Earthworm manure quality evaluation method

Abstract

The invention belongs to the technical field of agricultural waste resource utilization, and particularly relates to a method for evaluating the quality of earthworm manure. The invention provides an earthworm manure quality prediction model and a model construction method, the model is used for evaluating the quality of earthworm manure, the grade division of the earthworm manure quality can be realized by collecting the infrared spectrum data of the earthworm manure, and the method has the advantages of rapidness, high efficiency, accuracy, reliability and low cost; the whole detection process is environment-friendly and pollution-free, the operation is simple, automation is easy to realize, and popularization and application are convenient.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of agricultural waste resource utilization, and particularly relates to a method for evaluating the quality of earthworm manure. BACKGROUND

[0002] As a kind of efficient organic fertilizer, earthworm manure has the advantages of improving soil quality and increasing crop yield and quality. Its quality directly affects its application effect and market value. However, due to the influence of raw material sources, fermentation process and other factors, the quality of earthworm manure varies greatly, and it is urgent to establish a scientific and efficient quality evaluation method. Although the traditional chemical analysis method is accurate, it has the disadvantages of complex operation and long time-consuming, which cannot meet the demand of rapid detection. Therefore, it is necessary to grade the quality of earthworm manure. The existing quality classification methods mainly include:

[0003] (1) Index content classification method [1] : that is, by detecting the content of organic matter, nitrogen, phosphorus and potassium in earthworm manure, the higher the content, the higher the grade. This method can reflect the content of each element in earthworm manure as a whole, but since the measured indexes are many and there is no correlation between them, it is difficult to comprehensively evaluate the quality of fertilizer according to the measured values of each index. The detection is complicated, time-consuming and laborious, and it is difficult to realize rapid detection of fertilizer. The detection result is the specific value of each index, and there is no clear correlation between each index, so it is impossible to accurately classify the grade by comprehensively considering the detection results of all indexes. However, the detection of these indexes takes a long time, which cannot meet the demand of rapid detection. Each index is measured separately, and it is difficult to form a comprehensive evaluation result. When the number of samples is too large, it is difficult to detect.

[0004] (2) Microbial content classification method [2-4] : by detecting the content of bacteria, fungi, actinomycetes, antagonistic microorganisms and soil active microorganisms in earthworm manure, the content of each type of microorganism is used to classify the quality of earthworm manure, the higher the content, the better the grade, and vice versa. This method can accurately reflect the content and effect of microorganisms in earthworm manure, but the effect of earthworm manure is not only reflected in the action of microorganisms, but also needs to be comprehensively evaluated in combination with nutrients, structure, etc. Moreover, the detection operation is complicated and time-consuming, which cannot meet the requirement of rapid and non-destructive detection.

[0005] The microbial content method can also be said to be one of the index content methods, but the method is determined and divided by the microbial content of the earthworm manure after use, and the content of beneficial microorganisms in the earthworm manure and the content of active microorganisms in the soil after use are determined to distinguish the grades of earthworm manure, which is difficult to comprehensively evaluate the content of nutrient elements in the earthworm manure, and the evaluation has certain limitations. When measuring, in addition to measuring the microbial content of the earthworm manure, the earthworm manure also needs to be applied to the soil, the measurement period is long, the data measurement is greatly affected by the nature of the soil itself, and deviation often occurs. Moreover, the detection result can only compare the high and low of the microbial content, it is difficult to evaluate other influencing factors in the earthworm manure, it is impossible to realize accurate grade division, and it is impossible to comprehensively evaluate the content of nutrient elements in the earthworm manure, and comprehensively evaluate the effect of the earthworm manure.

[0006] And the infrared spectrum technology has the advantages of fast, non-destructive, environmental protection and the like, and can reflect the molecular structure and chemical composition information of a substance. The chemometrics method can extract effective information from complex spectrum data, and establish a prediction model, thereby providing a new idea for rapid detection. SUMMARY

[0007] The purpose of the present application is to provide a method for grading earthworm manure quality, which realizes rapid and non-destructive detection of the quality of earthworm manure by combining infrared spectrum technology and chemometrics method, solves the problems of complicated operation, long time consumption and high cost in the prior art, and provides an effective means for quality control and grading utilization of earthworm manure.

[0008] The technical scheme of the present application is as follows:

[0009] A prediction model for the quality of earthworm manure, and the formula is:

[0010]

[0011] The construction method of the earthworm manure quality prediction model described above comprises the following steps:

[0012] (1) Sample collection and pretreatment: Collect earthworm manure samples, dry, crush, sieve, and obtain uniform powder samples;

[0013] (2) Infrared spectrum acquisition: Collect the infrared spectrum of the sample by a Fourier transform infrared spectrometer, with a wave number range of 400-4000 cm -1 , a resolution of 4 cm -1 , background subtraction, 16 cumulative scans per sample, and an average of three repeated tablet scans. To reduce the influence of instrument and environmental factors, a standard calibration sample is inserted every 10 samples for calibration;

[0014] (3) Spectrum pretreatment: Pretreat the original spectrum to eliminate interference factors and improve spectrum quality;

[0015] (4) Feature wave number screening and initial classification: Compare the influence of different characteristic wave number ranges on model performance through peak characteristics on the original infrared spectrum and principal component analysis (PCA) analysis results, and finally determine the optimal waveband. The earthworm manure spectrum data is initially classified by using chemometrics methods;

[0016] (5) PLS-DA grade division and model verification: Perform grade division and model verification by partial least squares regression-discriminant analysis (PLS-DA), test the grade prediction ability, and verify that the standard value is reached;

[0017] (6) PLS grade model establishment and verification and simplification: Based on the grade division and verification results in step (5), use partial least squares regression analysis (PLS) to establish a prediction model for the quality grade of earthworm manure, and use cross-validation and precision analysis methods to verify and optimize the model, evaluate the prediction accuracy and stability of the model, use variable weight value (VIP) calculation to select the wave number corresponding to the score greater than 1 from the screening waveband, and establish a simplified prediction model.

[0018] Further, in step (1), different raw materials are mixed according to a dry basis mass mixing mode and divided into carbon-rich (≥25) and nitrogen-rich (≤25) groups based on total C / N value for earthworm composting treatment.

[0019] Further, the composting time is 45 days.

[0020] Further, in step (2), the standard calibration sample is a polyethylene film.

[0021] Further, in the step (4), the characteristic wave number range is 1000-1800 cm -1 .

[0022] Further, in the step (3), the pretreatment includes baseline correction, smoothing, and standardization.

[0023] Further, in the step (4), the adopted chemometrics method includes principal component analysis (PCA) and hierarchical clustering (HCA).

[0024] Further, in the step (6), the wave numbers corresponding to the scores greater than 1 are in turn: 1410, 1409, 1408, 1407, 1406, 1405, 1404, 1403, 1402, 1401, 1400, 1399, 1398, 1397, 1396, 1395, 1394, 1393, 1392, 1391, 1390, 1389, 1234, 1233, 1232, 1231, 1230, 1229, 1228, 1227, 1226, 1225, 1224, 1223, 1222, 1221, 1220, 1219, 1218, 1217, 1216, 1215, 1214, 1213, 1212, 1211, 1210, 1209, 1208, 1207, 1206, 1205, 1204, 1203, 1202, 1201, 1200, 1199, 1198, 1197, 1196, 1195, 1194, 1193, 1192, 1191, 1190, 1189, 1188, 1187, 1186, 1185, 1184, 1183, 1182, 1181, 1180, 1179, 1178, 1177, 1176, 1175, 1174, 1173, 1172, 1171, 1170, 1169, 1168, 1167, 1166, 1165, 1164, 1163, 1162, 1161, 1160, 1159, 1158, 1157, 1156, 1155, 1154, 1153, 1152, 1151, 1150, 1149, 1148, 1147, 1146, 1145, 1144, 1143, 1142, 1141, 1140, 1139, 1138, 1137, 1136, 1135, 1134, 1133, 1132, 1131, 1130, 1129, 1128, 1127, 1126, 1125, 1124, 1123, 1122, 1121, 1120, 1119, 1118, 1117, 1116, 1115, 1114, 1113, 1112, 1111, 1110, 1109, 1108, 1107, 1106, 1105, 1104, 1103, 1102, 1101, 1100, 1099, 1098, 1097, 1096, 1095, 1094, 1093, 1092, 1091, 1090, 1089, 1088, 1087, 1086, 1085, 1084, 1083, 1082, 1081, 1080, 1079, 1078, 1077, 1076, 1075, 1074, 1073, 1072, 1071, 1070, 1069, 1068, 1067, 1066, 1065, 1064, 1063,1062, 1061, 1060, 1059, 1058, 1057, 1056, 1055, 1054, 1053, 1052, 1051, 1050, 1049, 1048, 1047, 1046, 1045, 1044, 1043, 1042, 1041, 1040, 1039, 1038, 1037, 1036, 1035, 1034, 1033, 1032, 1031, 1030, 1029, 1028, 1027, 1026, 1025, 1024, 1023, 1022, 1021, 1020, 1019, 1018, 1017, 1016, 1015, 1014, 1013, 1012, 1011, 1010, 1009, 1008, 1007, 1006, 1005, 1004, 1003, 1002, 1001 and 1000.

[0025] A method for evaluating the quality of vermicompost, comprising the following steps:

[0026] (1) Sample collection and pretreatment: The vermicompost sample to be tested is subjected to drying, crushing, and sieving to obtain a uniform powder sample;

[0027] (2) Infrared spectrum acquisition: The infrared spectrum of the sample is collected by a Fourier transform infrared spectrometer;

[0028] (3) The infrared spectrum data of the vermicompost sample to be tested are input into the above-mentioned prediction model to calculate Y value, and the quality grade thereof can be quickly obtained.

[0029] Further, in the step (2), when collecting the infrared spectrum of the sample, the background is deducted, each sample is accumulated for 16 times, and the average value of three repeated tablet scanning is taken. In order to reduce the influence of instrument and environmental factors, a standard sample is inserted every 10 samples for correction. The standard sample is a polyethylene film.

[0030] Further, in the step (2), the wave number range is 1000-1800 cm -1 , and the resolution is 4 cm -1 .

[0031] Further, in the step (3), the absolute value |Y| of Y value ranges from 12.0 for first-grade product, 8.0 for second-grade product, and 0-8.0 for third-grade product. The first-grade product is excellent, with excellent fertility and high humus content, long duration, and optimal for soil and crops; the second-grade product is good, inferior to the first-grade product; and the third-grade product is qualified, suitable for planting and soil.

[0032] Beneficial effects:

[0033] The model constructed by the construction method has the advantages that:

[0034] (1) Fast and efficient: without complex sample pretreatment, the detection can be completed within a few minutes, greatly improving the detection efficiency.

[0035] (2) Accurate and reliable: the prediction model is constructed by using chemometrics method, which can accurately divide the quality grade of earthworm manure.

[0036] (3) Low cost: without expensive chemical reagents, the detection cost is reduced.

[0037] (4) Environmentally friendly: the entire detection process does not require the use of toxic and harmful reagents, and is friendly to the environment.

[0038] (5) Easy to promote: the method is simple to operate and easy to automate, and is convenient for popularization and application. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0040] Figure 1 The infrared spectrum of the earthworm manure sample.

[0041] Figure 2 The PCA principal component analysis score plot.

[0042] Figure 3 The HCA system clustering analysis plot.

[0043] Figure 4 The PLS-DA partial least squares-discriminant analysis score plot.

[0044] Figure 5 The PLS partial least squares-discriminant analysis model verification plot.

[0045] Figure 6 The earthworm manure sample grading plot.

[0046] Figure 7 The earthworm manure VIP score plot, the vertical axis is the VIP score, and the horizontal axis is the wave number of each VIP score greater than 1. DETAILED DESCRIPTION

[0047] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0048] Embodiment 1

[0049] A method for evaluating the quality of earthworm manure, comprising the following steps:

[0050] (1) Sample collection and pretreatment: 10 different raw materials are mixed according to a dry basis mass mixing mode, and are divided into carbon-rich (≥25) and nitrogen-rich (≤25) groups based on total C / N value for earthworm composting for 45 days, and 30 samples are collected (15, 30, 45), which are immediately sealed and stored after sampling, and are treated within 24 hours. In the laboratory, the samples are first dried at 40°C to constant weight, then crushed by a crusher and passed through a 100-mesh sieve to obtain uniform powder samples.

[0051] (2) Infrared spectrum acquisition: the infrared spectra of the 30 samples are collected by a Fourier transform infrared spectrometer, with a wave number range of 400-4000 cm -1 , a resolution of 4 cm -1 , background subtraction, 16 cumulative scans for each sample, and three repeated tablet scans for average value.

[0052] (3) Spectrum pretreatment: the original spectrum is pretreated on the OMNIC software, including baseline correction, smoothing, standardization, etc., to eliminate interference factors and improve the spectrum quality.

[0053] (4) Feature wave number screening and initial classification based on PCA and HCA: the peak characteristics on the infrared original spectrum and the principal component analysis (PCA) analysis results are compared to determine the optimal wave band, and the feature wave number with strong correlation with the quality grade of earthworm manure is screened out as 1000-1800 cm -1 . Through SIMCA P14.0, the 1000-1800 cm -1 wave number is used for PCA / HCA / PLS-DA / PLS data statistics by using principal component analysis (PCA), hierarchical clustering (HCA) and other methods, and the wrong data is corrected. The earthworm manure spectrum data is divided into three groups, and the division standard is principal component analysis and clustering analysis. The two methods are used to divide the similarities and differences of each component, and similar ones are grouped.

[0054] (5) Grade division and model validation of PLS-DA: The earthworm castings were divided into three grades by partial least squares-discriminant analysis (PLS-DA), and the prediction accuracy reached 77%, and the left R 2 Y were all lower than the right R 2 Y and the intercept value <0.3, Q 2 Y (simulation) were all lower than the right real model Q 2 Y value and Q 2 <0.05, indicating that the model has no overfitting and has good prediction value.

[0055] (6) PLS grade model establishment and verification and simplification: Based on the grade division verification results in step 5, a partial least squares regression analysis (PLS) grade prediction model was established, and cross-validation and precision analysis methods were used to verify and optimize the model equation. The wave number with a score greater than 1 was obtained from the screening band by using the variable weight value (VIP), a total of 257 wave numbers, and a simplified model equation was established.

[0056] The simplified equation is as follows:

[0057]

[0058] (7) Quality grade division: the infrared spectrum data of the earthworm manure sample to be tested is input into the optimized PLS prediction model equation, the absolute value of Y is calculated, and the grade value is obtained. According to the grade value, the quality grade can be quickly obtained. The absolute value of the grade |Y| range: first-class product 12.0<|Y|, second-class product 8.0<|Y|≤12, third-class product 0≤|Y|≤8.0. The SIMCAp14.0 software is used for calculation, and the prediction accuracy of the model reaches 91% by taking the PLS least square regression analysis as a reference.

[0059] Among them, the first-class product is excellent, with the highest score, excellent fertility, high humus content and long duration, which is optimal for soil and crops. The second-class product is good, with a lower score than the first-class product. The third-class product is qualified, with the lowest quality among the three grades. All earthworm manure is suitable for planting and soil.

[0060] (I) Comparison of grading prediction ability of different wave number ranges

[0061] Under the operating conditions of Example 1, different infrared spectrum wave number ranges were designed as controls to observe the grading prediction ability and the grading prediction ability in different wave band ranges. Each wave band range was operated once. At the end of the experiment, the prediction of each group was recorded to determine the best wave number range of the prediction ability. The specific effect is shown in Table 1.

[0062] Table 1 Comparison of prediction ability of different wave number ranges

[0063]

[0064] As shown in Table 1, the use of complete wave band infrared spectrum for chemometrics analysis found that the selection of wave band range directly affected the effect of chemometrics grade division. When the complete wave band infrared spectrum was used for chemometrics analysis, too many peak values led to the classification of various earthworm manure into one category in principal component and cluster analysis, and the grading ability was poor. Although the R 2 Y value was as high as 0.985, the prediction ability was lower than 0.5 and the goodness of fit was greater than 0.4 in model validation, which was lower than that of the example, the model was over-fitted, and it was difficult to realize grade division.

[0065] (II) Influence of different validation methods and precision analysis methods on model accuracy and stability

[0066] A prediction model of vermicompost quality grade is established by using partial least square regression analysis (PLS), and the model is verified and optimized by using cross-validation and precision analysis. Different verification methods and precision analysis methods are used to analyze the data, and the influence of different verification methods on precision is compared.

[0067] Table 2 Cross-validity and precision analysis of PLS regression of vermicompost grade classification

[0068]

[0069] As shown in Table 2, the cross-validity of grade classification is 0.807 when the first component is extracted, 0.535 when the second component is extracted, and 0.0253 when the third component is extracted. Generally, the cross-validity of all extracted components is greater than 0.0975, and the new component has a significant improvement on the prediction effect of the model. Some studies also point out that R2X (variable X explanation ability) increases by how much the explanation ability of variable X increases by one principal component, and R2Y (variable Y explanation ability) indicates how much the explanation ability of variable Y increases by one principal component. In the study, R2X of the third extracted component is 0.0295, and R2Y is 0.0197, indicating that the explanation ability of variable X increases by 2.95% and the explanation ability of variable Y increases by 1.97% with the addition of one principal component. Therefore, in combination with the above conclusion, in order to better analyze the results, the present application extracts the third component for analysis. As shown in the table, R2X (X variable cumulative explanation ability) is 0.954, indicating that the explanation ability of the model for variable X is 95.4%; R2Y (Y variable cumulative explanation ability) is 0.942, and the principal component can explain 94.2% of the variation of variable Y; Q2 (model prediction ability) is 0.913 (> 0.5), indicating that the model prediction ability is better. Therefore, the prediction ability of the finally established model is significantly improved.

[0070] (III) Examples of vermicompost sample grade classification

[0071] 30 samples of vermicompost from different sources are classified by using index content classification method, microorganism content classification method and the method of the present application.

[0072] The operation method of index content classification method is as follows: the contents of three fertilizer macroelements N, P and K are determined, and the quality of vermicompost is classified according to the contents of each element. The higher the element content, the better the grade.

[0073] The operation method of microorganism content classification method is as follows: the contents of bacteria, fungi, actinomycetes, antagonistic microorganisms and soil active microorganisms after application in vermicompost are detected, and the contents of various microorganisms are classified.

[0074] Meanwhile, banana seedling cultivation experiments are adopted to verify the fertilizer efficiency of the earthworm manure. The operation method is as follows: healthy banana seedlings with similar growth and 3 leaves are selected, and the seedlings are planted in fine sand pots after being washed. The earthworm manure is added once every 7 days (10 g per seedling in terms of the dry weight of the manure), 10 seedlings are used in each treatment, and after continuous cultivation for one month, the average score of the growth state of the seedlings in each treatment is calculated by taking irrigation of banana sand culture nutrient solution (30 ml per seedling) as the control group. The specific results are shown in Table 1.

[0075] The seedling growth state score statistical method is as follows: 1 point - abnormal: compared with the control group, the seedling plant is short, the stem is thin, the leaf color is changed, there are more yellow leaves or obvious wilting and curling, and the root system growth is limited.

[0076] 2 points - normal: compared with the control group, there is no obvious difference in the growth state.

[0077] 3 points - improved: compared with the control group, the growth state is obviously improved, the seedling stem is thick, the leaf number is increased, the leaf is obviously dark green, there are few or no yellow leaves, and the root system is more developed.

[0078] Table 3 quality grade evaluation results of different earthworm manure samples

[0079]

[0080]

[0081] In Table 3, the quality of 30 earthworm manure samples is evaluated by using the grade division method described in the application, the highest absolute value can reach 12.47736, and the highest 6.249789, which has a significant difference. It is shown that by using the method described in the application, different earthworm manure samples can be distinguished, and there is a significant difference between the samples.

[0082] According to the grades divided in Table 3, the samples numbered 15 and 16 both belong to the second grade, and the absolute values of the two samples are not obviously different. However, the contents of N, P and K of the two samples are determined by the index content determination method, and it is found that the content of N of the sample numbered 16 is obviously higher than that of the sample numbered 16, and the contents of P and K are not obviously different. According to the results of the index content determination method, the quality of the sample numbered 15 and 16 can be compared by comparing the contents of the three nutrients, but since the contents of the three nutrients are high and low, it is difficult to comprehensively evaluate the quality of the sample 15 and the sample 16 by comprehensively considering the contents of the three nutrients, and it is difficult to accurately divide the sample 15 and the sample 16. The samples 15 and 16 are tested by the microbial content division method, and the microbial content of the sample 15 before being applied to the soil is higher than that of the sample 16, but the active microbial content of the soil after being applied is higher than that of the sample 15, and it is also difficult to comprehensively evaluate by comprehensively considering the two microbial determination indexes. The two earthworm manure samples are applied to the same batch of banana seedlings, and the growth of the banana seedlings of the two treatments is not obviously different. Therefore, the difference in fertility of the two earthworm manure samples is not large, which is consistent with the grade division result of the grade evaluation method of the present application.

[0083] The samples numbered 21 and 22 are tested, and the absolute values of the two samples are very close, and both belong to the third grade, but the contents of N, P and K of the two samples are determined by the index content determination method, and the contents of P and K of the sample numbered 21 are obviously higher than those of the sample numbered 22, and the content of N of the sample numbered 22 is higher than that of the sample numbered 21. The samples 21 and 22 are tested by the microbial content division method, and the microbial content of the two samples before being applied to the soil is not obviously different, but the active microbial content of the soil after being applied is higher than that of the sample 21, and if the two determination results are comprehensively considered, the quality grade of the sample 22 is higher than that of the sample 21. The two earthworm manure samples are applied to the same batch of banana seedlings, and the growth of the banana seedlings is not obviously different. Therefore, according to the cultivation experiment results of the banana seedlings, the fertilizer efficiency of the sample 21 and the sample 22 is not obviously different, which indicates that the determination result of the present application is more accurate.

[0084] The contents of N, P and K of the samples 25 and 13 are not obviously different, and if the determination results are used, the quality grades of the two samples are not obviously different. The microbial content of the sample 25 before being applied to the soil is less than that of the sample 13, and the microbial content of the soil after being applied is greater than that of the sample 13, and it is difficult to comprehensively evaluate the quality grade. After the banana seedlings are applied, the growth score of the sample 25 is obviously higher than that of the sample 13, which indicates that the fertilizer efficiency of the sample 25 is higher than that of the sample 13.

[0085] Through the comparison of the above results, for the sample which is difficult to evaluate the quality by the index content determination method and the microbial content determination method, the evaluation of the present application can accurately divide the quality grade, and the evaluation result is closer to the seedling cultivation experiment result, which shows that the grade evaluation method of the present application can not only more accurately divide the quality grade of the earthworm manure, but also the evaluation result of the quality grade is more accurate.

[0086] Conclusion

[0087] The present application provides a method for quickly dividing the quality grade of earthworm manure, which uses Fourier transform infrared spectroscopy technology combined with chemometrics method to realize the rapid and non-destructive detection of the quality of earthworm manure. The research results show that the established quality grade division model has high accuracy and reliability, and can effectively distinguish earthworm manure of different quality grades.

[0088] Compared with traditional chemical analysis methods, the present application has the advantages of simple operation, rapid and efficient, environmental protection and non-destructive, etc., and provides a powerful tool for quality control and grading utilization of earthworm manure. The application of the present application will help to improve the standardization level of earthworm manure production and promote the healthy development of organic fertilizer industry. Future research can further optimize the model algorithm, expand the sample range, and explore the application potential of the method in the quality evaluation of other types of organic fertilizer.

[0089] The above only describes the preferred embodiments of the present application and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0090] Reference:

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[0092] [2] Liu X C. Effects of earthworm manure on tomato root zone environment, yield and quality [D]. Shandong Agricultural University, 2020.

[0093] [3] Zhao NN, Pan XH, Chen Q, et al. Inhibition of potato pathogenic bacteria by earthworm manure and activated sludge leachate [J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(12): 230-236.

[0094] [4]Hoeffner K,Monard C,Santonja M,et al.Feeding behaviour of epi-anecic earthworm species and their impacts on soil microbial communities[J].Soil Biology and Biochemistry,2018,125:1-9.

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