AU2020101607A4

AU2020101607A4 - Method for rapidly predicting nitrogen and phosphorus content in slurry movement routes of multiple different large-scale dairy farms by comprehensively integrating all factors

Info

Publication number: AU2020101607A4
Application number: AU2020101607A
Authority: AU
Inventors: Mengting Li; Meirui MU; Di SUN; Peng Wang; Renjie YANG; Run ZHAO
Original assignee: Tianjin Agricultural University; Agro Environmental Protection Institute Ministry of Agriculture and Rural Affairs
Current assignee: Tianjin Agricultural University; Agro Environmental Protection Institute Ministry of Agriculture and Rural Affairs
Priority date: 2019-08-28
Filing date: 2020-08-01
Publication date: 2020-09-10
Anticipated expiration: 2028-08-01
Also published as: CN110514618A

Abstract

The present invention discloses a method for rapidly predicting nitrogen (N) and phosphorus (P) content in slurry movement routes of different large-scale dairy farms by comprehensively integrating all factors. The present invention is based on a number of large-scale dairy farms with a typical planting and breeding mode to predict the N and P content in the complex slurry system through field investigation, sampling and measurement of slurry and biogas slurry, spectral collection and mathematical modeling. The slurry system has large quantity, high concentration, contains many suspended particles, and is mixed with straw and sludge, etc. The present invention carries out rapid detection and modeling by near-infrared diffuse reflectance spectroscopy by combining all dynamic influencing factors, including different regions, breeding scales, herd ratios, manure removal approaches and slurry treatment processes. While realizing rapid and accurate quantitative prediction on site, the present invention replaces conventional monitoring procedures, and solves the problem of difficulty in returning the slurry from the large-scale dairy farms to the field, providing technical support for promoting the green transformation of the dairy industry. 1/3 Sluryfro : Surrfrom *milking parlors m barns % S- -- ---- - •Biogasplant Storage tank Collection u7Regulating Sedimentat takcollection Slurry tank tank ion tank utkerg Separation Lagoo tank FIG. 1 2.4 2.1 1.8 0 .~1.5 1.2 0.9 4000 6000 8000 10000 12000 Wavenumber/cm-2 FIG. 2

Description

1/3

*milking parlors m : Sluryfro Surrfrom barns

% S- -- ---- - •Biogasplant Storage tank

Collection u7Regulating Sedimentat takcollection Slurry tank tank ion tank utkerg

Separation Lagoo tank

FIG. 1

2.4

2.1

1.8

0 . ~1.5

1.2

0.9

4000 6000 8000 10000 12000 Wavenumber/cm-2 FIG. 2

METHOD FOR RAPIDLY PREDICTING NITROGEN AND PHOSPHORUS CONTENT IN SLURRY MOVEMENT ROUTES OF MULTIPLE DIFFERENT LARGE-SCALE DAIRY FARMS BY COMPREHENSIVELY INTEGRATING ALL FACTORS TECHNICAL FIELD The present invention belongs to the technical field of monitoring and measurement, and in particular relates to a method for rapidly predicting nitrogen (N) and phosphorus (P) content in slurry samples throughout a movement process of multiple different large-scale dairy farms under multivariate factors. BACKGROUND Slurry management becomes a bottleneck for large-scale dairy farms in China, and needs to be solved urgently for environmental protection. Domestic and international practical experience has shown that as the core of planting and breeding, returning slurry to the field is the fundamental way to handle a large amount of slurry in large-scale dairy farms. At present, it is difficult to rapidly quantify the important nutrients of nitrogen (N) and phosphorus (P) contained in the high-concentration slurry on the spot, resulting in difficulty in returning slurry to the field. Conventional measurement methods feature low time effectiveness and high cost, and cannot immediately monitor the variation of N and P content in the whole process of slurry treatment. At present, large-scale dairy farms have different breeding scales and manure removal and treatment approaches, which vary greatly in the actual operating situation. When the slurry amount is large, the slurry is subjected to solid-liquid separation, and the liquid segment after separation is used to cyclically flush the slurry collection gutter. When the slurry amount is small, the slurry is directly cleaned out of the barn for storage. Some dairy farms only allow the slurry from the lactating cow barns to enter the treatment system, while some also allow the manure from the heifer barns to enter the system. The wastewater from the milking parlors is discharged to different facilities. These operations are likely to cause variations in the N and P content in the movement of the slurry, resulting in the difficulty of rapidly predicting the N and P content in the slurry from any treatment step, thereby affecting the effect of returning to the field. Therefore, there is an urgent need to develop a rapid prediction and analysis method for N and P content in the slurry through the whole treatment process of different types of large-scale dairy farms under multivariate factors on site. The intensive policy documents issued by China clearly pointed out that it is necessary to improve the standard system for the utilization and detection methods of animal manure and slurry returning to the field, so as to promote the returning of slurry through the formulation of technical specifications. Therefore, the establishment of an on-site rapid prediction and analysis method for N and P content in the slurry of different types of large-scale dairy farms can effectively guide the safe and scientific returning of the slurry to the field to avoid risks of environmental pollution. As a rapid analysis method, spectral detection has been widely used in the detection of animal manure or compost sample components. For example, near-infrared spectroscopy (NIRS) is used to quantitatively analyze N, P and potassium (K) in chicken manure. Fourier-transform infrared spectroscopy (FTIR) is used to study the conversion of organic matter in the composting of pig manure, characterize the evolution of water-soluble organic matter in the composting of cow manure, and study the changes of infrared characteristics in the composting of pig manure. The existing methods test and analyze static manure or compost samples under a single factor, and cannot predict the total nitrogen (TN) and total phosphorus (TP) of slurry that dynamically moves in different types of large-scale dairy farms. The complex factors such as the environment, breeding scale, herd division, manure removal approach and slurry treatment process of dairy farms interact in real time to cause changes in the composition and concentration of the slurry in various movement steps, affecting the prediction and analysis of the models. Therefore, it is necessary to establish a general prediction model for the N and P content of slurry in the whole movement process of different dairy farms, so as to realize instant analysis in any step under any environmental conditions. SUMMARY In order to overcome the shortcomings of the prior art, an objective of the present invention is to provide a method for rapidly predicting nitrogen (N) and phosphorus (P) content in slurry movement routes of multiple different large-scale dairy farms by comprehensively integrating all factors. The present invention is achieved by the following technical solutions: A method for rapidly predicting N and P content in slurry movement routes of different large-scale dairy farms by comprehensively integrating all factors, including the following steps: (1) collecting slurry samples from slurry movement steps (facilities) (Qi, Q2, Q3...QN) Of multiple different types of large-scale dairy farms (Pi, P2, P3...Pm) in different regions, with reference to GB/T 27522-2011 "Technical Specificationsfor Waste Water Sampling ofLivestock and Poultry Farm" and DB 12/T 655-2016 "Technical Regulations for Environmental Monitoring of Large-scale Dairy Farms"; where, the slurry movement facilities range from facilities for collecting and storing slurry from barns and milking parlors to final storage facilities before the slurry is returned to the field, covering collection tanks, slurry collection gutters, slurry tanks, separation tanks, regulating tanks, sedimentation tanks, storage tanks and lagoons; the different types include different feeding approaches, breeding scales, herd ratios, manure removal approaches and slurry treatment processes;

(2) collecting near-infrared diffuse reflectance spectra of all slurry samples obtained in step (1) separately by using a near-infrared spectrometer, to obtain a near-infrared diffuse reflectance spectral matrix X (Pi,Qk) of the slurry samples from various steps of the dairy farms, where i=1 m, k=1-n; (3) detecting total nitrogen (TN) in the slurry samples from various facilities with reference to GB 11891-1989 "Water Quality - Determination ofKjeldahl Nitrogen", and detecting total phosphorus (TP) in the slurry samples from various facilities with reference to GB11893-1989 "Water Quality - Determination of Total Phosphorus - Ammonium Molybdate SpectrophotometricMethod", to obtain a TN matrix YN and a TP matrix YP of each sample; (4) eliminating abnormal samples in steps (2) and (3), optimizing preprocessing methods, i.e. near-infrared diffuse reflectance spectra, and selecting optimal wavebands; (5) using optimized parameters to establish quantitative analysis models of TN and TP in the slurry samples in the treatment process of the large-scale dairy farms under all factors, namely, YN,P=X(Pi,Qk);

(6) scanning near-infrared diffuse reflectance spectra of unknown slurry samples collected from the whole slurry treatment process of any of the large-scale dairy farms to obtain a near infrared diffuse reflectance spectral matrix X'(Pi,Qk) of the unknown slurry samples, and substituting the spectral matrix into the quantitative analysis models obtained in step (5) to obtain predicted values Y'N and Y'p of the TN and TP in the unknown slurry samples from the large scale dairy farm. In the above technical solution, in step (1), the slurry samples are collected from the various steps (facilities) in parallel for multiple times under different time, temperature and weather conditions. The collection tanks are facilities for collecting and storing wastewater of milking parlors; the slurry collection gutters are facilities for gathering slurry from bams; the slurry tanks are facilities where all slurry in the dairy farms converges; the separation tanks are facilities for temporarily storing the slurry after solid-liquid separation; the regulating tanks are facilities for homogenization and conditioning before the slurry enters a biogas plant; the sedimentation tanks are facilities for separating biogas slurry and biogas residues after anaerobic fermentation; the storage tanks and the lagoons are facilities for storing the slurry before returning the slurry to the field. In step (1), the samples are collected as follows: using a self-made 1 L stainless steel bucket, a 500 mL scoop or other tools to randomly collect slurry samples at 3 sites 10-20 cm vertically below a liquid level at sampling points of various facilities; mixing the slurry samples well in a 20 L mixing bucket by a scoop, taking about 400 mL of slurry into a 500 mL slurry collection bottle; placing the slurry collection bottle in a sample incubator equipped with an ice pack, and immediately sending the slurry to a laboratory for testing. In step (2), the near-infrared spectrometer is a Fourier-transform near-infrared spectrometer (InGaAs detector) of American PerkinElmer, which scans in the range of 4,000-12,000 cm-i. In step (2), the near-infrared diffuse reflectance spectra are measured as follows: fully shaking the to-be-tested slurry samples in the slurry collection bottle; using a 3 mL disposable dropper to take 2-3 mL of slurry from the middle of the slurry collection bottle into a sample cuvette; placing the sample cuvette on a rotating sample stage (provided with an integrating sphere in which a background is built as a reference), where the spectral scanning parameters include: resolution: 8 cm-i, scan interval: 2 cm-i, and number of scans: 64. In step (3), the TN is measured by a fully automatic Kjeldahl nitrogen analyzer (Foss kjeltec 8400, Denmark), and the TP is measured by a visible light spectrophotometer (722E, China). In step (4), abnormal spectra are eliminated by Monte Carlo cross validation (MCCV). In step (4), samples after eliminating the abnormal samples are divided into a calibration set and a prediction set; samples in the calibration set are used to establish the quantitative analysis mathematical models, and samples in the prediction set are used to verify the accuracy and stability of the established mathematical models; the correction set and the prediction set are selected by using a Kennard-Stone (K-S) method. In step (4), the spectral preprocessing methods include one or more of normalization, multivariate scatter correction (MSC), baseline correction, standard normal variate (SNV), Savitzky-Golay (SG) smoothing + normalization and SG smoothing + baseline correction; normalization is the optimal preprocessing method for the TN; SG smoothing + baseline correction is the optimal preprocessing method for the TP. In step (4), a waveband of 4,400-8,800 cm-i (2,201 wavenumber variables) is selected to establish the quantitative analysis mathematical model of the TN in the slurry, and a waveband of 4,000-8,000 cm-i (2,001 wavenumber variables) is selected to establish the quantitative analysis mathematical model of the TP in the slurry. The present invention has the following beneficial effects: The present invention is based on a number of large-scale dairy farms with a typical planting and breeding mode to predict the N and P content of the slurry from various steps in the entire treatment process through field investigation, sampling and measurement of slurry and biogas slurry, spectral collection and mathematical modeling. The present invention carries out rapid detection and modeling by near-infrared diffuse reflectance spectroscopy by combining all dynamic influencing factors, including different regions, breeding scales, herd ratios, manure removal approaches and slurry treatment paths. While realizing rapid and accurate quantitative prediction on site, the present invention replaces conventional monitoring procedures, and solves the problem of difficulty in returning the slurry from the large-scale dairy farms to the field, providing technical support for promoting the green transformation of the dairy industry. BRIEF DESCRIPTION OF DRAWINGS FIG. 1 shows a slurry treatment process route and sampling site distribution of a sampling object in a method of the present invention. FIG. 2 shows raw near-infrared diffuse reflectance spectra of all samples according to an example of the present invention. FIG. 3 shows statistical distribution (mean (MEAN) and standard deviation (STD)) of root mean square errors of prediction (RMSEP) of totally 138 samples (total nitrogen (TN) model) obtained by randomly sampling for 1,000 times. FIG. 4 shows a score chart of principal components (PCs) of near-infrared diffuse reflectance spectra according to an example of the present invention. FIG. 5 shows a linear fitting diagram of TN predicted by a linear fitting model and measured TN according to an example of the present invention. FIG. 6 shows a linear fitting diagram of total phosphorus (TP) predicted by a linear fitting model and measured TP according to an example of the present invention. DETAILED DESCRIPTION In order to make the present invention more comprehensible for those skilled in the art, the technical solutions of the present invention are further described below with reference to the specific examples. Example 1. Sample collection In the present invention, all the factors include: time and space (seasons, regions where the dairy farms are located), breeding scales, herd ratios at different growth stages, manure removal approaches and slurry treatment processes. Usually, the feces and urine of lactating cows access to the treatment system. If the manure of heifers accesses to the treatment system, the concentrations of nitrogen (N) and phosphorus (P) will be affected. The manure removal approaches include dry removal, water flushing, deep litter, dry removal + dry removal, dry removal+ water flushing and dry removal+ bedding, etc. The slurry treatment processes include anaerobic fermentation, storage as biogas slurry, lagoon and different combinations thereof. In this example, a total of 85 normal production large-scale dairy farms in Tianjin of China were investigated, and 23 large-scale dairy farms adopting a typical planting and breeding mode were selected from 5 districts around the city where dairy farms were concentrated, including Wuqing District, Binhai New District, Jinghai District, Ninghe District and Beichen District. The selected dairy farms bred Chinese Holstein dairy cows. The herds were propagated and raised by the dairy farms themselves, and were divided according to growth stages. 80% of the dairy farms had more than 10 years of operating history and ran stably all year round. The lactating cows were mostly housed in a cubic barn with a bedding of dry cow dung after solid-liquid separation, and heifers were housed in a free stall bam. All slurry produced in lactating barns entered the treatment system. These dairy farms differed in total stock scale (ranging from 400 to 5,000), manure removal approaches and slurry treatment processes. The manure removal approaches included dry removal, dry removal - water flushing and water flushing. The slurry collection and storage facilities included slurry collection gutters, slurry collection sewers and collection tanks, etc. The manure removal facilities included manure scrapers, forklifts and manure suction trucks, etc. The slurry treatment approaches included solid-liquid separation, anaerobic digestion and aerobic fermentation, etc. The slurry treatment facilities included slurry tanks, separation tanks, regulating tanks, sedimentation tanks and lagoons, etc. The slurry treatment equipment included upflow solids reactor (USR), upflow anaerobic sludge blanket (UASB), continuous stirred-tank reactor (CSTR), plug flow reactor (PFR) and other anaerobic reactors. The manure of heifers in some dairy farms entered the slurry treatment system, and the wastewater from the milking parlors of some dairy farms entered the treatment system. Overall, the 23 typical large-scale dairy farms covered all the on-site elements that might have an impact on the determination of N and P content in the slurry. According to GB/T 27522-2011 "Technical Specifications for Waste Water Sampling of Livestock and PoultryFarm"and DB 12/T 655-2016 "TechnicalRegulationsforEnvironmental Monitoring ofLarge-scale Dairy Farms", samples were collected from the slurry collection and storage facilities in the barns and milking parlors to the final storage facilities before the slurry was returned to the field. The sampling process covered all manure movement facilities (whole process elements), including the collection tanks, the slurry collection gutters, the slurry tanks, the separation tanks, the regulating tanks, the sedimentation tanks, the storage tanks and the lagoons. FIG. 1 shows a slurry treatment process route and sampling site distribution of the sampling object. The collection tanks were facilities for collecting and storing wastewater of milking parlors; the slurry collection gutters were facilities for gathering slurry from barns; the slurry tanks were facilities where all slurry in the dairy farms converged; the separation tanks were facilities for temporarily storing the slurry after solid-liquid separation; the regulating tanks were facilities for homogenization and conditioning before the slurry entered a biogas plant; the sedimentation tanks were facilities for separating biogas slurry and biogas residues after anaerobic fermentation; the storage tanks and the lagoons were facilities for storing the slurry before returning the slurry to the field. The facilities (tanks) were usually connected through underground concealed pipes or trenches, and hoses and pumps were used to transport the slurry in unusual times.

According to GB/T 27522-2011 "Technical Specifications for Waste Water Sampling of Livestock and Poultry Farm", a self-made 1 L stainless steel bucket, a 500 mL scoop or other tools were used to randomly collect slurry samples at 3 sites 10-20 cm vertically below a liquid level at sampling points of various facilities. The slurry samples were mixed well in a 20 L mixing bucket by a scoop, and about 400 mL of slurry was taken into a 500 mL slurry collection bottle. The slurry collection bottle was placed in a sample incubator equipped with an ice pack, and immediately sent to a laboratory for testing. A total of 141 slurry samples were collected from the 23 different types of large-scale dairy farms. By integrating the various realistic influencing factors such as the geographic locations, breeding scales, manure removal approaches and slurry treatment processes of the 23 dairy farms, this example establishes comprehensive quantitative analysis and prediction models. The models are suitable for the rapid prediction of N and P content in the slurry of different types of dairy farms on site, and realizes the rapid analysis and prediction of total nitrogen (TN) and total phosphorus (TP) in the slurry of large-scale dairy farms by means of near-infrared spectroscopy (NIRS) under the combined action of various dynamic factors. 2. Collection of near-infrared diffuse reflectance spectra The test was conducted by using a Fourier-transform near-infrared spectrometer (InGaAs detector) of American PerkinElmer, which scans in the range of 4,000-12,000 cm-i. The to-be tested slurry sample in the slurry collection bottle was fully shaken. Then a 3 mL disposable dropper was used to take 2-3 mL of slurry from the middle of the slurry collection bottle into a sample cuvette. The sample cuvette was placed on a rotating sample stage (provided with an integrating sphere in which a background was built as a reference) to collect the near-infrared diffuse reflectance spectrum of each sample. The spectral scanning parameters included: resolution: 8 cm-i, scan interval: 2 cm-i, and number of scans: 64. FIG. 2 shows raw near-infrared diffuse reflectance spectra of all samples. 3. Determination of TN and TP According to the method as specified in the GB 11891-1989 "Water Quality - Determination ofKjeldahl Nitrogen", the TN in the slurry was measured by a fully automatic Kjeldahl nitrogen analyzer (Foss kjeltec 8400, Denmark). According to the method as specified in the GB11893 1989 "Water Quality - Determination of Total Phosphorus - Ammonium Molybdate Spectrophotometric Method", the TP in the slurry was measured by a visible light spectrophotometer (722E, China) The TN and TP in 138 slurry samples collected from the 23 large-scale dairy farms were measured, and the statistic results are shown in Table 1. It shows that the TN and TP in the slurry from each step of the whole treatment process of different dairy farms vary greatly due to various combined factors such as breeding scale, manure removal method and slurry treatment process.

Table 1 Statistic results of TN and TP of slurry samples from 23 large-scale dairy farms

Composition Numbersof Concentration MEAN (mg/L) STD (mg/L) samples (mg/L) TN 138 45.14-5262.30 1600.00 1215.58 TP 138 1.89-141.20 32.22 23.64 4. Modeling method 4.1 Sample selection for modeling In this study, abnormal samples were eliminated by using Monte Carlo cross validation (MCCV). FIG. 3 shows statistical distribution (mean (MEAN) and standard deviation (STD)) of root mean square errors of prediction (RMSEP) of the 138 samples (TN model) obtained by randomly sampling for 1,000 times. The threshold of the MEAN was set to 1,500 and the threshold of the STD was set to 300. A total of 7 abnormal samples including 7, 15, 52, 54, 69, 71 and 126 were eliminated. As for the TP model, the same method was used to eliminate 8 abnormal samples from the 138 samples. After the abnormal samples were eliminated, the sample set was divided into a calibration set and a prediction set. The samples in the calibration set were used to establish mathematical models for calibration, and the samples in the prediction set were used to verify the accuracy and stability of the models. To ensure that the established quantitative analysis models accurately predicted the TN and TP of the slurry from each step of the whole treatment process of the large scale dairy farms, the samples in the calibration set were required to contain representative samples from each step of the dairy farms. In this study, the correction set and the prediction set were selected by using a Kennard-Stone (K-S) method, and the results are shown in Table 2. Table 2 Calibration set and prediction set divided by K-S method

Model Class Number of Concentration MEAN (mg/L) samples (mg/L) Calibration set 90 45.14-5262.30 1796.50 Prediction set 41 109.11-3328.30 1340.40 Calibration set 91 1.89-84.40 29.02 TP Prediction set 39 4.76-82.96 31.45 4.2 Algorithm selection for modeling In the field detection of TN and TP in the slurry from each step of the large-scale dairy farms, the applicability, stability and prediction performance of a more complex model algorithm are more susceptible to external factors. Partial least squares (PLS) is the most commonly used method for spectral multivariate correction, and is widely used in the establishment of quantitative models of near infrared, Raman and fluorescence spectra. As a general method for establishing a spectral quantitative prediction model, PLS has been practically applied in field detection. Therefore, the present invention uses the PLS algorithm to establish comprehensive mathematical models for on-site rapid quantitative analysis of TN and TP throughout the slurry treatment process of the large-scale dairy farms.

4.3 Preprocessing method selection for modeling The raw near-infrared diffuse reflectance spectra were preprocessed by using different methods. A multivariate regression model was established by using the PLS algorithm, and an optimal modeling factor number (principal component number) was selected by using root mean square errors of cross validation (RMSECV). Table 3 shows the PLS regression results of different preprocessing methods. The coefficient of determination R2, root mean square error of calibration (RMSEC)and root mean square error of prediction (RMSEP) indicate that the normalization method is the optimal preprocessing method for the PLS regression model of the TN; and Savitzky-Golay (SG) smoothing + baseline correction is the optimal preprocessing method for the TP.

Table 3 PLS regression results of different preprocessing methods Preprocessing TN TP methods Factor R2 RMSEC RMSEP Factor R2 RMSEC RMSEP number number Unprocessed 5 0.88 414.38 447.62 4 0.78 9.47 9.99 Normalization 5 0.90 373.18 405.70 5 0.81 8.98 10.04 MSC 4 0.87 438.59 470.73 6 0.79 9.36 10.12 Baseline correction 6 0.89 397.74 439.61 6 0.81 8.84 9.62 SNV 5 0.88 418.84 457.56 6 0.79 9.25 10.16 SG smoothing + 5 0.90 373.19 414.93 5 0.81 8.98 10.04 normalization SG smoothing + baseline 5 0.88 414.53 449.49 6 0.82 9.73 9.38 correction 4.4 Waveband selection for modeling If 4,001 variables of the full waveband (12,000-4,000 cm-i) are used for modeling, since these variables include redundant information unrelated to the components to be tested, the modeling efficiency will be reduced. In the present invention, the interval partial least squares (iPLS) was used to select the effective wave number variables needed to establish the mathematical models of TN and TP in the slurry. Through calculation, a waveband of 4,400 8,800 cm-i (2,201 wavenumber variables) and a waveband of 4,000-8,000 cm-i (2,001 wavenumber variables) were selected to establish the mathematical models of the TN and TP in the slurry, respectively. In the present invention, self-written PLS Matlab codes were used to establish the quantitative analysis models, the Unscrambler 9.7 software was used to preprocess the spectral data, and all calculations were conducted using the MatlabR2017a software (Mathwork Inc.).

5. Principal component analysis (PCA) In order to clarify the correlation and characteristic differences between the slurry samples from the treatment process in different dairy farms and the impact on the quantitative analysis models, the near-infrared diffusion reflectance spectra of the 138 manure samples from the 23 large-scale dairy farms in Tianjin were subjected to PCA. These samples were affected by different feed inputs (feed formula and feed volume), feeding approaches, manure removal approaches and slurry treatment processes. FIG. 4 is a score chart of the first two principal components (PCs), which explain 64% and 33% of the total spectral variables, respectively. The overall sample distribution shows that most samples of the same dairy farm are closer. In this figure, 5 ellipses are provided to illustrate that the samples included in each ellipse are from the same dairy farm. The largest number of samples was collected from a dairy farm in Binhai New District. Samples 94 and 95 were from another dairy farm, which provided only these two samples. The above results indicate that the samples collected from different dairy farms are more different than those collected from the different treatment steps of the same dairy farm. 6. Establishment of quantitative analysis models and detection of TN and TP of unknown slurry samples PLS mathematical models were established based on the processed spectral data to quantitatively analyze the TN and TP in the slurry samples from various treatment steps of different types of dairy farms, and the RMSECV of the models were 355.74 and 8.13, respectively. FIG. 5 shows a linear fitting diagram of TN (of 41 unknown samples included in the prediction set) predicted by the established TN model and measured TN. The fitting relationship was: Cpredicted0.9lCmeasured-0.84, the fitting correlation coefficient R was 0.96, and the RMSEP was 238.59. FIG. 6 shows a linear fitting diagram of TP (of 38 unknown samples included in the prediction set) predicted by the established TP model and the measured TP. The fitting relationship was: CpredictedO.8Cmeasure+7.12, the fitting correlation coefficient R was 0.91, and the RMSEP was 6.56. The above results indicate that the predicted value and the measured value have a high degree of fit and the prediction effect is ideal. Therefore, it is completely feasible to realize the detection of TN and TP in the slurry samples from all treatment steps of different dairy farms based on near-infrared diffuse reflectance spectroscopy.

Claims

What is claimed is: 1. A method for rapidly predicting nitrogen (N) and phosphorus (P) content in slurry movement routes of different large-scale dairy farms by comprehensively integrating all factors, comprising the following steps: (1) collecting slurry samples from slurry movement steps (facilities) (Qi, Q2, Q3...QN) Of multiple different types of large-scale dairy farms (P, P2, P3...Pm), wherein, the slurry movement facilities range from facilities for collecting and storing slurry from barns and milking parlors to final storage facilities before the slurry is returned to the field, covering collection tanks, slurry collection gutters, slurry collection tanks, separation tanks, regulating tanks, sedimentation tanks, storage tanks and lagoons; the different types comprise different feeding approaches, breeding scales, herd ratios, manure removal approaches and slurry treatment processes; (2) collecting near-infrared diffuse reflectance spectra of all slurry samples obtained in step (1) separately by using a near-infrared spectrometer, to obtain a near-infrared diffuse reflectance spectral matrix X (Pi,Qk) of the slurry samples from various steps in all the dairy farms, wherein i=1-m, k=1-n; (3) detecting total nitrogen (TN) and total phosphorus (TP) in the slurry samples from various facilities separately by using a standard test method to obtain a TN matrix YN and a TP matrix YP of each sample; (4) eliminating abnormal samples in steps (2) and (3), optimizing preprocessing methods, i.e. near-infrared diffuse reflectance spectra, and selecting optimal wavebands; (5) using optimized parameters to establish quantitative analysis models of TN and TP in the slurry movement routes of different large-scale dairy farms under all factors, namely, YN,P=X(Pi,Qk); (6) scanning near-infrared diffuse reflectance spectra of unknown slurry samples collected from the whole slurry treatment process of any of the large-scale dairy farms to obtain a near-infrared diffuse reflectance spectral matrix X'(Pi,Qk) of the unknown slurry samples, and substituting the spectral matrix into the quantitative analysis models obtained in step (5) to obtain predicted values Y'N and Y'P of the TN and TP in the unknown slurry samples from the large scale dairy farm.
2. The method for rapidly predicting N and P content in slurry samples according to claim 1, wherein in step (1), the slurry samples are collected from the slurry movement facilities in parallel for multiple times under different time, temperature, humidity and weather conditions; wherein in step (1), the collection tanks are facilities for collecting and storing wastewater of milking parlors; the slurry collection gutters are facilities for gathering slurry from barns; the slurry collection tanks are facilities where all slurry in the dairy farms converges; the separation tanks are facilities for temporarily storing the slurry after solid-liquid separation; the regulating tanks are facilities for homogenization and conditioning before the slurry enters a biogas plant; the sedimentation tanks are facilities for separating biogas slurry and biogas residues after anaerobic fermentation; the storage tanks and the lagoons are facilities for storing the slurry before returning the slurry to the field; wherein in step (1), the slurry samples are collected as follows: using a self-made 1 L stainless steel bucket or a 500 mL scoop to randomly collect slurry samples at 3 sites 10-20 cm vertically below a liquid level at sampling points of various facilities; mixing the slurry samples well in a mixing bucket by a scoop, taking about 400 mL of slurry into a slurry collection bottle; placing the slurry collection bottle in a sample incubator equipped with an ice pack, and immediately sending the slurry to a laboratory for testing.
3. The method for rapidly predicting N and P content in slurry samples according to claim 1, wherein in step (2), the near-infrared spectrometer is a Fourier-transform near-infrared spectrometer (InGaAs detector) of American PerkinElmer, which scans in the range of 4,000 12,000 cm-i; wherein in step (2), the near-infrared diffuse reflectance spectra are measured as follows: fully shaking the to-be-tested slurry samples in the slurry collection bottle; using a 3 mL disposable dropper to take 2-3 mL of slurry from the middle of the slurry collection bottle into a sample cuvette; placing the sample cuvette on a rotating sample stage provided with an integrating sphere, wherein the spectral scanning parameters comprise: resolution: 8 cm-i, scan interval: 2 cm-i, and number of scans: 64.
4. The method for rapidly predicting N and P content in slurry samples according to claim 1, wherein in step (3), the TN in the slurry samples is measured by a fully automatic Kjeldahl nitrogen analyzer, and the TP is measured by a visible light spectrophotometer.
5. The method for rapidly predicting N and P content in slurry samples according to claim 1, wherein in step (4), abnormal samples are eliminated by Monte Carlo cross validation (MCCV); samples after eliminating the abnormal samples is divided into a calibration set and a prediction set; samples in the calibration set are used to establish the quantitative analysis mathematical models, and samples in the prediction set are used to verify the stability and accuracy of the established mathematical models; the correction set and the prediction set are selected by using a Kennard-Stone (K-S) method; wherein in step (4), the spectral preprocessing methods comprise one or more of normalization, multivariate scatter correction (MSC), baseline correction, standard normal variate (SNV), Savitzky-Golay (SG) smoothing + normalization and SG smoothing + baseline correction; normalization is the optimal preprocessing method for the TN; SG smoothing + baseline correction is the optimal preprocessing method for the TP; wherein in step (4), a waveband of 4,400-8,800 cm-i is selected to establish the quantitative analysis mathematical model of the TN in the slurry, and a waveband of 4,000-8,000 cm-i is selected to establish the quantitative analysis mathematical model of the TP in the slurry.