Establishment and application method of soil health evaluation system for saline-alkali cotton field

Abstract

The application discloses a kind of saline-alkali soil cotton field soil health evaluation system and its application method, belong to soil health evaluation and agricultural information technology field, by collecting pH, electric conductivity EC, exchangeable sodium percentage ESP, soil organic carbon SOC, microbial biomass carbon MBC, urease activity UE and soil bulk density BD core index, after data preprocessing, correlation analysis, principal component analysis and minimum data set method screening core evaluation index and determine weight, adopt membership function standardization after calculating comprehensive soil health index ISHI, and according to ISHI value, health grade is divided and potential limiting factor is identified;ISHI evaluation result is coupled with APSIS crop production process model and HYDRUS root zone water and salt transport model, realize the explanation of crop production process and root zone water and salt transport process to evaluation result, and output health grade, limit risk diagnosis, process explanation and management suggestion through platformization system.

Description

Technical Field

[0001] This invention belongs to the field of soil health assessment and agricultural environmental management technology, specifically involving the establishment and application method of a soil health assessment system for saline-alkali cotton fields based on the coupling of the Integrated Soil Health Index (ISHI) and a process model. Background Technology

[0002] Cotton is an important economic crop, playing a vital role in ensuring the supply of raw materials for the textile industry, promoting agricultural efficiency, and increasing farmers' income. With increasing constraints on arable land resources and adjustments to the agricultural production structure, cotton production is gradually expanding to marginal arable land such as saline-alkali land and dry land. Saline-alkali land has certain development and utilization potential, and cotton, with its strong salt tolerance, is an important crop for agricultural utilization in saline-alkali land. Therefore, establishing a soil health assessment system adapted to the characteristics of saline-alkali cotton fields is of practical significance for the efficient utilization of saline-alkali land resources and the sustainable development of the cotton industry.

[0003] Existing soil health assessment technologies are mostly designed for ordinary farmland or grain crop production areas, typically focusing on conventional indicators such as soil organic matter, total nitrogen, available nutrients, bulk density, aggregates, and enzyme activity. While these assessment methods can reflect the general soil quality of farmland, they do not adequately consider the unique characteristics of saline-alkali cotton fields, such as higher pH, increased electrical conductivity, exchangeable sodium accumulation, seasonal fluctuations in salinity, and water-salt transport processes in the root zone. Studies have shown that with increasing years of continuous cotton cropping, soil electrical conductivity and salinity tend to increase, and the negative impact of salt accumulation on soil health becomes increasingly significant. For saline-alkali cotton fields, soil health depends not only on fertility levels but also on salinity barriers, carbon pool activity, microbial function, physical structure, and water-salt migration processes. Directly applying the assessment system for ordinary farmland can easily lead to insufficient indicator specificity and a disconnect between assessment results and production limiting factors.

[0004] In existing technologies, some evaluation methods employ multi-index weighting or comprehensive index methods to assess soil quality. However, these methods often suffer from problems such as a large number of candidate indicators, high testing costs, significant indicator redundancy, and insufficient basis for determining weights. For example, soil quality evaluation in cotton-growing areas of Xinjiang often involves more than ten or even dozens of indicators. Although principal component analysis and factor analysis can be used to screen core indicators, existing methods do not adequately consider the salt accumulation and solanization risks unique to saline-alkali cotton fields. For the production and management of large-scale saline-alkali cotton fields, an overly complex indicator system is not conducive to grassroots agricultural technology extension or platform-based applications; conversely, too few indicators make it difficult to simultaneously characterize key limiting factors such as salinity, alkalinity, solanization, carbon pool, biological activity, and structural state.

[0005] Furthermore, existing soil health assessment methods mostly rely on static scoring or grading, typically providing only a comprehensive score for a specific plot or period. This makes it difficult to explain the reasons for changes in the health index or to determine the impact of different management practices on cotton production functions and the rhizosphere's water and salt environment. Some studies have indicated that while soil health index assessment methods based on minimal datasets simplify the assessment process, differences in MDS exist across different regions and crops, necessitating evaluation tailored to regional characteristics. For saline-alkali cotton field management, simply obtaining a health grade is insufficient for precise improvement; it is also necessary to further identify the sources of limiting risks, determine their relationship with cotton yield formation, soil carbon and nitrogen cycling, and salt migration, and propose targeted management recommendations accordingly.

[0006] While existing smart agriculture platforms can display data, manage plots, and provide early warnings for some indicators, they still lack a technical solution for assessing the soil health of saline-alkali cotton fields. This solution integrates the selection of core indicators, calculation of comprehensive health indices, interpretation of process models, and output of management recommendations. In particular, crop production process models such as APSIM and water-salt transport models such as HYDRUS are mostly used to independently simulate crop yield or water-salt distribution, and have not yet formed a stable diagnostic closed loop with the assessment of soil health indices in saline-alkali cotton fields. Summary of the Invention

[0007] This invention provides a method for establishing and applying a soil health assessment system for saline-alkali cotton fields based on the coupling of a comprehensive soil health index and a process model. This method can quantitatively assess the soil health status of saline-alkali cotton fields, classify the soil into grades, identify limiting factors, and recommend management measures. Furthermore, it interprets the assessment results through a crop production process model and a root zone water and salt transport model.

[0008] To achieve the above objectives, the present invention first provides a method for establishing a soil health evaluation system for cotton fields in saline-alkali land, including the following steps S1 to S6.

[0009] S1, collect soil health assessment data for saline-alkali cotton fields.

[0010] In a preferred embodiment of the present invention, the soil health assessment data for saline-alkali cotton fields includes at least chemical, biological, and physical indicators. The chemical indicators include at least pH, electrical conductivity (EC), exchangeable sodium percentage (ESP), and soil organic carbon (SOC); the biological indicators include at least microbial biomass carbon (MBC) and urease activity (UE); and the physical indicators include at least soil bulk density (BD). The selection of these core indicators ensures that the assessment system can simultaneously characterize salinity barriers, carbon pool basis, biological activity, and physical structure, thereby improving the relevance of soil health assessments for saline-alkali cotton fields.

[0011] More preferably, the soil health assessment data further includes at least one of the following: sampling period, management measures, meteorological data, yield data, soil moisture data, and stratified electrical conductivity data. The sampling period includes at least one of pre-sowing, seedling stage, flowering and boll-forming stage, and boll-opening stage; the management measures include at least one of conventional fertilization, no fertilization, biochar return to the field, and straw return to the field. Collecting the above auxiliary data helps in subsequent process interpretation and management decision analysis of the assessment results.

[0012] S2, preprocess the data collected in step S1.

[0013] In a preferred embodiment of the present invention, the preprocessing includes data integrity checking, outlier identification, missing value handling, unit unification, and standardization conversion. Specifically, outlier identification includes checking pH, EC, ESP, SOC, MBC, UE, and BD data that exceed preset reasonable ranges; unit unification includes standardizing the units of conductivity, bulk density, soil organic carbon, and microbial biomass carbon from different data sources. Through preprocessing, it is ensured that data from different years, different treatments, and different plots can be calculated within the same evaluation framework.

[0014] S3. Based on the preprocessed data, construct the minimum dataset for soil health assessment of saline-alkali cotton fields.

[0015] In a preferred embodiment of the present invention, step S3 specifically includes: performing correlation analysis and principal component analysis (PCA) on the preprocessed candidate indicators, and using the minimum dataset (MDS) method to screen core evaluation indicators to obtain the minimum dataset for soil health evaluation of saline-alkali cotton fields. Preferably, the minimum dataset includes pH, EC, ESP, SOC, MBC, UE, and BD. Specifically, pH, EC, and ESP are used to characterize salinity barriers; SOC is used to characterize soil carbon pool base and buffering capacity; MBC and UE are used to characterize soil microbial activity and nutrient transformation capacity; and BD is used to characterize the soil physical structure state.

[0016] The correlation analysis is used to eliminate redundant indicators with high information repetition; the principal component analysis is used to determine the explanatory power of different candidate indicators for the comprehensive variation of soil health in saline-alkali cotton fields; the minimum dataset method is used to retain core indicators that are highly representative, have low detection costs, and can reflect the key characteristics of soil health in saline-alkali cotton fields.

[0017] In one approach, the weights of the core evaluation indicators are determined by a combination of principal component loadings, variance contribution rate, and indicator representativeness in principal component analysis.

[0018] S4, perform membership function standardization on the core evaluation indicators in the minimum dataset.

[0019] In a preferred embodiment of the present invention, in step S4: for SOC, MBC, and UE, a positive membership function is used, where the standardized score increases as the index value increases; for pH, EC, ESP, and BD, a negative or suitable interval membership function is used, where the standardized score decreases as the index value deviates from the suitable range or increases. The membership function converts the indices of different dimensions into standardized scores within the 0-1 range, facilitating subsequent weighted comparison.

[0020] S5 calculates the comprehensive soil health index ISHI based on standardized core evaluation indicators.

[0021] In one aspect of the present invention, the formula for calculating the Integrated Soil Health Index (ISHI) is as follows:

[0022] ISHI = Σ Wi × Si

[0023] In the formula, Wi is the weight of the i-th core evaluation indicator, and Si is the score of the i-th core evaluation indicator after standardization by the membership function. The ISHI value is between 0 and 1, making the results intuitive and facilitating comparisons between plots, time periods, and management measures.

[0024] S6. Based on the comprehensive soil health index ISHI, classify the soil health level and diagnose limiting factors.

[0025] In one embodiment of the present invention, soil health is classified into four levels based on ISHI values: ISHI ≥ 0.75 is Level I healthy, 0.60 ≤ ISHI < 0.75 is Level II relatively healthy, 0.45 ≤ ISHI < 0.60 is Level III sub-healthy, and ISHI < 0.45 is Level IV unhealthy. Simultaneously, potential limiting factors are identified based on the standardized scores of individual core evaluation indicators. When the standardized score of an indicator is lower than a preset threshold, it is determined to be an indicator with a low contribution to the soil health of the plot and a potential limiting risk.

[0026] Specifically, when pH, EC, or ESP scores are low, a risk warning of salinity barriers or sodiumification is issued; when SOC scores are low, a risk warning of insufficient carbon pool is issued; when MBC or UE scores are low, a risk warning of insufficient microbial activity or nutrient transformation function is issued; and when BD scores are low, a risk warning of soil structure degradation or compaction is issued.

[0027] The present invention also provides an application method for the soil health assessment system established based on the above-described method, comprising the following steps.

[0028] Soil health assessment data of saline-alkali cotton fields to be evaluated are obtained; the comprehensive soil health index ISHI of the saline-alkali cotton fields to be evaluated is calculated based on the system, and the health level is classified and the limiting factor is diagnosed; a crop production process interpretation module and a root zone water and salt transport process interpretation module are constructed to interpret the evaluation results.

[0029] In one embodiment of the present invention, the crop production process interpretation module employs the APSIM model to simulate and interpret cotton yield formation, biomass accumulation, soil carbon and nitrogen cycling, and management response processes. The input data of the APSIM crop production process model includes daily meteorological data, soil stratification parameters, crop variety parameters, sowing date, fertilizer application rate, irrigation amount, and organic matter return method; the output data includes at least one of seed cotton yield, biomass, soil moisture, nitrogen supply, and crop production function evaluation results.

[0030] The root zone water and salt transport process interpretation module employs the HYDRUS model to simulate and interpret soil moisture movement, salt migration, stratified EC changes, and potential salt return risks in different soil layers. The input data for the HYDRUS root zone water and salt transport model includes soil hydraulic parameters, initial water content, initial salinity or initial EC, rainfall boundary, irrigation boundary, evaporation boundary, and root distribution parameters; the output data includes at least one of the following: soil water content in the 20–40 cm and 40–60 cm soil layers, stratified EC changes, salt redistribution results, and potential salt return risks.

[0031] Importantly, the APSIM and HYDRUS models do not directly participate in ISHI calculations, but rather explain the causes of ISHI evaluation results and the pathways of management measures. This coupling approach expands the evaluation results from a single score into a comprehensive diagnostic result that is interpretable, traceable, and applicable to management decision-making.

[0032] In one embodiment of the present invention, the application method further includes a platform-based application step: embedding the system or application method into a saline-alkali cotton field soil health assessment platform. The platform includes a data input and quality control module, an MDS-ISHI evaluation module, an APSIM process interpretation module, a HYDRUS process interpretation module, a limiting factor diagnosis module, a management suggestion output module, and a results visualization module.

[0033] The data input and quality control module is used to import soil index data, meteorological data, yield data, and stratified water and salt data, and to perform integrity checks and standardization processing; the MDS-ISHI evaluation module is used to calculate ISHI values ​​and classify health levels; the APSIM process interpretation module is used to interpret cotton production and carbon and nitrogen cycle processes; the HYDRUS process interpretation module is used to interpret root zone water and salt transport processes; the limiting factor diagnosis module is used to identify indicators with low contributions to soil health; the management suggestion output module is used to generate improvement suggestions based on the limiting factor diagnosis results; and the results visualization module is used to display health levels, individual indicator scores, limiting factor diagnosis results, and management suggestions.

[0034] In one embodiment of the present invention, the management suggestion output module generates and outputs management suggestions based on the limiting factor diagnosis results: when the SOC, MBC, or UE scores are low, it outputs a risk warning of insufficient carbon pool or insufficient biological function, and suggests taking measures such as returning biochar to the field, returning straw to the field, increasing the application of organic fertilizer, or long-term fertilization; when the EC or ESP scores are low, it outputs a risk warning of salinity barrier or sodiumization, and suggests taking measures such as optimizing irrigation, root zone leaching, salt drainage and control, or sodiumization structure improvement; when the BD score is low, it outputs a risk warning of soil structure degradation, and suggests taking measures such as deep loosening, returning straw to the field, or organic material improvement.

[0035] This invention also provides a soil health assessment system for saline-alkali cotton fields, comprising:

[0036] The data acquisition module is used to collect soil health assessment data for saline-alkali cotton fields. The data includes at least pH, EC, ESP, SOC, MBC, UE and BD indicators.

[0037] The data preprocessing module is used to perform integrity checks, outlier identification, missing value handling, unit unification, and standardization conversion on the collected data.

[0038] The core indicator screening and weight determination module is used to screen core evaluation indicators and determine their weights through correlation analysis, principal component analysis, and minimum dataset methods.

[0039] The ISHI calculation module is used to convert each core indicator into a standardized score through membership functions, and to calculate the comprehensive soil health index ISHI by combining the weights.

[0040] The health classification and diagnosis module is used to classify health levels based on ISHI values ​​and identify potential limiting risks based on individual indicator scores.

[0041] The process interpretation module, including the APSIM crop production process interpretation submodule and the HYDRUS rhizosphere water and salt transport process interpretation submodule, is used to interpret the ISHI evaluation results.

[0042] The output module is used to output ISHI values, health levels, risk assessment results, process explanations, and management recommendations.

[0043] In one embodiment of the present invention, the process interpretation module comprises: an APSIM submodule for interpreting seed cotton yield, biomass, soil carbon and nitrogen cycling, and management response; and a HYDRUS submodule for interpreting root zone water movement, stratified EC changes, salinity profile, and potential salt return risk. The two submodules do not participate in ISHI calculations but are only used for process interpretation of the evaluation results.

[0044] In one embodiment of the present invention, the health grading and diagnosis module uses the following criteria for classifying health levels: ISHI ≥ 0.75 is Level I healthy, 0.60 ≤ ISHI < 0.75 is Level II relatively healthy, 0.45 ≤ ISHI < 0.60 is Level III sub-healthy, and ISHI < 0.45 is Level IV unhealthy.

[0045] In one embodiment of the present invention, the diagnostic rules for limiting risks in the health grading and diagnosis module are as follows: low pH, EC, or ESP scores indicate salinity or sodium degradation risk; low SOC scores indicate carbon pool deficiency risk; low MBC or UE scores indicate insufficient microbial activity or nutrient conversion function risk; and low BD scores indicate soil compaction or structural degradation risk.

[0046] In one embodiment of the present invention, the system adopts a front-end and back-end separation architecture. The front-end is used for user interaction and result display, while the back-end is used for business logic processing and data interface services. It also includes a database for storing soil index data, model parameters, evaluation results, and management recommendations. The platform can be implemented using a B / S architecture, a C / S architecture, a mobile app, desktop software, or a cloud service, facilitating large-scale promotion and deployment.

[0047] Beneficial effects:

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] First, this invention uses pH, EC, ESP, SOC, MBC, UE, and BD as core indicators for assessing the soil health of saline-alkali cotton fields. These indicators simultaneously characterize salinity barriers, carbon pool activity, microbial function, and physical structure, thus improving the relevance of soil health assessments for saline-alkali cotton fields. This combination of indicators better reflects the actual characteristics of saline-alkali cotton fields—"strong salinity barriers, weak carbon pool foundation, sensitive biological activity, and easily degraded structure"—and enhances the ability of the assessment results to identify production limiting factors in saline-alkali cotton fields.

[0050] Secondly, this invention employs correlation analysis, principal component analysis, and minimum dataset methods to construct a core evaluation index system, reducing index redundancy and field testing costs, and improving the simplicity and generalizability of the evaluation system. Simultaneously, it uses membership functions and MDS weights to calculate ISHI, enabling unified evaluation of indicators with different dimensions. The ISHI value ranges from 0 to 1, providing intuitive results and facilitating comparisons between plots, time periods, and management measures.

[0051] Third, this invention constructs the comprehensive soil health index ISHI through membership function standardization and weight calculation, which enables quantitative comparison of soil health status among different management measures, different growth stages, and different plots.

[0052] Fourth, this invention combines the MDS-ISHI integrated evaluation model with the APSIM crop production process model and the HYDRUS rhizosphere water and salt transport model. This allows the evaluation results to not only provide a health level but also explain the reasons for changes in the health index from the perspectives of crop production, carbon and nitrogen cycling, and water and salt transport processes. Through process model explanation, the crop production and water and salt migration mechanisms behind ISHI changes can be elucidated, improving the scientific rigor and acceptability of the evaluation results.

[0053] Fifth, this invention can realize data import, quality control, indicator calculation, health classification, limiting factor diagnosis, process interpretation, management suggestion output and result visualization through a platform-based approach, providing technical support for the precise improvement of saline-alkali cotton fields and smart agricultural decision-making.

[0054] Sixth, the thresholds, weights, and management recommendation rules of this invention can be updated based on new years, management measures, and external verification data, and are not limited to a single plot or a single year, thus enhancing the adaptability and scalability of the evaluation system under different saline-alkali cotton field conditions. Attached Figure Description

[0055] Figure 1 This is a technical roadmap for the establishment and application of the soil health evaluation system for saline-alkali cotton fields according to the present invention.

[0056] Figure 2 This is a schematic diagram of the method for establishing a soil health evaluation system for saline-alkali cotton fields according to the present invention (corresponding to steps S1 to S6).

[0057] Figure 3 This is a flowchart illustrating the process of constructing the core evaluation index system based on correlation analysis, principal component analysis, and minimum dataset method in this invention.

[0058] Figure 4This is a schematic diagram of the standardized transformation of the membership function of a single core indicator in this invention, where (a) is a positive membership function (applicable to SOC, MBC, UE), and (b) is a negative or suitable interval membership function (applicable to pH, EC, ESP, BD).

[0059] Figure 5 This is a logic diagram for calculating the Integrated Soil Health Index (ISHI) and classifying health levels in this invention.

[0060] Figure 6 This is a flowchart illustrating the diagnostic rules for potential limiting factors in this invention.

[0061] Figure 7 This is a schematic diagram illustrating the input and output of the APSIM crop production process interpretation module in this invention and its coupling relationship with the ISHI evaluation results.

[0062] Figure 8 This is a schematic diagram of the input and output of the HYDRUS root zone water-salt transport process interpretation module in this invention and its coupling relationship with the ISHI evaluation results.

[0063] Figure 9 This is a schematic diagram of the overall system architecture and module composition of the saline-alkali cotton field soil health assessment platform of the present invention.

[0064] Figure 10 This is a schematic diagram illustrating the application process and management recommendations of the saline-alkali cotton field soil health assessment platform of this invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0066] Example 1: Establishment of a Soil Health Assessment System for Saline-Alkali Cotton Fields

[0067] This embodiment takes the conventional fertilization treatment of saline-alkali cotton fields in Dafeng District, Jiangsu Province from 2023 to 2025 as the research object, and evaluates the soil samples of the 0-20 cm topsoil layer at the cotton boll opening stage (T3).

[0068] 1. Data Collection

[0069] Soil samples were collected, and seven core indicators—pH, EC, ESP, SOC, MBC, UE, and BD—were measured. At the floret-opening stage, the values ​​for each indicator were: pH 7.60±0.22, EC 0.71±0.16 mS·cm.-1 , ESP 9.78±0.24%, SOC 11.33±0.16 g·kg -1 MBC 258.00±7.14 mg·kg -1 UE 1.01±0.03 mg NH4 + -N·g -1 ·d -1 BD 1.37±0.01g·cm -3 It also records meteorological, yield, and soil moisture data.

[0070] 2. Data Preprocessing

[0071] Perform a completeness check on the collected data to confirm that there are no missing values; identify outliers and remove data points that exceed the preset reasonable range; standardize the units of all indicators to ensure data consistency.

[0072] 3. Construct a minimal dataset

[0073] Pearson correlation analysis and principal component analysis were performed on the preprocessed candidate indicators, and the core evaluation indicators were screened using the minimum dataset method. In this embodiment, pH, EC, ESP, SOC, MBC, UE, and BD were identified as the core indicators of MDS. Weights were determined based on principal component loadings and variance contribution rates after normalization. The weights of pH, EC, ESP, SOC, MBC, UE, and BD were 0.085, 0.065, 0.171, 0.173, 0.168, 0.167, and 0.171, respectively. Among these, the total weight of indicators related to salinity and alkalinity barriers was 0.321, the total weight of indicators related to carbon pools and biological functions was 0.508, and the weight of physical structure indicators was 0.171.

[0074] 4. Membership function standardization

[0075] For SOC, MBC, and UE, a positive membership function Si=(Xi-Xmin) / (Xmax-Xmin) is used; for pH, EC, ESP, and BD, a negative membership function Si=(Xmax-Xi) / (Xmax-Xmin) is used. In the formula, Xi is the measured value of the indicator, and Xmax and Xmin are the maximum and minimum values ​​of the indicator in the evaluation dataset, respectively; after standardization, the scores of each indicator are all between 0 and 1.

[0076] 5. Calculate ISHI

[0077] The comprehensive soil health index was calculated using the formula ISHI = Σ(Wi × Si), where Wi is the index weight and Si is the standardized score. In this embodiment, the ISHI of the conventional fertilization treatment during the boll opening stage was 0.470.

[0078] 6. Health level classification and diagnosis of limiting factors

[0079] Based on an ISHI value of 0.470, the soil health level of this treatment was determined to be Level III (sub-healthy) (0.45≤ISHI<0.60). According to the ranking of core indicator scores, SOC and ESP were relatively low-scoring items, identified as major limiting factors, indicating insufficient carbon pool and risk of salinization or sodiumization, respectively.

[0080] Example 2: Application of Soil Health Assessment System in Saline-Alkali Cotton Fields

[0081] This embodiment continues the evaluation results of Embodiment 1, and further explains the process and outputs management suggestions.

[0082] 1. Obtain the data to be evaluated

[0083] Obtain the soil health assessment data collected in Example 1, as well as the corresponding meteorological data, soil stratification parameters, crop variety parameters, irrigation amount, etc.

[0084] 2. Calculate ISHI and perform grading and restriction factor diagnosis.

[0085] Following the same steps as in Example 1, the ISHI was 0.470, the health level was Level III (sub-healthy), and the main limiting factors were SOC and ESP.

[0086] 3. Explanation of the crop production process

[0087] Daily meteorological data, soil stratification parameters, crop variety parameters, sowing date, fertilizer application rate, and irrigation amount were input into the APSIM model to simulate the cotton yield formation and biomass accumulation process. In the platform example, the seed cotton yield corresponding to the conventional fertilization treatment was 3837 kg·hm². -2 The APSIM model was calibrated using 2024-2025 data and independently validated using 2023 data. The validation result for seed cotton yield was R. 2 =0.936, RMSE=169.27 kg·hm -2 NRMSE = 4.59%, used to explain the crop production function corresponding to the ISHI evaluation results.

[0088] 4. Explanation of water and salt transport processes in the root zone

[0089] Soil hydraulic parameters, initial water content, initial EC, rainfall boundary, and irrigation boundary were input into the HYDRUS model to simulate the root zone water movement and salt migration processes. Under conventional fertilization treatment, the EC values ​​for the 20–40 cm and 40–60 cm soil layers during the boll opening stage were 1.54 ± 0.10 mS·cm. -1 and 1.26±0.07 mS·cm -1The root region average EC is 1.40 mS·cm. -1 The HYDRUS model was calibrated using 2024–2025 data and independently validated using 2023 data. The EC validation results for the 40–60 cm soil layer were R. 2 =0.808, RMSE=0.10 mS·cm -1 NRMSE = 8.27%, used to explain changes in salinity profile and potential risk of salt return.

[0090] 5. Platform application and management recommendations

[0091] The above data, evaluation results, and process interpretation results were input into the saline-alkali cotton field soil health assessment platform. The platform output an evaluation report: ISHI of 0.470, health level III (sub-healthy), and main limiting factors SOC and ESP. Based on this, the management recommendation module outputs suggestions to increase organic carbon input, improve soil fertility, and combine these with optimized irrigation, root zone leaching, and salt control.

[0092] Example 3: Comparative Application of Evaluation Systems under Different Management Measures

[0093] This embodiment sets up four treatments in the same saline-alkali cotton field at the Rice and Wheat Seed Farm in Dafeng District, Jiangsu Province: conventional fertilization (CK), no fertilization (Ff), biochar return to the field (B), and straw return to the field (S). The CK treatment applied 150 kg·hm² of nitrogen. -2 P2O5 75 kg·hm -2 and K2O 112.5 kg·hm -2 Treatment B involved the application of 4860 kg·hm² of biochar. -2 S treatment applied 6000 kg·hm of straw -2 The equivalent carbon input for both B and S treatments is 2700 kg·hm². -2 Soil samples were collected from the 0-20 cm topsoil layer, and ISHI calculations and evaluations were performed according to the method in Example 1.

[0094] The evaluation results for the boll opening period were as follows: the ISHI values ​​for CK, Ff, B, and S treatments were 0.470, 0.125, 0.839, and 0.735, respectively, corresponding to Grade III sub-healthy, Grade IV unhealthy, Grade I healthy, and Grade II relatively healthy. The seed cotton yields for treatments B and S were 4339 kg·hm². -2 and 4238 kg·hm -2 Higher than the CK treatment of 3837 kg·hm -2 The root zone average EC was 1.15 mS·cm. -1 and 1.20 mS·cm -1 The concentration was lower than 1.40 mS·cm in the CK treatment. -1The results show that the evaluation system can distinguish the differences in soil health under different management measures, and the APSIM and HYDRUS models can interpret the evaluation results from the perspectives of crop production and rhizosphere water and salt processes, respectively.

[0095] The soil health evaluation system and application method for saline-alkali cotton fields provided by this invention can be applied to fields such as soil health diagnosis, management effect evaluation, precision improvement scheme formulation, and smart agriculture management platform construction in saline-alkali cotton fields, and has good prospects for promotion and application. This invention has a clear evaluation process, a moderate number of core indicators, and a programmable calculation method, making it suitable for embedding in web, mobile, or agricultural management platforms, providing an operable core algorithm module for the intelligent management of saline-alkali cotton fields.

Claims

1. A method for establishing a soil health evaluation system for cotton fields in saline-alkali land, characterized in that, Includes the following steps: S1, collect soil health assessment data for saline-alkali cotton fields; S2, preprocess the data collected in step S1; S3. Based on the preprocessed data, construct the minimum dataset for soil health assessment of saline-alkali cotton fields; S4, perform membership function standardization on the core evaluation indicators in the minimum dataset; S5, based on the standardized core evaluation indicators, calculates the comprehensive soil health index ISHI; S6. Based on the comprehensive soil health index ISHI, classify the soil health level and diagnose limiting factors. and / or In step S1, the soil health assessment data for the saline-alkali cotton field includes at least the following: Chemical indicators: pH, electrical conductivity (EC), exchangeable sodium percentage (ESP), and soil organic carbon (SOC); Biological indicators: microbial biomass carbon (MBC) and urease activity (UE); Physical properties: Soil bulk density (BD); and / or In step S1, the soil health assessment data further includes at least one of the following: sampling period, management measures, meteorological data, yield data, soil moisture data, and stratified electrical conductivity data.

2. The method for establishing according to claim 1, characterized in that, The preprocessing in step S2 includes data integrity checks, outlier identification, missing value handling, unit unification, and standardization conversion.

3. The method for establishing according to claim 3, characterized in that, Step S3 specifically includes: performing correlation analysis and principal component analysis on the preprocessed candidate indicators, and screening core evaluation indicators using the minimum dataset method to obtain the minimum dataset for soil health evaluation of saline-alkali cotton fields; the minimum dataset includes pH, EC, ESP, SOC, MBC, UE, and BD; wherein, pH, EC, and ESP are used to characterize saline-alkali barriers, SOC is used to characterize the soil carbon pool base, MBC and UE are used to characterize soil microbial activity and nutrient transformation capacity, and BD is used to characterize the soil physical structure state; and / or The weights of the core evaluation indicators are determined by a combination of principal component loadings, variance contribution rate, and indicator representativeness in principal component analysis.

4. The method for establishing according to claim 3, characterized in that, In step S4: For SOC, MBC and UE, a positive membership function is used, and the standardized score increases as the index value increases. For pH, EC, ESP, and BD, a negative or suitable interval membership function is used, and the standardized score decreases when the index value deviates from the suitable range or increases.

5. The method for establishing according to claim 4, characterized in that, In step S5, the formula for calculating the Integrated Soil Health Index (ISHI) is as follows: ISHI = ΣWi × Si In the formula, Wi is the weight of the i-th core evaluation indicator, and Si is the score of the i-th core evaluation indicator after standardization by the membership function.

6. The method for establishing according to claim 1, characterized in that, In step S6: Soil health is classified into four levels based on ISHI values: Level I is healthy if ISHI ≥ 0.75, Level II is relatively healthy if ISHI ≤ 0.60 < ISHI < 0.75, Level III is sub-healthy if ISHI ≤ 0.45 < ISHI < 0.60, and Level IV is unhealthy if ISHI < 0.

45. Potential limiting factors are identified based on the standardized scores of individual core evaluation indicators. When the standardized score of an indicator is lower than a preset threshold, it is identified as a potential limiting factor.

7. An application method for a soil health evaluation system for cotton fields in saline-alkali land, characterized in that, The system is established by the method according to any one of claims 1-6, and the application method includes: Obtain soil health assessment data for the saline-alkali cotton fields to be evaluated; Based on the system, the comprehensive soil health index ISHI of the saline-alkali cotton field to be evaluated is calculated, and the health level is classified and the limiting factor is diagnosed. A crop production process interpretation module and a root zone water and salt transport process interpretation module were constructed to interpret the evaluation results.

8. The application method according to claim 7, characterized in that, The crop production process interpretation module uses the APSIM model to simulate and interpret cotton yield formation, biomass accumulation, soil carbon and nitrogen cycling, and management response processes; the root zone water and salt transport process interpretation module uses the HYDRUS model to simulate and interpret soil moisture movement, salt migration, stratified EC changes, and potential salt return risk in different soil layers. and / or The APSIM and HYDRUS models do not directly participate in ISHI calculations, but rather explain the causes of ISHI evaluation results and the pathways through which management measures take effect.

9. The application method according to claim 7, characterized in that, It also includes the step of platform application: embedding the system described in any one of claims 1-6 or the application method described in any one of claims 7-8 into the saline-alkali cotton field soil health assessment platform; The platform includes a data input and quality control module, an MDS-ISHI evaluation module, an APSIM process interpretation module, a HYDRUS process interpretation module, a limiting factor diagnosis module, a management suggestion output module, and a results visualization module. and / or The management suggestion output module generates and outputs management suggestions based on the limiting factor diagnostic results: When the SOC, MBC or UE scores are low, the output will indicate the risk of insufficient carbon pool or insufficient biological function, and it will suggest taking measures such as returning biochar to the field, returning straw to the field, increasing the application of organic fertilizer, or long-term fertilization. When the EC or ESP score is low, a warning of salinity barrier or sodiumization risk is issued, and measures such as optimized irrigation, root zone leaching, salt removal and control, or sodiumization structure improvement are recommended. When the BD score is low, a risk warning of soil structure degradation is issued, and suggestions are made to take measures such as deep loosening, straw return to the field, or organic material improvement.

10. A soil health assessment system for saline-alkali cotton fields, characterized in that, include: The data acquisition module is used to collect soil health assessment data for saline-alkali cotton fields. The data includes at least pH, EC, ESP, SOC, MBC, UE and BD indicators. The data preprocessing module is used to perform integrity checks, outlier identification, missing value handling, unit unification, and standardization conversion on the collected data. The core indicator screening and weight determination module is used to screen core evaluation indicators and determine their weights through correlation analysis, principal component analysis, and minimum dataset methods. The ISHI calculation module is used to convert each core indicator into a standardized score through membership functions, and to calculate the comprehensive soil health index ISHI by combining the weights. The health classification and diagnosis module is used to classify health levels based on ISHI values ​​and identify potential limiting risks based on individual indicator scores. The process interpretation module, including the APSIM crop production process interpretation submodule and the HYDRUS rhizosphere water and salt transport process interpretation submodule, is used to interpret the ISHI evaluation results. The output module is used to output ISHI values, health levels, risk assessment results, process explanations, and management recommendations. and / or In the process interpretation module: The APSIM submodule is used to interpret seed cotton yield, biomass, soil carbon and nitrogen cycles, and management response. The HYDRUS submodule is used to interpret root zone water movement, stratified EC changes, salinity profiles, and potential salt return risks. The two submodules do not participate in ISHI calculations; they are only used for process interpretation of the evaluation results. and / or In the health grading and diagnosis module, the health grade classification criteria are as follows: ISHI ≥ 0.75 is Grade I healthy, 0.60 ≤ ISHI < 0.75 is Grade II relatively healthy, 0.45 ≤ ISHI < 0.60 is Grade III sub-healthy, and ISHI < 0.45 is Grade IV unhealthy. and / or In the health grading and diagnosis module, the diagnostic rules for limiting risk are as follows: Low pH, EC, or ESP scores indicate a risk of salinity disorder or sodiumization. A low SOC score indicates a risk of insufficient carbon pool; Low MBC or UE scores indicate a risk of insufficient microbial activity or nutrient conversion function; A low BD score indicates a risk of soil compaction or structural degradation. and / or The system adopts a front-end and back-end separation architecture. The front-end is used for user interaction and result display, while the back-end is used for business logic processing and data interface services. It also includes a database for storing soil index data, model parameters, evaluation results, and management recommendations.

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