Straw returning index design method

Abstract

The invention discloses a straw returning index design method, and belongs to the field of straw returning mode optimization. The problem that an existing method is poor in straw returning index optimization effect is solved. Through drift diameter analysis, the response of a soil ecosystem in the field returning decomposition process can be analyzed from four aspects of soil basic nutrients, a soil organic carbon library, a soil enzyme activity system and a soil microbial community; and by means of factor analysis modeling thinking, a soil ecology comprehensive response index is constructed, so that the influence of the straw length, the straw returning amount and the straw burial depth on a soil ecosystem is explored, the optimal returning index for promoting straw decomposition and improving the soil ecosystem is found, and the effect of optimizing the straw returning index is improved. According to the method, the straw length, the straw returning amount and the straw burial depth are optimized, and important support is provided for agricultural sustainable development. The method can be applied to straw returning optimization.

Description

Technical Field

[0001] The invention belongs to the field of optimization of straw returning to fields, and in particular relates to a straw returning to fields index design method. Background Art

[0002] Crop straw is the most important source of organic fertilizer for farmland soil. After returning it to the field, it can improve the physical structure of the soil, enhance the soil's ability to retain water and fertilizer, and significantly increase and fix carbon. It is an extremely effective way to improve farmland fertility and stabilize productivity.

[0003] In recent years, many scholars have conducted extensive research on the methods of returning straw to the field, including the effects of straw length, amount of straw returned to the field, and depth of straw burial on soil. Studies have found that different straw crushing lengths have an impact on the physical properties of the soil ecosystem, soil porosity, volume quality, etc.; the amount of straw returned to the field plays an important role in maintaining the physical and chemical environment of the soil; the depth of straw burial significantly affects the soil's ability to regulate moisture, thereby affecting the soil ecosystem. However, existing methods are mainly focused on studying one or two factors of returning straw to the field, lacking research on the interaction of multiple factors, and cannot truly reveal the impact of returning straw to the field on the soil ecosystem.

[0004] In addition, farmland soil is a complex ecosystem, and its soil enzyme system, microbial community, organic carbon pool, etc. will respond to the process of returning straw to the field, resulting in many factors to be considered in the optimization of straw returning to the field, and the process is complicated. However, the traditional optimization method of returning straw to the field does not fully consider the response of soil enzyme system, microbial community and organic carbon pool to the process of returning straw to the field. Therefore, the credibility of the data analysis results obtained by the traditional method is poor, and too much information is lost in the data extraction process, and the biological significance of the analysis results is missing, which seriously restricts the research on the response of soil ecosystem during the decomposition process of returning to the field.

[0005] In summary, due to the lack of research on the interaction of multiple factors of straw in the existing methods, and the failure to fully consider the response of soil enzyme system, microbial community and organic carbon pool to the straw return process, the effect of existing methods on the optimization of straw return indicators is still poor. Proposing a new method for optimizing straw return indicators is an urgent problem that needs to be solved. Summary of the invention

[0006] The purpose of the invention is to solve the problem that the existing method has poor effect on optimizing the straw return index, and propose a straw return index design method.

[0007] The technical solution adopted by the present invention to solve the above technical problems is: a method for designing straw return index, which specifically comprises the following steps:

[0008] Step 1: Using straw length, straw return amount and straw burial depth as straw return indicators, and setting a total of N groups of straw return indicator values;

[0009] Step 2, initializing the number of groups n=1;

[0010] Step 3, under the nth group of straw return index values, measuring the values ​​of various soil nutrient indexes, various soil enzyme activities, various soil carbon pool indexes and various soil microbial community indexes;

[0011] Step 4: Perform path analysis on the index values ​​measured in step 3 to obtain the indexes that are initially screened;

[0012] Step 5: Perform factor analysis on the indicators initially selected in step 4 to calculate the soil ecological comprehensive response index Q corresponding to the nth group of straw return index values. n ;

[0013] Step 6: Determine whether n=N is satisfied;

[0014] If not satisfied, set n=n+1 and return to step 3;

[0015] If satisfied, proceed to step seven;

[0016] Step 7: Perform surface fitting on the soil ecological comprehensive response index Q corresponding to all N groups of straw return index values, obtain the maximum soil ecological comprehensive response index Q value from the fitting results, and then obtain the straw length, straw return amount and straw burial depth corresponding to the maximum soil ecological comprehensive response index Q value.

[0017] Furthermore, in step 1, the value of N is 20.

[0018] Furthermore, the N groups of straw return index values ​​are specifically:

[0019] (1) The straw length is L4, the amount of straw returned to the field is A4, and the straw burial depth is BD4;

[0020] (2) The straw length is L4, the amount of straw returned to the field is A4, and the straw burial depth is BD2;

[0021] (3) The straw length is L4, the amount of straw returned to the field is A2, and the straw burial depth is BD4;

[0022] (4) The straw length is L4, the amount of straw returned to the field is A2, and the straw burial depth is BD2;

[0023] (5) The straw length is L2, the amount of straw returned to the field is A4, and the straw burial depth is BD4;

[0024] (6) The straw length is L2, the amount of straw returned to the field is A4, and the straw burial depth is BD2;

[0025] (7), straw length is L2, straw return amount is A2, and straw burial depth is BD4;

[0026] (8) The straw length is L2, the amount of straw returned to the field is A2, and the straw burial depth is BD2;

[0027] (9), straw length is L5, straw return amount is A3, and straw burial depth is BD3;

[0028] (10), straw length is L1, straw return amount is A3, and straw burial depth is BD3;

[0029] (11), straw length is L3, straw return amount is A5, and straw burial depth is BD3;

[0030] (12), straw length is L3, straw return amount is A1, and straw burial depth is BD3;

[0031] (13), straw length is L3, straw return amount is A3, straw burial depth is BD5;

[0032] (14), straw length is L3, straw return amount is A3, straw burial depth is BD1;

[0033] (15), straw length is L3, straw return amount is A3, straw burial depth is BD3;

[0034] (16), straw length is L3, straw return amount is A3, straw burial depth is BD3;

[0035] (17), straw length is L3, straw return amount is A3, straw burial depth is BD3;

[0036] (18), straw length is L3, straw return amount is A3, straw burial depth is BD3;

[0037] (19), straw length is L3, straw return amount is A3, straw burial depth is BD3;

[0038] (20), the straw length is L3, the amount of straw returned to the field is A3, and the straw burial depth is BD3.

[0039] Furthermore, L5 is the maximum length of the straw, and L1 is the minimum length of the straw.

[0040] A5 is the maximum amount of straw returned to the field, and A1 is the minimum amount of straw returned to the field.

[0041] BD5 is the maximum straw burial depth, BD1 is the minimum straw burial depth,

[0042] Furthermore, the soil nutrient indicators include soil ammonium nitrogen and nitrate nitrogen, soil available potassium, soil available phosphorus, soil microbial biomass carbon and soil microbial biomass nitrogen.

[0043] Furthermore, the soil enzyme activities include soil FDA hydrolase, soil dehydrogenase, soil sucrase, soil urease, soil acid phosphatase and soil catalase.

[0044] Furthermore, the soil carbon pool indicators include particulate organic carbon, water-soluble organic carbon, easily oxidizable organic carbon, total soil organic carbon and microbial biomass carbon.

[0045] Furthermore, the soil microbial community indicators include the cumulative utilization intensity of carbohydrate carbon sources, amino acid carbon sources, carboxylic acid carbon sources, polymer carbon sources, phenolic acid carbon sources and amine carbon sources.

[0046] Furthermore, the specific process of step 4 is as follows:

[0047] Step 4: The index value of index α measured in step 3 is recorded as G x-α , the index value of index β measured in step 3 is recorded as G x-β , G x-α and G x-β The correlation coefficient is denoted as r αβ , the index value measured in step 3 is used as the independent variable, the straw decomposition rate is used as the dependent variable, and the index value G is used as the x-α The correlation coefficient with the straw decomposition rate is recorded as r αφ , then the direct path coefficient of each index is calculated according to the following equations:

[0048]

[0049] Among them, K represents the total number of indicators measured in step 3, is the direct path coefficient of index α;

[0050] Step 4.2: Calculate the path residual effect P based on the direct path coefficient of each indicator Rφ :

[0051]

[0052] Calculate the P of each indicator measured in step 3 respectively iφ r iφ , and then sort the P values ​​of each indicator in descending order. iφ r iφ Sort and select P iφr iφ The first k positions satisfy P Rφ Indicators greater than or equal to 0.95;

[0053] The selected k indicators are used as the preliminary screened indicators.

[0054] Furthermore, the specific process of step five is as follows:

[0055] Step 51: Solve the correlation coefficient matrix of each indicator that is initially screened, and then perform eigenvalue decomposition on the correlation coefficient matrix of each indicator to obtain the maximum eigenvalue of each indicator;

[0056] The index with the largest eigenvalue greater than 1 is used as the extracted main factors F1, F2, ..., F k′ , j = 1, 2, ..., k', k' represents the number of main factors extracted;

[0057] Step 52: Determine whether the cumulative contribution rate of the main factor is greater than or equal to 0.7;

[0058] If the cumulative contribution rate of the main factor is greater than or equal to 0.7, continue to step 53;

[0059] If the cumulative contribution rate of the main factor is less than 0.7, then obtain the indicator that ranks next to the indicator initially screened out in step 4.2, add the obtained indicator to the indicator initially screened out, and then return to step 5.1;

[0060] Step 53: For any indicator preliminarily screened, calculate the factor loading of the indicator relative to each main factor, and then the indicator belongs to the main factor corresponding to the largest factor loading;

[0061] Similarly, the main factors to which each indicator belongs that are initially screened are obtained;

[0062] Step 54: Calculate the verification commonality of each indicator preliminarily screened based on the result of step 53;

[0063] For any indicator preliminarily screened, if the verification commonality of the indicator is greater than or equal to 0.5, there is no need to re-determine the subordinate main factor of the indicator. If the verification commonality of the indicator is less than 0.5, it is necessary to re-determine the subordinate main factor of the indicator in combination with the biological significance of the indicator and the factor loading of the indicator, and recalculate the verification commonality of the indicator based on the re-determined subordinate main factor;

[0064] Step 5. Calculate the soil ecological comprehensive response index Q based on the verification commonality of each index preliminarily screened and the contribution rate of each index preliminarily screened n ;

[0065]

[0066] Among them, ψ i represents the verification commonality of the i-th indicator, ξ i Represents the contribution rate of the i-th indicator.

[0067] The beneficial effects of the present invention are:

[0068] The present invention can analyze the response of the soil ecosystem during the decomposition process of returning to the field from four aspects: soil basic nutrients, soil organic carbon pool, soil enzyme activity system and soil microbial community through path analysis; and with the help of factor analysis modeling thinking, construct a soil ecological comprehensive response index, and then explore the influence of straw length, straw return amount and straw burial depth on the soil ecosystem, find the best return index to promote straw decomposition and improve the soil ecosystem, and improve the effect of optimizing the straw return index. The method of the present invention provides important support for sustainable agricultural development by optimizing straw length, straw return amount and straw burial depth. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is an indicator system diagram of the soil ecosystem in the present invention. DETAILED DESCRIPTION

[0070] Specific implementation method 1: Combination Figure 1 The present embodiment is described as follows. The present embodiment describes a method for designing straw return indicators, and the method specifically comprises the following steps:

[0071] Step 1: Using straw length, straw return amount and straw burial depth as straw return indicators, and setting a total of N groups of straw return indicator values;

[0072] Step 2, initializing the number of groups n=1;

[0073] Step 3, under the nth group of straw return index values, measuring the values ​​of various soil nutrient indexes, various soil enzyme activities, various soil carbon pool indexes and various soil microbial community indexes;

[0074] Step 4: Perform path analysis on the index values ​​measured in step 3 to obtain the indexes that are initially screened;

[0075] Step 5: Perform factor analysis on the indicators initially selected in step 4 to calculate the soil ecological comprehensive response index Q corresponding to the nth group of straw return index values. n ;

[0076] Step 6: Determine whether n=N is satisfied;

[0077] If not satisfied, set n=n+1 and return to step 3;

[0078] If satisfied, proceed to step seven;

[0079] Step 7. Perform surface fitting on the soil ecological comprehensive response index Q corresponding to all N groups of straw return index values, obtain the maximum soil ecological comprehensive response index Q value from the fitting results, and then obtain the straw length, straw return amount and straw burial depth corresponding to the maximum soil ecological comprehensive response index Q value. The straw length, straw return amount and straw burial depth corresponding to the maximum soil ecological comprehensive response index Q value are the optimal straw return index values.

[0080] Specific implementation method 2: This implementation method is different from specific implementation method 1 in that in step 1, the value of N is 20.

[0081] The other steps and parameters are the same as those in the first embodiment.

[0082] Specific implementation method three: This implementation method is different from specific implementation methods one or two in that the N groups of straw return index values ​​are specifically:

[0083] (1) The straw length is L4, the amount of straw returned to the field is A4, and the straw burial depth is BD4;

[0084] (2) The straw length is L4, the amount of straw returned to the field is A4, and the straw burial depth is BD2;

[0085] (3) The straw length is L4, the amount of straw returned to the field is A2, and the straw burial depth is BD4;

[0086] (4) The straw length is L4, the amount of straw returned to the field is A2, and the straw burial depth is BD2;

[0087] (5) The straw length is L2, the amount of straw returned to the field is A4, and the straw burial depth is BD4;

[0088] (6) The straw length is L2, the amount of straw returned to the field is A4, and the straw burial depth is BD2;

[0089] (7), straw length is L2, straw return amount is A2, and straw burial depth is BD4;

[0090] (8) The straw length is L2, the amount of straw returned to the field is A2, and the straw burial depth is BD2;

[0091] (9), straw length is L5, straw return amount is A3, and straw burial depth is BD3;

[0092] (10), straw length is L1, straw return amount is A3, and straw burial depth is BD3;

[0093] (11), straw length is L3, straw return amount is A5, and straw burial depth is BD3;

[0094] (12), straw length is L3, straw return amount is A1, and straw burial depth is BD3;

[0095] (13), straw length is L3, straw return amount is A3, straw burial depth is BD5;

[0096] (14), straw length is L3, straw return amount is A3, straw burial depth is BD1;

[0097] (15), straw length is L3, straw return amount is A3, straw burial depth is BD3;

[0098] (16), straw length is L3, straw return amount is A3, straw burial depth is BD3;

[0099] (17), straw length is L3, straw return amount is A3, straw burial depth is BD3;

[0100] (18), straw length is L3, straw return amount is A3, straw burial depth is BD3;

[0101] (19), straw length is L3, straw return amount is A3, straw burial depth is BD3;

[0102] (20), the straw length is L3, the amount of straw returned to the field is A3, and the straw burial depth is BD3.

[0103] The other steps and parameters are the same as those in the first or second embodiment.

[0104] Each group of index values ​​in the present embodiment utilizes a three-factor five-level quadratic orthogonal rotation experimental design, selects the maximum value (L5, A5, BD5) and minimum value (L1, A1, BD1) of each index in the existing research results, and takes the straw length index as an example, takes the maximum value of the straw length index as the actual value corresponding to the coding value 1.682, and takes the minimum value of the straw length index as the actual value corresponding to the coding value -1.682, and then determines the actual values ​​under other coding values ​​(0, 1 and -1) according to the same conversion between the coding value and the actual value.

[0105] The straw return index values ​​of each group are shown in Table 1:

[0106] Table 1 Experimental design

[0107]

[0108] Through the various groups of index values ​​designed by the present invention, the setting of experimental data can be made more reasonable and comprehensive, so as to obtain the optimal index value later.

[0109] Specific implementation method 4: This implementation method is different from any one of the specific implementation methods 1 to 3 in that L5 is the maximum length of the straw, and L1 is the minimum length of the straw.

[0110] A5 is the maximum amount of straw returned to the field, and A1 is the minimum amount of straw returned to the field.

[0111] BD5 is the maximum straw burial depth, BD1 is the minimum straw burial depth,

[0112] The other steps and parameters are the same as those in Specific Embodiments 1 to 3.

[0113] Specific implementation method five: This implementation method is different from any one of specific implementation methods one to four in that the soil nutrient indicators include soil ammonium nitrogen and nitrate nitrogen, soil available potassium, soil available phosphorus, soil microbial biomass carbon (referred to as soil microbial biomass carbon) and soil microbial biomass nitrogen (referred to as soil microbial biomass nitrogen).

[0114] The other steps and parameters are the same as those in Specific Embodiments 1 to 4.

[0115] Soil ammonium nitrogen and nitrate nitrogen were determined using the SKALAR analysis method;

[0116] Soil available potassium was determined by NH4OAc extraction method;

[0117] Soil available phosphorus was extracted with NaHCO3 solution and determined by molybdenum antimony colorimetric method;

[0118] Soil microbial biomass carbon and nitrogen were extracted by chloroform fumigation.

[0119] Specific implementation method six: This implementation method is different from any one of specific implementation methods one to five in that the soil enzyme activities include soil FDA hydrolase, soil dehydrogenase, soil sucrase, soil urease, soil acid phosphatase and soil catalase.

[0120] The other steps and parameters are the same as those in Specific Implementation Methods 1 to 5.

[0121] Soil FDA hydrolase was determined using the fluorescein diacetate colorimetric method;

[0122] Soil dehydrogenase was determined by TTC colorimetric method;

[0123] Soil sucrase activity was determined using the 3,5-dinitrosalicylic acid colorimetric method;

[0124] Soil urease activity was determined using the indophenol blue colorimetric method;

[0125] Soil acid phosphatase activity was determined using disodium phenyl phosphate colorimetric method;

[0126] Soil catalase activity was determined by titration with 0.005 mol / L KMnO4.

[0127] Specific implementation method seven: This implementation method is different from any of specific implementation methods one to six in that the soil carbon pool indicators include particulate organic carbon (POC), water-soluble organic carbon (WDOC), easily oxidizable organic carbon (ROC), soil total organic carbon (TOC) and microbial biomass carbon (MBC).

[0128] The other steps and parameters are the same as those in Specific Embodiments 1 to 6.

[0129] The content of particulate organic carbon (POC) was determined by the sodium hexametaphosphate dispersion method;

[0130] The water-soluble organic carbon (WDOC) content was extracted with water, filtered through a 0.45 μm filter membrane, and then analyzed using a TOC instrument;

[0131] The readily oxidizable organic carbon (ROC) was determined by potassium permanganate oxidation extraction method;

[0132] Soil total organic carbon (TOC) was determined using the potassium dichromate-external heating method;

[0133] The microbial biomass carbon (MBC) was determined by the chloroform fumigation-potassium sulfate extraction method.

[0134] Specific embodiment eight: This embodiment differs from any one of specific embodiments one to seven in that the soil microbial community indicators include the cumulative utilization intensity of carbohydrate carbon sources, amino acid carbon sources, carboxylic acid carbon sources, polymer carbon sources, phenolic acid carbon sources and amine carbon sources.

[0135] The other steps and parameters are the same as those in Specific Embodiments 1 to 7.

[0136] The cumulative carbon source utilization intensity in this embodiment is determined by the ECO microplate method. The absorbance value (OD value) at 590 nm is measured with a culture cycle of 24 hours, and the measurement is continued for seven culture cycles (168 hours). In the t-th culture cycle, the total change of 31 carbon sources in the ECO plate is in, i represents the type of carbon source, i=1,2,...,31; t=1,2,3,…,7.

[0137] The data is dimensionless, and we get

[0138] Therefore, the utilization intensity of carbon source i by the microbial community is

[0139] The distribution of carbon sources in the ECO board is shown in Table 2. The carbon sources are classified according to their distribution in the ECO board:

[0140] Table 2 Carbon source distribution in ECO board

[0141]

[0142]

[0143] The cumulative utilization intensity of this type of carbon source by microorganisms is:

[0144]

[0145] Wherein, W represents the cumulative utilization intensity of this type of carbon source by microorganisms.

[0146] Specific implementation method 9: This implementation method is different from specific implementation methods 1 to 8 in that the specific process of step 4 is as follows:

[0147] Step 4: The index value of any index α measured in step 3 is recorded as G x-α , the index value of another arbitrary index β measured in step 3 is recorded as G x-β , G x-α and G x-β The correlation coefficient is denoted as r αβ , the index value measured in step 3 is used as the independent variable, the straw decomposition rate is used as the dependent variable, and the index value G is used as the x-α The correlation coefficient with the straw decomposition rate is recorded as r αφ , then the direct path coefficient of each index is calculated according to the following equations:

[0148]

[0149] Among them, K represents the total number of indicators measured in step 3, is the direct path coefficient of the indicator α (indicates that when other variables are fixed, G x-α Direct effect on straw decomposition rate G y size);

[0150] Step 4.2: Calculate the path residual effect P based on the direct path coefficient of each indicator Rφ :

[0151]

[0152] Calculate the P of each indicator measured in step 3 respectively iφ r iφ , and then sort the P values ​​of each indicator in descending order.iφ r iφ Sort and select P iφ r iφ The first k positions satisfy P Rφ Indicators greater than or equal to 0.95 (i.e., according to P iφ r iφ The values ​​calculated for the first k-1 indicators satisfy: Less than 0.95, and according to P iφ r iφ The values ​​calculated for the top k indicators satisfy: Greater than or equal to 0.95);

[0153] The selected k indicators are used as the preliminary screened indicators.

[0154] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.

[0155] Specific implementation method 10: This implementation method is different from specific implementation methods 1 to 9 in that the specific process of step 5 is as follows:

[0156] Solve the correlation coefficient matrix R of each index that has been initially screened, where the absolute values ​​of more than 80% of the correlation coefficients in each correlation coefficient matrix are greater than 0.1. Therefore, there is a strong correlation between the indicators, and it is suitable to use factor analysis to study the relationship between the indicators.

[0157] Step 51: Solve the correlation coefficient matrix of each indicator that is initially screened, and then perform eigenvalue decomposition on the correlation coefficient matrix of each indicator to obtain the maximum eigenvalue of each indicator;

[0158] The index with the largest eigenvalue greater than 1 is used as the extracted main factors F1, F2, ..., F k′ , j = 1, 2, ..., k', k' represents the number of main factors extracted;

[0159] Step 52: Determine whether the cumulative contribution rate of the main factor is greater than or equal to 0.7;

[0160] If the cumulative contribution rate of the main factor is greater than or equal to 0.7, continue to step 53;

[0161] If the cumulative contribution rate of the main factor is less than 0.7, then obtain the indicator that ranks next to the indicator initially screened out in step 4.2, add the obtained indicator to the indicator initially screened out, and then return to step 5.1;

[0162] That is, in step 42, the P values ​​of each indicator are calculated in descending order. iφ r iφSorting is performed. If the top 10 indicators have been selected before, then the indicator ranked 11th is added to the selected indicators until the conditions are met;

[0163] Step 53: For any indicator preliminarily screened, calculate the factor loading of the indicator relative to each main factor (the factor loading reflects the correlation between the indicator and the main factor. The larger the factor loading, the more significant the relationship between the indicator and the main factor). Then the indicator belongs to the main factor corresponding to the largest factor loading.

[0164] Similarly, the main factors to which each indicator belongs that are initially screened are obtained;

[0165] Step 54: Based on the results of step 53, the verification commonality of each indicator preliminarily screened is calculated (the verification commonality indicates the degree to which the main factor to which the indicator belongs can explain the indicator. The closer the verification commonality value is to 1, the better, and the maximum value is 1);

[0166] For any indicator preliminarily screened, if the verification commonality of the indicator is greater than or equal to 0.5, there is no need to re-determine the subordinate main factor of the indicator. If the verification commonality of the indicator is less than 0.5, it is necessary to re-determine the subordinate main factor of the indicator in combination with the biological significance of the indicator and the factor loading of the indicator (first find all the main factors belonging to the same category as the indicator from the main factors, and then select the main factor with the largest factor loading from the main factors found), and recalculate the verification commonality of the indicator based on the re-determined subordinate main factor;

[0167] Step 5. Calculate the soil ecological comprehensive response index Q based on the verification commonality of each index preliminarily screened and the contribution rate of each index preliminarily screened n ;

[0168]

[0169] Among them, ψ i represents the verification commonality of the i-th indicator, ξ i Represents the contribution rate of the i-th indicator.

[0170] The other steps and parameters are the same as those in Specific Embodiments 1 to 9.

[0171] The present invention takes returning corn straw to the fields in cold black soil areas as the experimental basis. In the six sampling periods after returning the straw to the fields, 13 indicators each entered the path analysis model, and the residual path coefficients were all lower than 2%, meeting the analysis criteria of the path analysis model. The path analysis model can effectively analyze the response of the soil ecosystem during the decomposition process of returning the straw to the fields.

[0172] The constructed factor analysis model was used to analyze the indicators entering the path analysis model. It can be seen from the correlation coefficient matrix that more than 80% of the absolute values ​​of the correlation coefficients are greater than 0.1, indicating that the results of the factor analysis model are credible. The cumulative contribution rate of the main factors extracted from all treatment groups exceeded 0.70, and at the same time, the characteristic value was greater than 1, indicating that the main factor extraction effect reached an ideal state. The constructed model is scientific, and the main factors of the indicators can be determined according to the factor loading value, and then the comprehensive response index of soil ecology can be constructed. Based on the response index, the response surface analysis of the straw return factor was carried out, and the factor analysis model and the response surface method were combined to explore the impact of the return factor on the soil ecosystem, and then find the best return index to promote straw decomposition and improve the soil ecosystem.

[0173] The above calculation examples of the present invention are only used to explain the calculation model and calculation process of the present invention in detail, and are not intended to limit the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. A straw return index design method, characterized in that: The method specifically comprises the following steps: Step 1: Using straw length, straw return amount and straw burial depth as straw return indicators, and setting a total of N groups of straw return indicator values; Step 2, initialize the number of groups n = 1; Step 3, under the nth group of straw return index values, measuring the values ​​of various soil nutrient indexes, various soil enzyme activities, various soil carbon pool indexes and various soil microbial community indexes; Step 4: Perform path analysis on the index values ​​measured in step 3 to obtain the indexes that are initially screened; Step 5: Perform factor analysis on the indicators initially selected in step 4 to calculate the soil ecological comprehensive response index Q corresponding to the nth group of straw return index values. n ; Step 6: Determine whether n=N is satisfied; If not satisfied, set n=n+1 and return to step 3; If satisfied, proceed to step seven; Step 7: Perform surface fitting on the soil ecological comprehensive response index Q corresponding to all N groups of straw return index values, obtain the maximum soil ecological comprehensive response index Q value from the fitting results, and then obtain the straw length, straw return amount and straw burial depth corresponding to the maximum soil ecological comprehensive response index Q value.

2. A straw return index design method according to claim 1, characterized in that: In step 1, the value of N is 20.

3. A straw return index design method according to claim 2, characterized in that: The N group of straw return index values ​​are specifically: (1) The straw length is L4, the amount of straw returned to the field is A4, and the straw burial depth is BD4; (2) The straw length is L4, the amount of straw returned to the field is A4, and the straw burial depth is BD2; (3) The straw length is L4, the amount of straw returned to the field is A2, and the straw burial depth is BD4; (4) The straw length is L4, the amount of straw returned to the field is A2, and the straw burial depth is BD2; (5) The straw length is L2, the amount of straw returned to the field is A4, and the straw burial depth is BD4; (6) The straw length is L2, the amount of straw returned to the field is A4, and the straw burial depth is BD2; (7), straw length is L2, straw return amount is A2, and straw burial depth is BD4; (8) The straw length is L2, the amount of straw returned to the field is A2, and the straw burial depth is BD2; (9), straw length is L5, straw return amount is A3, and straw burial depth is BD3; (10), straw length is L1, straw return amount is A3, and straw burial depth is BD3; (11), straw length is L3, straw return amount is A5, and straw burial depth is BD3; (12), straw length is L3, straw return amount is A1, and straw burial depth is BD3; (13), straw length is L3, straw return amount is A3, straw burial depth is BD5; (14), straw length is L3, straw return amount is A3, straw burial depth is BD1; (15), straw length is L3, straw return amount is A3, straw burial depth is BD3; (16), straw length is L3, straw return amount is A3, straw burial depth is BD3; (17), straw length is L3, straw return amount is A3, straw burial depth is BD3; (18), straw length is L3, straw return amount is A3, straw burial depth is BD3; (19), straw length is L3, straw return amount is A3, straw burial depth is BD3; (20), the straw length is L3, the amount of straw returned to the field is A3, and the straw burial depth is BD3.

4. A straw return index design method according to claim 3, characterized in that: L5 is the maximum length of the straw, and L1 is the minimum length of the straw. A5 is the maximum amount of straw returned to the field, and A1 is the minimum amount of straw returned to the field. BD5 is the maximum straw burial depth, BD1 is the minimum straw burial depth, 5. A straw return to field index design method according to claim 4, characterized in that: The soil nutrient indicators include soil ammonium nitrogen and nitrate nitrogen, soil available potassium, soil available phosphorus, soil microbial biomass carbon and soil microbial biomass nitrogen.

6. A straw return index design method according to claim 5, characterized in that: The soil enzyme activities include soil FDA hydrolase, soil dehydrogenase, soil sucrase, soil urease, soil acid phosphatase and soil catalase.

7. A straw return index design method according to claim 6, characterized in that: The soil carbon pool indicators include particulate organic carbon, water-soluble organic carbon, easily oxidizable organic carbon, total soil organic carbon and microbial biomass carbon.

8. A straw return index design method according to claim 7, characterized in that: The soil microbial community indicators include the cumulative utilization intensity of carbohydrate carbon sources, amino acid carbon sources, carboxylic acid carbon sources, polymer carbon sources, phenolic acid carbon sources and amine carbon sources.

9. A straw return index design method according to claim 8, characterized in that: The specific process of step 4 is as follows: Step 4: The index value of index α measured in step 3 is recorded as G x-α , the index value of index β measured in step 3 is recorded as G x-β , G x-α and G x-β The correlation coefficient is denoted as r αβ , the index value measured in step 3 is used as the independent variable, the straw decomposition rate is used as the dependent variable, and the index value G is used as the x-α The correlation coefficient with the straw decomposition rate is recorded as r αφ , then the direct path coefficient of each index is calculated according to the following equations: Among them, K represents the total number of indicators measured in step 3, is the direct path coefficient of index α; Step 4.2: Calculate the path residual effect P based on the direct path coefficient of each indicator Rφ : Calculate the P of each indicator measured in step 3 respectively iφ r iφ , and then sort the P values ​​of each indicator in descending order. iφ r iφ Sort and select P iφ r iφ The first k positions satisfy P Rφ Indicators greater than or equal to 0.95; The selected k indicators are used as the preliminary screened indicators.

10. A straw return to field index design method according to claim 9, characterized in that: The specific process of step five is as follows: Step 51: Solve the correlation coefficient matrix of each indicator that is initially screened, and then perform eigenvalue decomposition on the correlation coefficient matrix of each indicator to obtain the maximum eigenvalue of each indicator; The index with the largest eigenvalue greater than 1 is used as the extracted main factors F1, F2, ..., F k′ , j = 1, 2, ..., k', k' represents the number of main factors extracted; Step 52: Determine whether the cumulative contribution rate of the main factor is greater than or equal to 0.7; If the cumulative contribution rate of the main factor is greater than or equal to 0.7, continue to step 53; If the cumulative contribution rate of the main factor is less than 0.7, then obtain the indicator that ranks next to the indicator initially screened out in step 4.2, add the obtained indicator to the indicator initially screened out, and then return to step 5.1; Step 53: For any indicator preliminarily screened, calculate the factor loading of the indicator relative to each main factor, and then the indicator belongs to the main factor corresponding to the largest factor loading; Similarly, the main factors to which each indicator belongs that are initially screened are obtained; Step 54: Calculate the verification commonality of each indicator preliminarily screened based on the result of step 53; For any indicator preliminarily screened, if the verification commonality of the indicator is greater than or equal to 0.5, there is no need to re-determine the subordinate main factor of the indicator. If the verification commonality of the indicator is less than 0.5, it is necessary to re-determine the subordinate main factor of the indicator in combination with the biological significance of the indicator and the factor loading of the indicator, and recalculate the verification commonality of the indicator based on the re-determined subordinate main factor; Step 5. Calculate the soil ecological comprehensive response index Q based on the verification commonality of each index preliminarily screened and the contribution rate of each index preliminarily screened n ; Among them, ψ i represents the verification commonality of the i-th indicator, ξ i Represents the contribution rate of the i-th indicator.

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