture is too dry or too wet, the straw degradation rate and the mineralization process of organic materials may be inhibited. Existing proportioning algorithms often only use nominal material parameters and fixed mineralization rates for calculation, lacking joint analysis of the distribution of straw crushing length along the depth of the topsoil, the distribution of actual soil volumetric moisture content, and the suitable moisture conditions for microorganisms, making it difficult to dynamically correct the effective nitrogen supply based on the real field microenvironment.

[0005] Therefore, how to comprehensively consider the time misalignment of carbon and nitrogen release kinetics, the nitrogen competition effect of key seedling stage microorganisms, and the compensating effect of the microenvironment of the topsoil space on the degradation rate during the soil fertilization process of straw return to the field and organic material addition, and construct a dynamic and effective nitrogen supply constraint model that better reflects the actual nitrogen supply process in the field, so as to improve the accuracy and stability of the straw and organic material application scheme, is an urgent problem to be solved. Summary of the Invention

[0006] In view of this, the present invention aims to propose a soil fertility optimization method that integrates straw return to the field and organic material addition, in order to solve the problem that the existing soil fertility ratio algorithm only makes linear balance decisions based on the static total nitrogen supply throughout the growth period, which makes it difficult to identify the early effective nitrogen deficit and inaccurate application rate caused by microbial competition for nitrogen during the critical seedling stage.

[0007] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0008] A soil fertility improvement and optimization method integrating straw return to the field and organic material addition, the method comprising:

[0009] Step S1: Collect and preprocess data on the release kinetic parameters of straw and organic materials and the physical characteristics of the topsoil microenvironment in the target farmland plot to obtain a basic dataset for soil fertility ratio.

[0010] Step S2: By performing an integral gap assessment on the time misalignment characteristics of carbon and nitrogen release rate kinetics in the soil fertility ratio basic dataset, the carbon and nitrogen kinetic misalignment penalty factor is obtained.

[0011] Step S3: By evaluating the spatial synergistic compensation characteristics of the microenvironment in the topsoil space in the soil fertility ratio basic dataset, the microenvironment synergistic compensation factor is obtained.

[0012] Step S4: Obtain the optimized dynamic effective nitrogen supply by nonlinearly boundedly coupling the carbon-nitrogen kinetic misalignment penalty factor, the microenvironment synergistic compensation factor, and the basic static nitrogen supply.

[0013] Step S5: Obtain the straw and organic material application scheme by performing constraint solution and cost minimization on the optimized dynamic effective nitrogen supply.

[0014] Furthermore, the process involves collecting and preprocessing data on the release kinetics of straw and organic materials and the physical characteristics of the topsoil microenvironment in the target farmland plots to obtain a basic dataset for soil fertility improvement, including:

[0015] For any target farmland plot where a soil fertility improvement plan is to be implemented, the instantaneous carbon release rate of the planned straw biomass under standard conditions is obtained through agricultural basic databases, field in-situ culture test records, or indoor constant temperature culture test records, and the instantaneous nitrogen release rate of the planned organic materials under standard conditions is also obtained; through soil microbial culture databases or soil sample culture test results from the target farmland plot, the microbial nitrogen assimilation equivalent data corresponding to the effective carbon assimilation unit of soil microorganisms is obtained.

[0016] Based on the target crop type, sowing time, and agronomic management standards for the current planting season, determine the boundary data of the critical seedling stage time window for the target crop; obtain the theoretical absolute nitrogen requirement of the target farmland plot during the critical seedling stage time window through the target crop fertilizer requirement model, regional agronomic database, or historical yield management records of the target farmland plot, and obtain the theoretical total nitrogen requirement target for the entire growth period of the target crop;

[0017] By using actual harvest data of previous crops, straw residue survey data, and straw-to-grain ratio conversion data, the dry matter mass of straw returned to the field per unit area of ​​the target farmland is obtained; by using straw physicochemical test reports, agricultural production material databases, or standard parameter tables corresponding to straw types, the standard nominal nitrogen content of straw and the standard mineralization rate of straw throughout its growth period are obtained; by using organic matter physicochemical test reports, commercial organic fertilizer test labels, or standard parameter tables corresponding to organic material types, the standard nominal nitrogen content of organic materials and the standard mineralization rate of organic materials throughout its growth period are obtained, and the dry matter mass of organic materials applied per unit area of ​​the target farmland is set as the application decision variable to be solved;

[0018] The actual mixing depth of straw after mechanical crushing is obtained by using airborne depth sensors, on-vehicle depth sensing nodes of agricultural machinery, or data recorded in tillage operations. Along the vertical soil profile corresponding to the actual mixing depth, the length of straw fragments after mechanical crushing in different depth ranges is statistically analyzed using agricultural machinery operation image recognition devices, soil profile sampling and screening devices, or straw fragment length detection devices. The actual mechanical crushing length data of straw at different depths in the vertical soil profile is obtained, and the standard crushing length data of straw is obtained from agricultural machinery operation specifications or straw returning operation standards.

[0019] By deploying a moisture sensor array within the topsoil of the target farmland, actual volumetric moisture content data of the soil at different depths are collected at preset depth intervals; and by using soil texture test results of the target farmland, regional soil database, or microbial activity test results, the optimal volumetric moisture content data of microorganisms corresponding to the soil texture of the target farmland is obtained.

[0020] Outlier removal, missing value imputation, unit unification, and time or spatial scale alignment were performed on the following data: instantaneous carbon release rate of straw, instantaneous nitrogen release rate of organic materials, microbial nitrogen assimilation equivalent, critical seedling stage time window boundary data, theoretical absolute nitrogen requirement at critical seedling stages, theoretical total nitrogen requirement target, dry matter mass of straw returned to the field per unit area, standard nominal nitrogen content of straw, nominal mineralization rate of straw throughout its growth period, standard nominal nitrogen content of organic materials, nominal mineralization rate of organic materials throughout its growth period, actual mixing depth of straw returned to the field, actual mechanical crushing length of straw, standard crushing length of straw, actual volumetric moisture content of soil, and optimal volumetric moisture content of microorganisms. The above data, along with the application decision variables to be solved, were used as the basic dataset for soil fertility improvement and allocation.

[0021] Furthermore, the step of obtaining a carbon and nitrogen kinetic misalignment penalty factor by performing an integral gap assessment on the temporal misalignment characteristics of carbon and nitrogen release rate kinetics in the soil fertility ratio baseline dataset includes:

[0022] By coupling and converting the instantaneous carbon release rate data of straw and the nitrogen assimilation equivalent data of microorganisms in the basic dataset of soil fertility ratio, the theoretical nitrogen flux data of key seedling stage microorganisms were obtained.

[0023] By performing integral gap normalization on the theoretical nitrogen flux data of key seedling stage microorganisms and the instantaneous nitrogen release rate data of organic materials, the carbon-nitrogen kinetic misalignment penalty factor was obtained.

[0024] Furthermore, by coupling and converting the instantaneous carbon release rate data of straw with the microbial nitrogen assimilation equivalent data in the soil fertility ratio basic dataset, the theoretical nitrogen flux data of key seedling stage microorganisms is obtained, including:

[0025] For any target farmland plot, extract the straw instantaneous carbon release rate data, microbial nitrogen assimilation equivalent data, and key seedling stage time window boundary data corresponding to the target farmland plot from the soil fertility ratio basic dataset;

[0026] Based on the boundary data of the critical seedling stage time window, the critical seedling stage time window corresponding to the target farmland plot is determined, and multiple target time points are set within the critical seedling stage time window according to the preset time interval.

[0027] For any target time point, extract the instantaneous carbon release rate value of straw corresponding to the target time point from the instantaneous carbon release rate data, and use the calculation result of multiplying the instantaneous carbon release rate value of straw corresponding to the target time point with the microbial nitrogen assimilation equivalent data as the theoretical nitrogen flux value of key seedling microorganisms corresponding to the target time point.

[0028] Arrange the theoretical nitrogen flux values ​​of microorganisms at all target time points within the critical seedling stage time window in chronological order to obtain the theoretical nitrogen flux data of microorganisms at the critical seedling stage for the target farmland plots.

[0029] Furthermore, the method of obtaining the carbon-nitrogen kinetic misalignment penalty factor by performing integral gap normalization on the theoretical nitrogen flux data of key seedling stage microorganisms and the instantaneous nitrogen release rate data of organic materials includes:

[0030] For any target farmland plot, extract the instantaneous nitrogen release rate data of organic matter, the boundary data of key seedling stage time window, and the theoretical absolute nitrogen requirement of key seedling stage from the soil fertility ratio basic dataset, and obtain the theoretical nitrogen flux data of microorganisms in the key seedling stage corresponding to the target farmland plot.

[0031] Based on the boundary data of the critical seedling stage time window, the critical seedling stage time window corresponding to the target farmland plot is determined, and multiple target time points are set within the critical seedling stage time window according to the preset time interval.

[0032] For any target time point, extract the theoretical nitrogen flux value of key seedling microorganisms corresponding to the target time point from the theoretical nitrogen flux data of key seedling microorganisms, and extract the instantaneous nitrogen release rate value of organic matter corresponding to the target time point from the instantaneous nitrogen release rate data of organic matter.

[0033] The difference between the theoretical nitrogen flux requirement of key seedling microorganisms at the target time point and the instantaneous nitrogen release rate of organic matter is used as the instantaneous retention gap assessment at the target time point. When the instantaneous retention gap assessment is less than a constant 0, the effective retention gap value at the target time point is set to a constant 0; when the instantaneous retention gap assessment is greater than or equal to a constant 0, the instantaneous retention gap assessment at the target time point is used as the effective retention gap value at the target time point.

[0034] Time integration is performed on the effective retention gap values ​​corresponding to all target time points within the critical seedling stage time window to obtain carbon and nitrogen kinetic integral gap data for the target farmland plots.

[0035] The carbon-nitrogen kinetic integral gap data is divided by the calculated theoretical absolute nitrogen requirement at the critical seedling stage as the relative retention gap assessment for the target farmland plot;

[0036] The result of adding constant 1 to the relative holding gap assessment is used as the holding gap amplification denominator, and the reciprocal of the holding gap amplification denominator is used as the carbon and nitrogen kinetic misalignment penalty factor corresponding to the target farmland plot.

[0037] Furthermore, the step of evaluating the spatial synergistic compensation of the microenvironmental physical coupling characteristics of the topsoil spatial microenvironment in the soil fertility ratio baseline dataset to obtain microenvironmental synergistic compensation factors includes:

[0038] By performing relative enhancement evaluation on the specific surface area of ​​actual mechanical crushing length data and standard crushing length data of straw in the soil fertilization ratio basic dataset, we obtained characterization data of accelerated straw crushing.

[0039] By performing water suitability constraint mapping on the distribution data of actual soil volumetric water content and the optimal volumetric water content of microorganisms in the basic dataset of soil fertility improvement ratio, water suitability characterization data is obtained.

[0040] By processing the data on accelerated straw crushing and moisture suitability using the mean of the tillage depth, a microenvironment synergistic compensation factor was obtained.

[0041] Furthermore, the method involves performing a relative enhancement evaluation of the specific surface area of ​​the actual mechanically crushed straw length data and the standard crushed straw length data in the soil fertility ratio dataset to obtain straw crushing acceleration characterization data, including:

[0042] For any target farmland plot, extract the actual return-to-field tillage mixing depth, actual mechanical crushing length of straw, and standard crushing length of straw corresponding to the target farmland plot from the soil fertility ratio basic dataset;

[0043] Based on the actual depth of the tillage mixing, the range of the tillage depth corresponding to the target farmland plot is determined, and multiple target depth points are set within the tillage depth range according to the preset depth interval.

[0044] For any target depth point, extract the actual mechanical crushing length value of straw corresponding to the target depth point from the actual mechanical crushing length data of straw, and extract the standard crushing length value of straw corresponding to the target farmland plot from the standard crushing length data of straw.

[0045] The standard crushing length value of straw corresponding to the target depth point is used as the numerator, the actual mechanical crushing length value of straw corresponding to the target depth point is used as the denominator, and the calculation result of the corresponding fraction is used as the straw crushing acceleration characterization value corresponding to the target depth point.

[0046] Arrange the straw crushing acceleration characterization values ​​corresponding to all target depth points within the tillage depth range in depth order to obtain the straw crushing acceleration characterization data corresponding to the target farmland plot.

[0047] Furthermore, the step of performing water suitability characterization data by mapping the actual volumetric water content distribution data of soil in the soil fertility ratio basic dataset with the optimal volumetric water content data for microorganisms includes:

[0048] For any target farmland plot, extract the actual soil returning and tillage mixing depth, actual soil volumetric moisture content distribution data, and microbial optimal volumetric moisture content data from the soil fertilization ratio basic dataset.

[0049] Based on the actual depth of the tillage mixing, the range of the tillage depth corresponding to the target farmland plot is determined, and multiple target depth points are set within the tillage depth range according to the preset depth interval.

[0050] For any target depth point, extract the actual volumetric moisture content value of the soil corresponding to the target depth point from the actual volumetric moisture content distribution data of the soil, and extract the optimal volumetric moisture content value of the microorganism corresponding to the target farmland plot from the optimal volumetric moisture content data of the microorganism.

[0051] The difference between the actual volumetric moisture content of the soil at the target depth point and the optimal volumetric moisture content of the microorganism is used as the moisture deviation assessment at the target depth point, and the square of the moisture deviation assessment is used as the moisture deviation intensity assessment at the target depth point.

[0052] The result of multiplying the square of the optimal volumetric water content of microorganisms by a constant 2 is used as the normalized denominator of water suitability. The result of dividing the water deviation intensity assessment by the negative of the normalized denominator of water suitability is subjected to exponential mapping with the natural constant as the base. The corresponding mapping result is used as the water suitability characterization value corresponding to the target depth point.

[0053] The water suitability characterization values ​​corresponding to all target depth points within the tillage depth range are arranged in order of depth to obtain the water suitability characterization data corresponding to the target farmland plots.

[0054] Furthermore, the process of obtaining the microenvironment synergistic compensation factor by processing the straw crushing acceleration characterization data and moisture suitability characterization data through the mean of the tillage depth includes:

[0055] For any target farmland plot, extract the actual incorporation and mixing depth of the target farmland plot from the soil fertility ratio basic dataset, and obtain the straw crushing acceleration characterization data and water suitability characterization data of the target farmland plot.

[0056] Based on the actual depth of the tillage mixing, the range of the tillage depth corresponding to the target farmland plot is determined, and multiple target depth points are set within the tillage depth range according to the preset depth interval.

[0057] For any target depth point, extract the straw crushing acceleration characterization value corresponding to the target depth point from the straw crushing acceleration characterization data, and extract the moisture suitability characterization value corresponding to the target depth point from the moisture suitability characterization data.

[0058] The result of multiplying the straw crushing acceleration characterization value and the moisture suitability characterization value corresponding to the target depth point is taken as the depth microenvironment synergistic characterization value corresponding to the target depth point.

[0059] The depth microenvironment collaborative characterization values ​​corresponding to all target depth points within the tillage depth range are processed by tillage depth integration to obtain the microenvironment collaborative integration data corresponding to the target farmland plots.

[0060] The result of dividing the microenvironment synergy integral data by the actual mixing depth of tillage is used as the mean microenvironment synergy data for the target farmland plot.

[0061] The result of adding constant 1 to the mean value of the microenvironment synergy is used as the microenvironment synergy compensation factor for the target farmland plot.

[0062] Furthermore, the method of obtaining the optimized dynamic effective nitrogen supply by nonlinearly boundedly coupling the carbon-nitrogen kinetic misalignment penalty factor, the microenvironment synergistic compensation factor, and the basic static nitrogen supply includes:

[0063] For any target farmland plot, extract the unit area dry matter of straw return to the field, the standard nominal nitrogen content of straw, the standard mineralization rate of straw throughout the growth period, the standard nominal nitrogen content of organic materials, the standard mineralization rate of organic materials throughout the growth period, and the application decision variables to be solved from the soil fertility ratio basic dataset. Also obtain the carbon and nitrogen kinetic misalignment penalty factor and the microenvironment synergistic compensation factor corresponding to the target farmland plot.

[0064] The result of multiplying the dry matter mass of straw returned to the field per unit area, the standard nominal nitrogen content of straw, and the nominal mineralization rate of straw throughout the entire growth period is used as the basic static nitrogen supply of straw for the target farmland plot.

[0065] The calculation result of multiplying the application decision variable to be solved, the standard nominal nitrogen content of organic materials, and the nominal mineralization rate of organic materials throughout the entire growth period is used as the basic static nitrogen supply of organic materials for the target farmland plot.

[0066] The calculation result of adding the basic static nitrogen supply of straw and the basic static nitrogen supply of organic materials is taken as the basic static nitrogen supply corresponding to the target farmland plot;

[0067] The difference between constant 1 and the carbon-nitrogen kinetic misalignment penalty factor is used as the assessment of the recoverable nitrogen supply loss corresponding to the target farmland plot;

[0068] The difference between the microenvironment synergistic compensation factor and the constant 1 is used as the assessment of the microenvironment compensation intensity corresponding to the target farmland plot. The microenvironment compensation intensity assessment is then processed by hyperbolic tangent mapping to obtain the bounded microenvironment recovery coefficient corresponding to the target farmland plot.

[0069] The result of multiplying the recoverable nitrogen supply loss assessment by the bounded microenvironment recovery coefficient is used as the nitrogen supply recovery compensation assessment for the target farmland plot.

[0070] The calculation result of adding the carbon and nitrogen kinetic misalignment penalty factor to the nitrogen supply recovery compensation assessment is used as the dynamic effective nitrogen supply correction coefficient corresponding to the target farmland plot;

[0071] The result of multiplying the basic static nitrogen supply by the dynamic effective nitrogen supply correction coefficient is taken as the optimized dynamic effective nitrogen supply for the target farmland plot.

[0072] Compared with the prior art, the present invention has the following advantages:

[0073] This invention discloses a soil fertility optimization method integrating straw return to the field and organic material addition. By introducing the dynamic constraint relationship between the instantaneous carbon release rate of straw, the instantaneous nitrogen release rate of organic materials, the nitrogen assimilation equivalent of microorganisms, and the nitrogen requirements of crops during key seedling stages, it transforms the static nitrogen supply calculation in traditional fertilization ratios, which relies solely on nominal nitrogen content and mineralization rate throughout the growth period, into a dynamic and effective nitrogen supply assessment that reflects the timing misalignment of carbon and nitrogen release. This allows for the early identification of net nitrogen retention gaps caused by the large-scale assimilation of environmental nitrogen by microorganisms in the early stages of straw return during the formulation of the application plan. This avoids situations where theoretical total nitrogen supply is sufficient but actual effective nitrogen is insufficient during the seedling stage, thereby reducing the risk of yellowing seedlings, weak seedlings, and early growth retardation, and improving the adaptability of the straw return to the field and organic material application plan to the nutrient requirements of crops during key growth stages. Meanwhile, this invention further combines the actual mechanical crushing length of straw, standard crushing length, mixing depth of the topsoil, distribution of soil volumetric moisture content, and optimal volumetric moisture content for microorganisms to quantify and compensate for the combined effect of physical crushing and moisture conditions on straw degradation rate. It also corrects the basic static nitrogen supply through a nonlinear bounded correction method. This treatment avoids underestimating the demand for organic materials by neglecting microbial nitrogen competition, and prevents excessive amplification of nitrogen deficiency risks in fields with sufficient straw crushing and suitable topsoil moisture. This ensures the final application rate better matches the actual degradation environment in the field, thereby reducing the blind over-input of organic materials, lowering agricultural production costs, and mitigating the risks of leaching and non-point source pollution caused by subsequent nitrogen redundancy. Attached Figure Description

[0074] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0075] Figure 1 This is a flowchart illustrating a soil fertility optimization method that integrates straw return to the field with the addition of organic materials, as described in an embodiment of the present invention. Detailed Implementation

[0076] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0077] See Figure 1 This is a flowchart of a soil fertility optimization method that integrates straw return to the field and organic material addition, as provided in Embodiment 1 of the present invention. Figure 1 As shown, a soil fertility optimization method that integrates straw return to the field with the addition of organic materials may include:

[0078] Step S1 involves collecting and preprocessing data on the release kinetics of straw and organic materials and the physical characteristics of the topsoil microenvironment in the target farmland plot to obtain a basic dataset for soil fertility ratio.

[0079] First, for any target farmland plot where a soil fertility improvement plan is to be implemented, data on the instantaneous carbon release rate of the planned applied straw biomass under standard conditions are obtained through agricultural basic databases, field in-situ culture test records, or indoor constant temperature culture test records. Data on the instantaneous nitrogen release rate of the planned organic materials under standard conditions is also obtained. Data on the microbial nitrogen assimilation equivalent per unit of available carbon assimilated by soil microorganisms is obtained through soil microbial culture databases or soil sample culture test results from the target farmland plots. Based on the target crop type, sowing time, and agronomic management standards for the current planting season, the boundary data of the critical seedling stage time window for the target crop are determined. Through the target crop nutrient requirement model, regional agronomic database, or historical yield management records of the target farmland plots, the theoretical absolute nitrogen requirement at the critical seedling stage within the critical seedling stage time window is obtained, along with the theoretical total nitrogen requirement target for the entire growth period of the target crop. Through actual harvest data of the previous crop, straw residue survey data, and straw-to-grain ratio conversion data, the dry matter mass of straw returned to the field per unit area of ​​the target farmland plot is obtained. Obtain the standard nominal nitrogen content and nominal mineralization rate of straw throughout its entire growth period using straw physicochemical test reports, agricultural production material databases, or standard parameter tables corresponding to straw types. Similarly, obtain the standard nominal nitrogen content and nominal mineralization rate of organic materials throughout their entire growth period using organic matter physicochemical test reports, commercial organic fertilizer test labels, or standard parameter tables corresponding to organic material types. Set the dry matter mass of organic materials applied per unit area of ​​the target farmland plot as the decision variable to be solved.

[0080] The actual mixing depth of straw in the target farmland is obtained through airborne depth sensors, on-vehicle depth sensing nodes, or tillage operation records. Along the vertical soil profile corresponding to the actual mixing depth, agricultural machinery operation image recognition devices, soil profile sampling and screening devices, or straw fragment length detection devices are used to statistically analyze the length of mechanically crushed straw fragments at different depth intervals. This yields data on the actual mechanically crushed straw length at different depths in the soil vertical profile, and standard straw crushing length data is obtained from agricultural machinery operation specifications or straw return operation standards.

[0081] A moisture sensor array deployed within the topsoil of the target farmland plot is used to collect actual volumetric moisture content data at different depths at preset intervals. Based on soil texture testing results, regional soil databases, or microbial activity test results, the optimal volumetric moisture content data corresponding to the soil texture of the target farmland plot is obtained.

[0082] Outlier removal, missing value imputation, unit unification, and time or spatial scale alignment were performed on the following data: instantaneous carbon release rate of straw, instantaneous nitrogen release rate of organic materials, microbial nitrogen assimilation equivalent, critical seedling stage time window boundary data, theoretical absolute nitrogen requirement at critical seedling stages, theoretical total nitrogen requirement target, dry matter mass of straw returned to the field per unit area, standard nominal nitrogen content of straw, nominal mineralization rate of straw throughout its growth period, standard nominal nitrogen content of organic materials, nominal mineralization rate of organic materials throughout its growth period, actual mixing depth of straw returned to the field, actual mechanical crushing length of straw, standard crushing length of straw, actual volumetric moisture content of soil, and optimal volumetric moisture content of microorganisms. The above data, along with the application decision variables to be solved, were used as the basic dataset for soil fertility improvement and allocation.

[0083] Thus, the basic dataset for soil fertility ratio was obtained by collecting and preprocessing data on the release kinetic parameters of straw and organic materials and the physical characteristics of the topsoil microenvironment in the target farmland plots.

[0084] Step S2: By performing an integral gap assessment on the time misalignment characteristics of carbon and nitrogen release rate kinetics in the soil fertility ratio basic dataset, the carbon and nitrogen kinetic misalignment penalty factor is obtained.

[0085] To quantify the actual weakening effect of early-stage microbial fixation on effective nitrogen supply at the algorithmic level, this method abandons the traditional approach of using a single static C / N ratio. Instead, it introduces cumulative carbon release kinetics models for straw and cumulative nitrogen release kinetics models for organic fertilizer to extract their release rates within the critical seedling stage time window. By calculating the integral area difference between the theoretical effective nitrogen flux required for microbial degradation of straw and the actual effective nitrogen flux released by organic fertilizer and soil within this specific time window, the true nitrogen hijacking gap in the early stage can be objectively and dynamically quantified. Based on this integral gap characteristic, a carbon-nitrogen kinetic misalignment penalty factor is constructed to dynamically suppress the total theoretical effective nitrogen calculated by existing algorithms, forcing the allocation algorithm to identify the risk of early-stage nutrient deficit.

[0086] In summary, this invention first couples and converts the instantaneous carbon release rate data of straw and the nitrogen assimilation equivalent data of microorganisms in the soil fertility ratio dataset to obtain the theoretical nitrogen flux requirement of microorganisms during the key seedling stage. Specifically, for any target farmland plot, the instantaneous carbon release rate data of straw, the nitrogen assimilation equivalent data of microorganisms, and the boundary data of the key seedling stage time window corresponding to the target farmland plot are extracted from the soil fertility ratio dataset. Based on the boundary data of the key seedling stage time window, the key seedling stage time window corresponding to the target farmland plot is determined, and multiple target time points are set at preset time intervals within the key seedling stage time window. For any target time point, the instantaneous carbon release rate value of straw corresponding to the target time point is extracted from the instantaneous carbon release rate data of straw, and the result of multiplying the instantaneous carbon release rate value of straw corresponding to the target time point with the nitrogen assimilation equivalent data of microorganisms is used as the theoretical nitrogen flux requirement of microorganisms during the key seedling stage corresponding to the target time point. Arrange the theoretical nitrogen flux values ​​of microorganisms at all target time points within the critical seedling stage time window in chronological order to obtain the theoretical nitrogen flux data of microorganisms at the critical seedling stage for the target farmland plots.

[0087] After obtaining the theoretical nitrogen flux data of microorganisms during the critical seedling stage, the carbon-nitrogen kinetic misalignment penalty factor is obtained by performing integral gap normalization on the theoretical nitrogen flux data of microorganisms during the critical seedling stage and the instantaneous nitrogen release rate data of organic matter. Specifically, for any target farmland plot, the instantaneous nitrogen release rate data of organic matter, the boundary data of the critical seedling stage time window, and the theoretical absolute nitrogen requirement of the critical seedling stage are extracted from the soil fertility ratio basic dataset for the target farmland plot, and the theoretical nitrogen flux data of microorganisms during the critical seedling stage for the target farmland plot are obtained. Based on the boundary data of the critical seedling stage time window, the critical seedling stage time window for the target farmland plot is determined, and multiple target time points are set within the critical seedling stage time window at preset time intervals. For any target time point, the theoretical nitrogen flux value of microorganisms during the critical seedling stage is extracted from the theoretical nitrogen flux data of microorganisms during the critical seedling stage, and the instantaneous nitrogen release rate value of organic matter is extracted from the instantaneous nitrogen release rate data of organic matter for the target time point. The difference between the theoretical nitrogen flux requirement of microorganisms at the critical seedling stage and the instantaneous nitrogen release rate of organic matter at the target time point is used as the instantaneous retention gap assessment at the target time point. When the instantaneous retention gap assessment is less than a constant 0, the effective retention gap value at the target time point is set to a constant 0; when the instantaneous retention gap assessment is greater than or equal to a constant 0, the instantaneous retention gap assessment at the target time point is used as the effective retention gap value at the target time point. Time integration is performed on the effective retention gap values ​​at all target time points within the critical seedling stage time window to obtain the carbon and nitrogen kinetic integral gap data for the target farmland plots. The carbon and nitrogen kinetic integral gap data divided by the calculated theoretical absolute nitrogen requirement at the critical seedling stage is used as the relative retention gap assessment for the target farmland plots. The result of adding a constant 1 to the relative retention gap assessment is used as the retention gap amplification denominator, and the reciprocal of the retention gap amplification denominator is used as the carbon and nitrogen kinetic misalignment penalty factor for the target farmland plots.

[0088] In one embodiment, it is assumed that the time variable after the organic material and straw are applied to the soil is... The critical seedling stage time window boundary is ;No. The instantaneous carbon release rate of straw at that moment was [value missing]. Microbial nitrogen assimilation equivalent data are as follows: ;No. The instantaneous nitrogen release rate of organic matter at that moment was [value missing]. ;No. The theoretical absolute nitrogen requirement at the critical seedling stage for each plot is: Then the first The formula for calculating the carbon and nitrogen kinetic dislocation penalty factor for each plot is as follows:

[0089]

[0090] in, Indicates the first Carbon and nitrogen kinetic dislocation penalty factor for each plot; This represents data on microbial nitrogen assimilation equivalents. Indicates the first The instantaneous carbon release rate of straw at a given moment; Indicates the first The instantaneous nitrogen release rate of organic materials at a given moment; Indicates the first Theoretical absolute nitrogen requirement at the critical seedling stage in each plot; Represents the maximum value function; This indicates the boundary of the critical seedling stage time window.

[0091] It should be noted that in the calculation expression of the carbon-nitrogen kinetic dislocation penalty factor, the upper limit of integration... This represents the boundary of the critical seedling stage time window for the target crop as defined by agronomic standards. The size of this time window is not a hyperparameter, but a specific number of days threshold determined based on the objective agronomic physiological period of the target crop variety (e.g., for winter wheat, this window is typically set to the period before sowing). The day, usually set as the front for rice. (day). This formula uses the release rate function in... The definite integral calculation within this time domain anchors the fragile growth period that is highly susceptible to yield reduction due to nutrient depletion. This characterizes the absolute nitrogen demand dynamics of microorganisms during the decomposition of straw and release of carbon, in order to maintain their own cell proliferation. The integral term calculates the nitrogen demand during key time windows. Within this context, the net area of ​​difference formed by the nitrogen demand curve of microorganisms being higher than that formed by the nitrogen supply curve of organic fertilizer is used. The operator ensures that the algorithm only accumulates the actual persistent gap caused by supply shortages, ignoring safe periods of supply exceeding demand. A larger integral area indicates more severe microbial nitrogen competition in the early stages. To make this absolute gap dimensionless and directly participate in subsequent algorithmic regulation, this step divides the gap integral by the actual nitrogen requirement of the crop at this initial growth stage. This allows for the calculation of the relative disruptive weight of local shortages relative to growth requirements. Finally, through... The algebraic structure is used to map this weight, thus constructing the dynamic penalty factor. When the two curves are perfectly synchronized and without misalignment, the integral term is zero. It automatically degenerates to 1, retaining the original proportions; however, when severe misalignment leads to a surge in the gap, the denominator increases nonlinearly, resulting in... It is strictly compressed. This factor design based on the time continuity calculus mechanism corrects the shortcomings of existing algorithms that only consider the static total amount over the whole period, thus implementing precise punishment for the implicit short-term nutrient cliff risk in advance during the ratio calculation stage.

[0092] Thus, the carbon and nitrogen kinetic misalignment penalty factor was obtained by evaluating the time misalignment characteristics of carbon and nitrogen release rate kinetics in the basic dataset of soil fertility ratios through integral gap assessment.

[0093] Step S3: Spatial collaborative compensation factor is obtained by evaluating the physical coupling characteristics of the microenvironment in the topsoil space in the soil fertility ratio basic dataset.

[0094] While step S2 quantifies the retention gap in the biological dimension through kinetic integration, in actual agricultural production, the mechanical crushing quality of straw before returning it to the field and the moisture content of the soil subsoil significantly alter the spatial microenvironment of the degradation process from a physical perspective. If straw is pulverized into fine fragments with high intensity, its specific surface area will increase exponentially; if a suitable volumetric moisture content is maintained within the topsoil layer simultaneously, the attachment and colonization efficiency of microorganisms will be significantly improved. This positive coupling effect of the physical microenvironment can accelerate the early consumption of carbon in real-world scenarios, thereby effectively shortening or even partially offsetting the retention window calculated in step S2. Without considering this acceleration mechanism, relying solely on the rigid penalty of step S2 will lead the fertilization algorithm to overestimate the early nitrogen deficiency risk, resulting in overly conservative decisions, such as instructing the application of excessive organic fertilizer. This not only wastes agricultural costs but also causes excessive nutrient redundancy in the later stages. Therefore, it is necessary to construct a microenvironment synergistic compensation factor by obtaining the actual crushed particle size characteristics and moisture content distribution characteristics of straw at the vertical tillage depth, and to perform elastic relaxation correction of the rigid penalty of the carbon and nitrogen kinetic misalignment penalty factor in accordance with the objective degradation law.

[0095] In summary, this invention first performs a relative enhancement assessment of the specific surface area of ​​actual mechanically crushed straw length data and standard crushed straw length data from the soil fertility ratio dataset to obtain straw crushing acceleration characterization data. Specifically, for any target farmland plot, the actual straw return-to-field mixing depth, actual mechanically crushed straw length data, and standard crushed straw length data corresponding to the target farmland plot are extracted from the soil fertility ratio dataset. Based on the actual straw return-to-field mixing depth, the range of tillage depth corresponding to the target farmland plot is determined, and multiple target depth points are set within the tillage depth range according to preset depth intervals. For any target depth point, the actual mechanically crushed straw length value corresponding to the target depth point is extracted from the actual mechanically crushed straw length data, and the standard crushed straw length value corresponding to the target farmland plot is extracted from the standard crushed straw length data. The standard crushed straw length value corresponding to the target depth point is used as the numerator, and the actual mechanically crushed straw length value corresponding to the target depth point is used as the denominator. The calculation result of the corresponding fraction is used as the straw crushing acceleration characterization value corresponding to the target depth point. Arrange the straw crushing acceleration characterization values ​​corresponding to all target depth points within the tillage depth range in depth order to obtain the straw crushing acceleration characterization data corresponding to the target farmland plot.

[0096] After obtaining the characterization data for accelerated straw crushing, the soil moisture content distribution data and the optimal volumetric moisture content data for microorganisms in the soil fertility ratio dataset were further processed using water suitability constraint mapping to obtain water suitability characterization data. Specifically, for any target farmland plot, the actual incorporation and tillage mixing depth, actual soil moisture content distribution data, and optimal volumetric moisture content data for microorganisms were extracted from the soil fertility ratio dataset. Based on the actual incorporation and tillage mixing depth, the range of topsoil depth corresponding to the target farmland plot was determined, and multiple target depth points were set within the topsoil depth range according to preset depth intervals. For any target depth point, the actual soil moisture content value corresponding to the target depth point was extracted from the actual soil moisture content distribution data, and the optimal volumetric moisture content value for microorganisms corresponding to the target farmland plot was extracted from the optimal volumetric moisture content data for microorganisms. The difference between the actual volumetric moisture content of the soil at the target depth point and the optimal volumetric moisture content for microorganisms is used as the moisture deviation assessment for the target depth point. The square of the moisture deviation assessment is used as the moisture deviation intensity assessment for the target depth point. The result of multiplying the square of the optimal volumetric moisture content for microorganisms by a constant 2 is used as the normalized denominator for moisture suitability. The moisture deviation intensity assessment is divided by the negative of the normalized denominator for moisture suitability and then subjected to exponential mapping with the natural constant as the base. The corresponding mapping result is used as the moisture suitability characterization value for the target depth point. The moisture suitability characterization values ​​for all target depth points within the tillage depth range are arranged in depth order to obtain the moisture suitability characterization data for the target farmland plots.

[0097] After obtaining the water suitability characterization data, the microenvironment synergistic compensation factor was obtained by integrating the straw crushing acceleration characterization data and the water suitability characterization data with the tillage depth. Specifically, for any target farmland plot, the actual straw return and tillage mixing depth corresponding to the target farmland plot was extracted from the soil fertility ratio basic dataset, and the corresponding straw crushing acceleration characterization data and water suitability characterization data were obtained. Based on the actual straw return and tillage mixing depth, the tillage depth range corresponding to the target farmland plot was determined, and multiple target depth points were set within the tillage depth range according to preset depth intervals. For any target depth point, the straw crushing acceleration characterization value corresponding to the target depth point was extracted from the straw crushing acceleration characterization data, and the water suitability characterization value corresponding to the target depth point was extracted from the water suitability characterization data. The result of multiplying the straw crushing acceleration characterization value and the water suitability characterization value corresponding to the target depth point was used as the depth microenvironment synergistic characterization value corresponding to the target depth point. The tillage depth was integrated with the depth microenvironment synergistic characterization values ​​corresponding to all target depth points within the tillage depth range to obtain the microenvironment synergistic integral data corresponding to the target farmland plot. The calculated result of dividing the microenvironment synergy integral data by the actual mixing depth of tillage is used as the mean microenvironment synergy data for the target farmland plot. The calculated result of adding the constant 1 to the mean microenvironment synergy data is used as the microenvironment synergy compensation factor for the target farmland plot.

[0098] In one implementation, assume the first The actual depth of tillage mixing in each plot is: The standard straw crushing length data is as follows: The depth variable of the soil vertical profile is ; in depth The statistical median value of the actual mechanically crushed length of straw at the location is ;depth The actual volumetric water content of the soil at that location is The optimal volumetric moisture content for microorganisms is as follows: Then the first The calculation expression for the microenvironmental synergistic compensation factor of each plot is as follows:

[0099]

[0100] in, Indicates the first Microenvironmental synergistic compensation factors for individual plots; Indicates the first The actual depth of soil mixing during tillage in each plot; This indicates the standard crushing length of straw. Indicates depth The statistical median of the actual mechanically shredded length of straw at the location; Indicates depth The actual volumetric moisture content of the soil at that location; This represents the optimal volumetric moisture content data for microorganisms; This represents an exponential function with the natural constant e as the base.

[0101] It should be noted that in this formula, This characterizes the relative increase in the specific surface area of ​​straw caused by mechanical crushing. (In actual crushing length...) Less than standard length When this ratio is greater than 1, it objectively reflects the potential for increased straw contact area to physically accelerate microbial attachment and degradation processes. Simultaneously, the formula introduces... As a moisture constraint term, this term is adopted in Gaussian form, taking the optimal moisture content as the basis, since microbial activity is highly sensitive to moisture changes. Constrain the center. When the depth... Actual moisture content at the location Serious deviation When the soil is either too dry or too wet, this index term approaches zero, indicating that even if the straw is finely pulverized, a lack of suitable moisture conditions will limit microbial activity; only when... Close Only then does this term approach 1, allowing the acceleration potential of physical crushing to be unleashed. This is achieved by working along the actual tillage depth. Integrate the product of the two features mentioned above and calculate the mean. The formula effectively quantifies the overall scale of the suitable microenvironment for degradation within the entire three-dimensional straw return space. When the straw pulverization degree is high and the moisture content is suitable within the overall tillage layer, the integral value increases, driving the compensation factor. A moderate increase is appropriate; conversely, if the grinding is coarse or the moisture conditions are poor, the integral value will be extremely small. Approaching 1. This design objectively recreates the corrective effect of the actual degradation environment on the theoretical dynamic process by coupling physical and environmental data in the spatial dimension, thereby avoiding the problem of algorithm overcompensation caused by a single penalty factor.

[0102] Thus, the spatial synergistic compensation factor was obtained by evaluating the physical coupling characteristics of the microenvironment in the topsoil space of the soil fertility ratio basic dataset.

[0103] Step S4: By performing nonlinear bounded coupling correction on the carbon-nitrogen kinetic misalignment penalty factor, the microenvironment synergistic compensation factor, and the basic static nitrogen supply, the optimized dynamic effective nitrogen supply is obtained.

[0104] Existing soil fertility formulation decision-making algorithms are typically based on a linear superposition model of stoichiometric equilibrium throughout the entire growth period. The conventional theoretical nitrogen supply constraint equation is expressed as follows: the planned total amount of straw returned to the field and the total amount of organic fertilizer applied per unit area are multiplied by their fixed nominal nitrogen content and the total mineralization rate over the entire growth period, respectively, and then the two are directly added as scalar values. The algorithm adjusts the amount of organic material applied. This results in a theoretical total nitrogen supply equal to or slightly greater than the crop's total nitrogen requirement over its entire lifespan. However, this static summation method masks a significant retention gap in the early stages. To correct this deficiency, this step will use the obtained carbon-nitrogen kinetic misalignment penalty factor. Synergistic Compensation Factors with Microenvironment This approach applies a nonlinear bounded coupling mechanism to the theoretical nitrogen supply of existing technologies. By utilizing the kinetic decay within the early time window and the recovery of the deep physical microenvironment, the structure can dynamically reduce the static theoretical nitrogen supply throughout the entire growth period. Through this reconstruction of the underlying constraint equations, the previously coarse-grained superposition model gains sensitivity to short-term biological temporal misalignments and physical spatial heterogeneity, thereby outputting an application strategy that conforms to the real agronomic environment.

[0105] Specifically, for any target farmland plot, the following parameters are extracted from the soil fertility ratio dataset: unit area dry matter of straw returned to the field, standard nominal nitrogen content of straw, nominal mineralization rate of straw throughout its growth period, standard nominal nitrogen content of organic matter, nominal mineralization rate of organic matter throughout its growth period, and the application decision variables to be solved. The carbon-nitrogen kinetic misalignment penalty factor and microenvironment synergistic compensation factor for the target farmland plot are also obtained. The product of the unit area dry matter of straw returned to the field, standard nominal nitrogen content of straw, and nominal mineralization rate of straw throughout its growth period is used as the basic static nitrogen supply for straw for the target farmland plot. The product of the application decision variables to be solved, standard nominal nitrogen content of organic matter, and nominal mineralization rate of organic matter throughout its growth period is used as the basic static nitrogen supply for organic matter for the target farmland plot. The sum of the basic static nitrogen supply for straw and the basic static nitrogen supply for organic matter is used as the basic static nitrogen supply for the target farmland plot. The difference between constant 1 and the carbon-nitrogen kinetic misalignment penalty factor is used as the assessment of recoverable nitrogen supply loss for the target farmland plot. The difference between the microenvironmental synergistic compensation factor and constant 1 is used as the assessment of microenvironmental compensation intensity for the target farmland plot, and a hyperbolic tangent mapping is applied to the microenvironmental compensation intensity assessment to obtain the bounded microenvironmental recovery coefficient for the target farmland plot. The result of multiplying the recoverable nitrogen supply loss assessment by the bounded microenvironmental recovery coefficient is used as the nitrogen supply recovery compensation assessment for the target farmland plot. The result of adding the carbon-nitrogen kinetic misalignment penalty factor to the nitrogen supply recovery compensation assessment is used as the dynamic effective nitrogen supply correction coefficient for the target farmland plot. The result of multiplying the basic static nitrogen supply by the dynamic effective nitrogen supply correction coefficient is used as the optimized dynamic effective nitrogen supply for the target farmland plot.

[0106] In one implementation, assume the first The dry matter weight of straw returned to the field per unit area corresponding to each farmland plot is: The standard nitrogen content of straw is ; The nominal mineralization rate of straw throughout its entire growth period is [missing information]. The standard nitrogen content of organic materials Nominal mineralization rate of organic materials throughout their entire growth period The decision variable to be solved is the first... The dry matter weight of organic materials applied per unit area of ​​each plot is: Then the first The formula for calculating the optimized dynamic effective nitrogen supply for each farmland plot is as follows:

[0107]

[0108] in, Indicates the first Optimized dynamic effective nitrogen supply for each farmland plot; Indicates the first The dry matter mass of straw returned to the field per unit area corresponding to each farmland plot; This indicates the standard nominal nitrogen content of straw; This indicates the nominal mineralization rate of straw throughout its entire growth period; Indicates the first The dry matter content of organic materials applied per unit area of ​​each plot; This indicates the standard nominal nitrogen content of organic materials; This indicates the nominal mineralization rate of organic materials throughout their entire growth period; This represents the carbon-nitrogen kinetic dislocation penalty factor; This represents the synergistic compensation factor of the microenvironment; This represents the hyperbolic tangent function.

[0109] It should be noted that the part within the parentheses on the left side of the formula... This represents the theoretical upper limit of nitrogen supply for the entire period calculated by existing algorithms based on static stoichiometry. The part within the square brackets on the right side of the formula is the nonlinear bounded correction operator designed in this invention. Because... ( This represents the baseline nitrogen supply penalty caused by prior biological fixation. This represents the nitrogen deficit that has been deemed invalid or hijacked. To prevent the microenvironmental synergistic compensation factor in step S3 from being over-amplified, causing the corrected nitrogen supply to exceed the actual amount, this formula uses... right Perform mapping, because , The function's output is strictly limited to This structure ensures that even in extremely poor physical microenvironments... When, the operator degenerates into The model strictly adheres to the initial biological deductions; this is achieved when the physical microenvironment is optimal, the straw is extremely fine, and the moisture content is perfect. When the value is much greater than 1, the operator approaches 1, and the dynamic nitrogen supply smoothly recovers to the theoretical upper limit. Through this bounded correction, the algorithm combines the short-term time-constrained risk and the potential for physical acceleration in space into a safe loss coefficient, scientifically reducing the model's expected effective nitrogen supply.

[0110] Thus, the optimized dynamic effective nitrogen supply was obtained by nonlinearly and boundedly coupling the carbon-nitrogen kinetic misalignment penalty factor, the microenvironment synergistic compensation factor, and the basic static nitrogen supply.

[0111] Step S5: By performing constraint solving and cost minimization on the optimized dynamic effective nitrogen supply, a straw and organic material combination scheme is obtained.

[0112] After obtaining the optimized dynamic effective nitrogen supply for the target farmland plot, the optimized dynamic effective nitrogen supply is used as the effective nitrogen supply constraint in the soil fertility ratio decision-making process, and the theoretical total nitrogen requirement of the target crop throughout its entire growth period is used as the condition for nitrogen supply satisfaction. Given that the dry matter mass of straw returned to the field per unit area of ​​the target farmland plot has been determined by the actual harvest data of the previous crop, straw residue survey data, and straw-to-grain ratio conversion data, the dry matter mass of organic materials applied per unit area of ​​the target farmland plot is used as the decision variable to be solved. A solution model is established with the constraint that the optimized dynamic effective nitrogen supply is not lower than the theoretical total nitrogen requirement, and the optimization objective being the minimum dry matter mass of organic materials applied per unit area or the minimum corresponding organic material input cost.

[0113] During the solution process, the soil fertility ratio decision system constrains the application amount of each candidate organic material based on the optimized dynamic effective nitrogen supply corresponding to different candidate application decision variables. When the optimized dynamic effective nitrogen supply corresponding to the candidate organic material application amount is lower than the theoretical total nitrogen requirement target, it is determined that the candidate application amount cannot meet the nitrogen requirement constraint for the entire crop growth period. When the optimized dynamic effective nitrogen supply corresponding to the candidate organic material application amount is greater than or equal to the theoretical total nitrogen requirement target, the candidate application amount is taken as a feasible application amount that meets the nitrogen supply constraint. Subsequently, among all feasible application amounts, the application amount with the lowest organic material input cost or the lowest dry matter mass of organic material per unit area is selected as the recommended organic material application amount for the target farmland plot.

[0114] Finally, based on the dry matter weight of straw returned to the field per unit area of ​​the target farmland, the recommended amount of organic materials applied, and the corresponding field operation area, a straw and organic material application plan is generated. This plan includes at least the target farmland identification, the dry matter weight of straw returned to the field per unit area, the amount of organic materials applied per unit area, the total amount of organic materials applied, and field application parameters. The farmland management system issues application instructions based on this plan to guide fertilizer machinery or organic material spreading equipment to complete the precise application of organic materials to the target farmland. Through this process, while meeting the crop's nitrogen requirements, the risk of nitrogen deficiency during the seedling stage due to overestimation of static nitrogen supply can be avoided, and excessive input of organic materials can be suppressed.

[0115] Thus, the constraint solution and cost minimization of the optimized dynamic effective nitrogen supply were completed to obtain the straw and organic material application scheme.

[0116] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for soil fertility improvement and optimization that integrates straw return to the field with the addition of organic materials, characterized in that, The method includes: Step S1: Collect and preprocess data on the release kinetic parameters of straw and organic materials and the physical characteristics of the topsoil microenvironment in the target farmland plot to obtain a basic dataset for soil fertility ratio. Step S2: By performing an integral gap assessment on the time misalignment characteristics of carbon and nitrogen release rate kinetics in the soil fertility ratio basic dataset, the carbon and nitrogen kinetic misalignment penalty factor is obtained. Step S3: By evaluating the spatial synergistic compensation characteristics of the microenvironment in the topsoil space in the soil fertility ratio basic dataset, the microenvironment synergistic compensation factor is obtained. Step S4: Obtain the optimized dynamic effective nitrogen supply by nonlinearly boundedly coupling the carbon-nitrogen kinetic misalignment penalty factor, the microenvironment synergistic compensation factor, and the basic static nitrogen supply. Step S5: Obtain the straw and organic material application scheme by performing constraint solution and cost minimization on the optimized dynamic effective nitrogen supply.

2. The soil fertility optimization method integrating straw return to the field and organic material addition as described in claim 1, characterized in that, The process involves collecting and preprocessing data on the release kinetics of straw and organic materials from the target farmland plots, as well as the physical characteristics of the topsoil microenvironment, to obtain a basic dataset for soil fertility improvement and formulation. This dataset includes: For any target farmland plot where a soil fertility improvement plan is to be implemented, the instantaneous carbon release rate of the planned straw biomass under standard conditions is obtained through agricultural basic databases, field in-situ culture test records, or indoor constant temperature culture test records, and the instantaneous nitrogen release rate of the planned organic materials under standard conditions is also obtained; through soil microbial culture databases or soil sample culture test results from the target farmland plot, the microbial nitrogen assimilation equivalent data corresponding to the effective carbon assimilation unit of soil microorganisms is obtained. Based on the target crop type, sowing time, and agronomic management standards for the current planting season, determine the boundary data of the critical seedling stage time window for the target crop; obtain the theoretical absolute nitrogen requirement of the target farmland plot during the critical seedling stage time window through the target crop fertilizer requirement model, regional agronomic database, or historical yield management records of the target farmland plot, and obtain the theoretical total nitrogen requirement target for the entire growth period of the target crop; By using actual harvest data of previous crops, straw residue survey data, and straw-to-grain ratio conversion data, the dry matter mass of straw returned to the field per unit area of ​​the target farmland is obtained; by using straw physicochemical test reports, agricultural production material databases, or standard parameter tables corresponding to straw types, the standard nominal nitrogen content of straw and the standard mineralization rate of straw throughout its growth period are obtained; by using organic matter physicochemical test reports, commercial organic fertilizer test labels, or standard parameter tables corresponding to organic material types, the standard nominal nitrogen content of organic materials and the standard mineralization rate of organic materials throughout its growth period are obtained, and the dry matter mass of organic materials applied per unit area of ​​the target farmland is set as the application decision variable to be solved; The actual mixing depth of straw after mechanical crushing is obtained by using airborne depth sensors, on-vehicle depth sensing nodes of agricultural machinery, or data recorded in tillage operations. Along the vertical soil profile corresponding to the actual mixing depth, the length of straw fragments after mechanical crushing in different depth ranges is statistically analyzed using agricultural machinery operation image recognition devices, soil profile sampling and screening devices, or straw fragment length detection devices. The actual mechanical crushing length data of straw at different depths in the vertical soil profile is obtained, and the standard crushing length data of straw is obtained from agricultural machinery operation specifications or straw returning operation standards. By deploying a moisture sensor array within the topsoil of the target farmland, actual volumetric moisture content data of the soil at different depths are collected at preset depth intervals; and by using soil texture test results of the target farmland, regional soil database, or microbial activity test results, the optimal volumetric moisture content data of microorganisms corresponding to the soil texture of the target farmland is obtained. Outlier removal, missing value imputation, unit unification, and time or spatial scale alignment were performed on the following data: instantaneous carbon release rate of straw, instantaneous nitrogen release rate of organic materials, microbial nitrogen assimilation equivalent, critical seedling stage time window boundary data, theoretical absolute nitrogen requirement at critical seedling stages, theoretical total nitrogen requirement target, dry matter mass of straw returned to the field per unit area, standard nominal nitrogen content of straw, nominal mineralization rate of straw throughout its growth period, standard nominal nitrogen content of organic materials, nominal mineralization rate of organic materials throughout its growth period, actual mixing depth of straw returned to the field, actual mechanical crushing length of straw, standard crushing length of straw, actual volumetric moisture content of soil, and optimal volumetric moisture content of microorganisms. The above data, along with the application decision variables to be solved, were used as the basic dataset for soil fertility improvement and allocation.

3. The soil fertility optimization method integrating straw return to the field and organic material addition as described in claim 1, characterized in that, The method involves evaluating the time misalignment characteristics of carbon and nitrogen release rate kinetics in the soil fertility ratio dataset using an integral gap assessment to obtain a carbon and nitrogen kinetic misalignment penalty factor, including: By coupling and converting the instantaneous carbon release rate data of straw and the nitrogen assimilation equivalent data of microorganisms in the basic dataset of soil fertility ratio, the theoretical nitrogen flux data of key seedling stage microorganisms were obtained. By performing integral gap normalization on the theoretical nitrogen flux data of key seedling stage microorganisms and the instantaneous nitrogen release rate data of organic materials, the carbon-nitrogen kinetic misalignment penalty factor was obtained.

4. The soil fertility optimization method integrating straw return to the field and organic material addition according to claim 3, characterized in that, The method involves coupling and converting straw instantaneous carbon release rate data and microbial nitrogen assimilation equivalent data from the soil fertility ratio dataset to obtain theoretical nitrogen flux data for key seedling stage microorganisms, including: For any target farmland plot, extract the straw instantaneous carbon release rate data, microbial nitrogen assimilation equivalent data, and key seedling stage time window boundary data corresponding to the target farmland plot from the soil fertility ratio basic dataset; Based on the boundary data of the critical seedling stage time window, the critical seedling stage time window corresponding to the target farmland plot is determined, and multiple target time points are set within the critical seedling stage time window according to the preset time interval. For any target time point, extract the instantaneous carbon release rate value of straw corresponding to the target time point from the instantaneous carbon release rate data, and use the calculation result of multiplying the instantaneous carbon release rate value of straw corresponding to the target time point with the microbial nitrogen assimilation equivalent data as the theoretical nitrogen flux value of key seedling microorganisms corresponding to the target time point. Arrange the theoretical nitrogen flux values ​​of microorganisms at all target time points within the critical seedling stage time window in chronological order to obtain the theoretical nitrogen flux data of microorganisms at the critical seedling stage for the target farmland plots.

5. The soil fertility optimization method integrating straw return to the field and organic material addition according to claim 3, characterized in that, The process involves performing integral gap normalization on the theoretical nitrogen flux data of key seedling stage microorganisms and the instantaneous nitrogen release rate data of organic materials to obtain the carbon-nitrogen kinetic misalignment penalty factor, including: For any target farmland plot, extract the instantaneous nitrogen release rate data of organic matter, the boundary data of key seedling stage time window, and the theoretical absolute nitrogen requirement of key seedling stage from the soil fertility ratio basic dataset, and obtain the theoretical nitrogen flux data of microorganisms in the key seedling stage corresponding to the target farmland plot. Based on the boundary data of the critical seedling stage time window, the critical seedling stage time window corresponding to the target farmland plot is determined, and multiple target time points are set within the critical seedling stage time window according to the preset time interval. For any target time point, extract the theoretical nitrogen flux value of key seedling microorganisms corresponding to the target time point from the theoretical nitrogen flux data of key seedling microorganisms, and extract the instantaneous nitrogen release rate value of organic matter corresponding to the target time point from the instantaneous nitrogen release rate data of organic matter. The difference between the theoretical nitrogen flux requirement of key seedling microorganisms at the target time point and the instantaneous nitrogen release rate of organic matter is used as the instantaneous retention gap assessment at the target time point. When the instantaneous retention gap assessment is less than a constant 0, the effective retention gap value at the target time point is set to a constant 0; when the instantaneous retention gap assessment is greater than or equal to a constant 0, the instantaneous retention gap assessment at the target time point is used as the effective retention gap value at the target time point. Time integration is performed on the effective retention gap values ​​corresponding to all target time points within the critical seedling stage time window to obtain carbon and nitrogen kinetic integral gap data for the target farmland plots. The carbon-nitrogen kinetic integral gap data is divided by the calculated theoretical absolute nitrogen requirement at the critical seedling stage as the relative retention gap assessment for the target farmland plot; The result of adding constant 1 to the relative holding gap assessment is used as the holding gap amplification denominator, and the reciprocal of the holding gap amplification denominator is used as the carbon and nitrogen kinetic misalignment penalty factor corresponding to the target farmland plot.

6. The soil fertility optimization method integrating straw return to the field and organic material addition according to claim 1, characterized in that, The process involves spatial synergistic compensation evaluation of the physical coupling characteristics of the topsoil microenvironment in the soil fertility ratio dataset to obtain microenvironment synergistic compensation factors, including: By performing relative enhancement evaluation on the specific surface area of ​​actual mechanically crushed straw length data and standard crushed straw length data in the soil fertilization ratio basic dataset, we obtained characterization data of accelerated straw crushing. By performing water suitability constraint mapping on the distribution data of actual soil volumetric water content and the optimal volumetric water content of microorganisms in the basic dataset of soil fertility improvement and ratio, water suitability characterization data is obtained. By processing the straw crushing acceleration characterization data and moisture suitability characterization data with the mean of the tillage layer depth, the microenvironment synergistic compensation factor was obtained.

7. A method for soil fertility improvement and optimization that integrates straw return to the field and the addition of organic materials according to claim 6, characterized in that, The process involves evaluating the relative enhancement of specific surface area by comparing the actual mechanical crushing length data and the standard crushing length data of straw in the soil fertility ratio dataset, thereby obtaining straw crushing acceleration characterization data, including: For any target farmland plot, extract the actual return-to-field tillage mixing depth, actual mechanical crushing length of straw, and standard crushing length of straw corresponding to the target farmland plot from the soil fertility ratio basic dataset; Based on the actual depth of the tillage and return to the field, the range of the tillage depth corresponding to the target farmland plot is determined, and multiple target depth points are set within the tillage depth range according to the preset depth interval. For any target depth point, extract the actual mechanical crushing length value of straw corresponding to the target depth point from the actual mechanical crushing length data of straw, and extract the standard crushing length value of straw corresponding to the target farmland plot from the standard crushing length data of straw. The standard crushing length value of straw corresponding to the target depth point is used as the numerator, the actual mechanical crushing length value of straw corresponding to the target depth point is used as the denominator, and the calculation result of the corresponding fraction is used as the straw crushing acceleration characterization value corresponding to the target depth point. Arrange the straw crushing acceleration characterization values ​​corresponding to all target depth points within the tillage depth range in depth order to obtain the straw crushing acceleration characterization data corresponding to the target farmland plot.

8. A method for soil fertility improvement and optimization integrating straw return to the field and organic material addition as described in claim 6, characterized in that, The process involves mapping the distribution data of actual soil volumetric water content in the soil fertility ratio dataset with the optimal volumetric water content data for microorganisms to obtain water suitability characterization data, including: For any target farmland plot, extract the actual soil returning and tillage mixing depth, actual soil volumetric moisture content distribution data, and microbial optimal volumetric moisture content data from the soil fertilization ratio basic dataset. Based on the actual depth of the tillage and return to the field, the range of the tillage depth corresponding to the target farmland plot is determined, and multiple target depth points are set within the tillage depth range according to the preset depth interval. For any target depth point, extract the actual volumetric moisture content value of the soil corresponding to the target depth point from the actual volumetric moisture content distribution data of the soil, and extract the optimal volumetric moisture content value of the microorganism corresponding to the target farmland plot from the optimal volumetric moisture content data of the microorganism. The difference between the actual volumetric moisture content of the soil at the target depth point and the optimal volumetric moisture content of the microorganism is used as the moisture deviation assessment at the target depth point, and the square of the moisture deviation assessment is used as the moisture deviation intensity assessment at the target depth point. The result of multiplying the square of the optimal volumetric water content of microorganisms by a constant 2 is used as the normalized denominator of water suitability. The result of dividing the water deviation intensity assessment by the negative of the normalized denominator of water suitability is subjected to exponential mapping with the natural constant as the base. The corresponding mapping result is used as the water suitability characterization value corresponding to the target depth point. The water suitability characterization values ​​corresponding to all target depth points within the tillage depth range are arranged in order of depth to obtain the water suitability characterization data corresponding to the target farmland plots.

9. A soil fertility optimization method integrating straw return to the field and organic material addition as described in claim 6, characterized in that, The process involves processing the straw crushing acceleration characterization data and moisture suitability characterization data by averaging the tillage depth to obtain the microenvironment synergistic compensation factor, including: For any target farmland plot, extract the actual incorporation and mixing depth of the target farmland plot from the soil fertility ratio basic dataset, and obtain the straw crushing acceleration characterization data and water suitability characterization data of the target farmland plot. Based on the actual depth of the tillage and return to the field, the range of the tillage depth corresponding to the target farmland plot is determined, and multiple target depth points are set within the tillage depth range according to the preset depth interval. For any target depth point, extract the straw crushing acceleration characterization value corresponding to the target depth point from the straw crushing acceleration characterization data, and extract the moisture suitability characterization value corresponding to the target depth point from the moisture suitability characterization data. The result of multiplying the straw crushing acceleration characterization value and the moisture suitability characterization value corresponding to the target depth point is taken as the depth microenvironment synergistic characterization value corresponding to the target depth point. The depth microenvironment collaborative characterization values ​​corresponding to all target depth points within the tillage depth range are processed by tillage depth integration to obtain the microenvironment collaborative integration data corresponding to the target farmland plots. The result of dividing the microenvironment synergy integral data by the actual mixing depth of tillage is used as the mean microenvironment synergy data for the target farmland plot. The result of adding constant 1 to the mean value of the microenvironment synergy is used as the microenvironment synergy compensation factor for the target farmland plot.

10. A method for soil fertility improvement and optimization integrating straw return to the field and organic material addition as described in claim 1, characterized in that, The process involves nonlinearly boundedly coupling and correcting the carbon-nitrogen kinetic misalignment penalty factor, the microenvironment synergistic compensation factor, and the basic static nitrogen supply to obtain an optimized dynamic effective nitrogen supply, including: For any target farmland plot, extract the unit area dry matter of straw return to the field, the standard nominal nitrogen content of straw, the standard mineralization rate of straw throughout the growth period, the standard nominal nitrogen content of organic materials, the standard mineralization rate of organic materials throughout the growth period, and the application decision variables to be solved from the soil fertility ratio basic dataset. Also obtain the carbon and nitrogen kinetic misalignment penalty factor and the microenvironment synergistic compensation factor corresponding to the target farmland plot. The result of multiplying the dry matter mass of straw returned to the field per unit area, the standard nominal nitrogen content of straw, and the nominal mineralization rate of straw throughout the entire growth period is used as the basic static nitrogen supply of straw for the target farmland plot. The calculation result of multiplying the application decision variable to be solved, the standard nominal nitrogen content of organic materials, and the nominal mineralization rate of organic materials throughout the entire growth period is used as the basic static nitrogen supply of organic materials for the target farmland plot. The calculation result of adding the basic static nitrogen supply of straw and the basic static nitrogen supply of organic materials is taken as the basic static nitrogen supply corresponding to the target farmland plot; The difference between constant 1 and the carbon-nitrogen kinetic misalignment penalty factor is used as the assessment of the recoverable nitrogen supply loss corresponding to the target farmland plot; The difference between the microenvironment synergistic compensation factor and the constant 1 is used as the assessment of the microenvironment compensation intensity corresponding to the target farmland plot. The microenvironment compensation intensity assessment is then processed by hyperbolic tangent mapping to obtain the bounded microenvironment recovery coefficient corresponding to the target farmland plot. The result of multiplying the recoverable nitrogen supply loss assessment by the bounded microenvironment recovery coefficient is used as the nitrogen supply recovery compensation assessment for the target farmland plot. The calculation result of adding the carbon and nitrogen kinetic misalignment penalty factor to the nitrogen supply recovery compensation assessment is used as the dynamic effective nitrogen supply correction coefficient corresponding to the target farmland plot; The result of multiplying the basic static nitrogen supply by the dynamic effective nitrogen supply correction coefficient is taken as the optimized dynamic effective nitrogen supply for the target farmland plot.

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