基于近红外漫反射光谱的规模化奶牛场粪水氮磷定量分析及模型构建
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摘要
为建立规模化奶牛场粪水中氮磷含量现场快速检测方法,以实现准确预测的同时替代常规监测程序,选取23家天津市典型种养结合模式的规模化奶牛场,围绕粪水处理全过程环节依次开展样品采集、实验室常规化学检测、近红外漫反射光谱采集,并进行主成分分析和偏最小二乘分析,建立多种动态复合影响因素条件下的全局、全程快速检测定量分析模型。结果表明:主成分分析不仅反映出同一奶牛场粪水有机组分随处理环节的变化,而且也反映出不同奶牛场粪水样品的差异性,以及在粪水处理过程中各因素对后续模型的影响程度。建立的全过程环节定量分析模型对总氮含量预测结果与实际含量的线性拟合相关系数R为0.96,预测均方根误差RMSEP为187.80;对总磷含量预测结果与实际含量的线性拟合相关系数R为0.91,预测均方根误差RMSEP为3.59。建立的全局定量分析模型对总氮含量预测结果与实际含量的线性拟合相关系数R为0.96,预测均方根误差RMSEP为238.59;总磷含量预测结果与实际含量的线性拟合相关系数R为0.91,预测均方根误差RMSEP为6.56。研究表明,基于近红外漫反射光谱和偏最小二乘法对规模化奶牛场粪水处理全过程环节粪水样品中氮、磷含量进行定量分析是可行的;纵向模型比横向模型能提供更好的预测结果;近红外漫反射光谱技术可实时、快速、高效地对规模化奶牛场粪水处理全过程总氮和总磷进行跟踪和监控。
In order to establish an on-spot rapid testing method for determining the nitrogen and phosphorus contents in the slurry produced from large-scale dairy farms, for accurate prediction meanwhile replacing the regular monitoring procedure, we selected 23 typical large-scale dairy farms in the combination of growing and breeding for experimental analysis. Samples were collected throughout the entire course of slurry treatment, at both the vertical and horizontal levels, and then subjected to chemical and statistical analyses, i.e. near-infrared diffuse reflection spectrum(NIDRS), principal component analysis(PCA), and partial least squares(PLS)analysis. Under the conditions of multiple dynamic composite impacting factors, we established a rapid testing qualified analysis model for the global and entire process of slurry treatment. The results indicated that the organic components of slurry not only varied among the different stages of treatment on individual dairy farms, but also differed among the different farms, which were given by the PCA. Furthermore, we found that factors associated with the different stages of the slurry treatment procedure, which influenced the models, were reflected by the PCA. The linear fitting correlation coefficient(R)for the relationship between predicted and actual content of total nitrogen(TN)and total phosphorus(TP),during the establishment of the quantified analysis model and through the entire course and links, were 0.96 and 0.91, respectively. The root mean square errors of the predictions were 187.80 and 3.59, respectively. The R values for the relationship between the predicted and actual contents of TN and TP, during the establishment of the quantified analysis model and through the entire course and links, were 0.96 and 0.91, respectively, whereas the root mean square errors of the prediction were 238.59 and 6.56, respectively. In this study, we therefore demonstrated the feasibility of performing a quantitative analysis of the TN and TP content in slurry samples collected at the vertical and horizontal levels of the entire slurry treatment process based on NIDRS and PLS. Better prediction result was provided by the entire process model compared to the global model. Accordingly, TN and TP concentration over the entire course of the slurry treatment process on largescale dairy farms can be rapidly and efficiently traced and monitored in real time via the NIDRS technology.
In order to establish an on-spot rapid testing method for determining the nitrogen and phosphorus contents in the slurry produced from large-scale dairy farms, for accurate prediction meanwhile replacing the regular monitoring procedure, we selected 23 typical large-scale dairy farms in the combination of growing and breeding for experimental analysis. Samples were collected throughout the entire course of slurry treatment, at both the vertical and horizontal levels, and then subjected to chemical and statistical analyses, i.e. near-infrared diffuse reflection spectrum(NIDRS), principal component analysis(PCA), and partial least squares(PLS)analysis. Under the conditions of multiple dynamic composite impacting factors, we established a rapid testing qualified analysis model for the global and entire process of slurry treatment. The results indicated that the organic components of slurry not only varied among the different stages of treatment on individual dairy farms, but also differed among the different farms, which were given by the PCA. Furthermore, we found that factors associated with the different stages of the slurry treatment procedure, which influenced the models, were reflected by the PCA. The linear fitting correlation coefficient(R)for the relationship between predicted and actual content of total nitrogen(TN)and total phosphorus(TP),during the establishment of the quantified analysis model and through the entire course and links, were 0.96 and 0.91, respectively. The root mean square errors of the predictions were 187.80 and 3.59, respectively. The R values for the relationship between the predicted and actual contents of TN and TP, during the establishment of the quantified analysis model and through the entire course and links, were 0.96 and 0.91, respectively, whereas the root mean square errors of the prediction were 238.59 and 6.56, respectively. In this study, we therefore demonstrated the feasibility of performing a quantitative analysis of the TN and TP content in slurry samples collected at the vertical and horizontal levels of the entire slurry treatment process based on NIDRS and PLS. Better prediction result was provided by the entire process model compared to the global model. Accordingly, TN and TP concentration over the entire course of the slurry treatment process on largescale dairy farms can be rapidly and efficiently traced and monitored in real time via the NIDRS technology.
引文
[1] Bai Z H, Ma L, Jin S Q, et al. Nitrogen, phosphorus, and potassium flows through flows through the manure management chain in China[J].Environmental Science and Technology, 2016, 50:13409-13418.
[2]曹玉博,邢晓旭,柏兆海,等.农牧系统氨挥发减排技术研究进展[J].中国农业科学, 2018, 51(3):566-580.CAO Yu-bo, XING Xiao-xu, BAI Zhao-hai, et al. Review on ammonia emission mitigation techniques of crop-livestock production system[J].Scientia Agricultura Sinica, 2018, 51(3):566-580.
[3]王樱洁,周博,孙海霞,等.近红外光谱分析技术在畜禽粪样成分检测中的应用[J].中国畜牧杂志, 2017, 53(12):13-16.WANG Ying-jie, ZHOU Bo, SUN Hai-xia, et al. Application of near infrared spectroscopy in the detection of fecal components in livestock and poultry[J]. Chinese Journal of Animal Science, 2017, 53(12):13-16.
[4]孔源,韩鲁佳,贾贵儒,等.近红外技术快速测定肉鸡粪便主要肥料成分含量的研究[J].农业工程学报, 2004, 20(6):251-254.KONG Yuan, HAN Lu-jia, JIA Gui-ru, et al. Rapid near infrared prediction of broiler manure nutrient contents[J]. Transactions of the Chinese Society of Agricultural Engineering, 2004, 20(6):251-254.
[5]黄光群,王晓燕,韩鲁佳.基于支持向量机的有机肥总养分含量NIRS分析[J].农业机械学报, 2010, 41(2):93-98.HUANG Guang-qun, WANG Xiao-yan, HAN Lu-jia. Near-infrared reflectance spectroscopy for total nutrient analysis in organic fertilizer using linear support vector machine[J]. Transactions of the Chinese Society for Agricultural Machinery, 2010, 41(2):93-98.
[6] Reeves J B. Near-infrared diffuse reflectance spectroscopy for the analysis of poultry manures[J]. Journal of Agricultural and Food Chemistry,2001, 49(5):2193-2197.
[7] Jancewicz L J, Penner G B, Swift M L, et al. Characterization of the variation in the daily excretion of faecal constituents and digestibility predictions in beef cattle fed feedlot diets using near-infrared spectroscopy[J]. Canadian Journal of Animal Science, 2016, 96(4):532-549.
[8] Ottavian M, Franceschin E, Signorin E, et al. Application of near infrared reflectance spectroscopy(NIRS)on faecal samples from lactating dairy cows to assess two levels of concentrate supple mentation during summer grazing in alpine pastures[J]. Animal Feed Science and Technology, 2015, 202:100-105.
[9] Galasso H L, Callier M D, Bastianelli D, et al. The potential of near infrared spectroscopy(NIRS)to measure the chemical composition of aquaculture solid waste[J]. Aquaculture, 2017, 476:134-140.
[10] Mowrer J, Kissel D, Cabrera M, et al. Near-infrared calibrations for organic, inorganic, and mineralized nitrogen from poultry litter[J]. Soil Science Society of America Journal, 2014, 78(5):1775-1785.
[11] Tamburini E, Castaldelli G, Ferrari G, et al. Onsite and online FTNIR spectroscopy for the estimation of total nitrogen and moisture content in poultry manure[J]. Environmental Technology, 2015, 36(18):2285-2294.
[12]王晓燕,黄光群,韩鲁佳.鸡粪工厂化堆肥主要营养成分含量近红外光谱分析研究[J].光谱学与光谱分析, 2010, 30(3):677-680.WANG Xiao-yan, HUANG Guang-qun, HAN Lu-jia. Rapid evaluation of primary nutrients during plant-field chicken manure composting using near-infrared reflectance spectroscopy[J]. Spectroscopy and Spectral Analysis, 2010, 30(3):677-680.
[13] Chen L, Xing L, Han L. Review of the application of near-infrared spectroscopy technology to determine the chemical composition of animal manure[J]. Journal of Environmental Quality, 2013, 42(4):1015-1028.
[14]严旭,杜周和,白史且,等.动物粪便近红外光谱的应用潜力[J].光谱学与光谱分析, 2015, 35(12):3382-3387.YAN Xu, DU Zhou-he, BAI Shi-qie, et al. Potential applicability of fecal NIRs:A review[J]. Spectroscopy and Spectral Analysis, 2015, 35(12):3382-3387.
[15]黄光群,韩鲁佳,樊霞,等.近红外光谱分析技术在畜禽粪便堆肥研究中的应用[J].中国农业大学学报, 2006, 111(3):93-97.HUANG Guang-qun, HAN Lu-jia, FAN Xia, et al. Application survey of analytical technique of near infrared spectroscopy on studies of livestock manure compost[J]. Journal of China Agricultural University,2006, 111(3):93-97.
[16]渠清博,杨鹏,翟中葳,等.规模化畜禽养殖粪便主要污染物产生量预测方法研究进展[J].农业资源与环境学报, 2016, 33(5):397-406.QU Qing-bo, YANG Peng, ZHAI Zhong-wei, et al. Prediction methods of major pollutants production in manure from large-scale livestock and poultry farms:A review[J]. Journal of Agricultural Resources and Environment, 2016, 33(5):397-406.
[17] Jancewicz L J, Swift M L, Penner G B, et al. Development of near-infrared spectroscopy calibrations to estimate fecal composition and nutrient digestibility in beef cattle[J]. Canadian Journal of Animal Science, 2016, 97(1):51-64.
[18] Schiborra A, Bulang M, Berk A, et al. Using faecal near-infrared spectroscopy(FNIRS)to estimate nutrient digestibility and chemical composition of diets and faeces of growing pigs[J]. Animal Feed Science and Technology, 2015, 210:234-242.
[19] Nirea K G, Pérez de Nanclares M, Skugor A, et al. Assessment of fecal near-infrared spectroscopy to predict feces chemical composition and apparent total-tract digestibility of nutrients in pigs[J]. Journal of Animal Science, 2018, 96(7):2826-2837.
[20]天津市畜牧兽医局.规模化奶牛场环境监测技术规程DB 12/T655—2016[S].天津：天津市市场监督管理委会, 2016.Tianjin Animal Husbandry and Veterinary Bureau. Technical regulation for environmental monitoring of large-scale dairy farms DB 12/T655—2016[S]. Tianjin:Tianjin Administration for Market Regulation,2016.
[21]国家质量监督检验检疫总局,国家标准化管理委员会.畜禽养殖污水采样技术规范GB/T 27522—2011[S].北京：中国标准出版社,2011.State Administration for Quality Supervision and Inspection and Quarantine, National Standardization Administration of China. Technical specifications or waste water sampling of livestock and poultry farm GB/T 27522—2011[S]. Beijing:China Standards Press, 2011.
[22]国家环境保护局标准处.水质凯氏氮的测定GB 11891—1989[S].北京：中国质检出版社, 1989.Standards Division of Environmental Protection Agency. Water qualitydetermination of Kjeldahl nitrogen GB 11891—1989[S]. Beijing:China Quality Inspection Press, 1989.
[23]国家环境保护局标准处.水质总磷的测定钼酸铵分光光度法GB 11893—1989[S].北京：中国环境出版有限责任公司, 1989.Standards Division of Environmental Protection Agency. Water quality—Determination of total phosphorus—Ammonium molybdate spectrophotometric method GB 11893—1989[S]. Beijing:China Environmental Publishing Co. Ltd, 1989.
[24] Jacobi H F, Moschner C R, Hartung E. Use of near infrared spectroscopy in online-monitoring of feeding substrate quality in anaerobic digestion[J]. Bioresource Technology, 2011, 102(7):4688-4696.
[25] Krapf L C, Nast D, Gronauer A, et al. Transfer of a near infrared spectroscopy laboratory application to an online process analyser for in situ monitoring of anaerobic digestion[J]. Bioresource Technology, 2013,129:39-50.
[26] Villamuelas M, Serrano E, Espunyes J, et al. Predicting herbivore faecal nitrogen using a multispecies near-infrared reflectance spectroscopy calibration[J]. PLoS One, 2017, 12(4):e0176635.
[2]曹玉博,邢晓旭,柏兆海,等.农牧系统氨挥发减排技术研究进展[J].中国农业科学, 2018, 51(3):566-580.CAO Yu-bo, XING Xiao-xu, BAI Zhao-hai, et al. Review on ammonia emission mitigation techniques of crop-livestock production system[J].Scientia Agricultura Sinica, 2018, 51(3):566-580.
[3]王樱洁,周博,孙海霞,等.近红外光谱分析技术在畜禽粪样成分检测中的应用[J].中国畜牧杂志, 2017, 53(12):13-16.WANG Ying-jie, ZHOU Bo, SUN Hai-xia, et al. Application of near infrared spectroscopy in the detection of fecal components in livestock and poultry[J]. Chinese Journal of Animal Science, 2017, 53(12):13-16.
[4]孔源,韩鲁佳,贾贵儒,等.近红外技术快速测定肉鸡粪便主要肥料成分含量的研究[J].农业工程学报, 2004, 20(6):251-254.KONG Yuan, HAN Lu-jia, JIA Gui-ru, et al. Rapid near infrared prediction of broiler manure nutrient contents[J]. Transactions of the Chinese Society of Agricultural Engineering, 2004, 20(6):251-254.
[5]黄光群,王晓燕,韩鲁佳.基于支持向量机的有机肥总养分含量NIRS分析[J].农业机械学报, 2010, 41(2):93-98.HUANG Guang-qun, WANG Xiao-yan, HAN Lu-jia. Near-infrared reflectance spectroscopy for total nutrient analysis in organic fertilizer using linear support vector machine[J]. Transactions of the Chinese Society for Agricultural Machinery, 2010, 41(2):93-98.
[6] Reeves J B. Near-infrared diffuse reflectance spectroscopy for the analysis of poultry manures[J]. Journal of Agricultural and Food Chemistry,2001, 49(5):2193-2197.
[7] Jancewicz L J, Penner G B, Swift M L, et al. Characterization of the variation in the daily excretion of faecal constituents and digestibility predictions in beef cattle fed feedlot diets using near-infrared spectroscopy[J]. Canadian Journal of Animal Science, 2016, 96(4):532-549.
[8] Ottavian M, Franceschin E, Signorin E, et al. Application of near infrared reflectance spectroscopy(NIRS)on faecal samples from lactating dairy cows to assess two levels of concentrate supple mentation during summer grazing in alpine pastures[J]. Animal Feed Science and Technology, 2015, 202:100-105.
[9] Galasso H L, Callier M D, Bastianelli D, et al. The potential of near infrared spectroscopy(NIRS)to measure the chemical composition of aquaculture solid waste[J]. Aquaculture, 2017, 476:134-140.
[10] Mowrer J, Kissel D, Cabrera M, et al. Near-infrared calibrations for organic, inorganic, and mineralized nitrogen from poultry litter[J]. Soil Science Society of America Journal, 2014, 78(5):1775-1785.
[11] Tamburini E, Castaldelli G, Ferrari G, et al. Onsite and online FTNIR spectroscopy for the estimation of total nitrogen and moisture content in poultry manure[J]. Environmental Technology, 2015, 36(18):2285-2294.
[12]王晓燕,黄光群,韩鲁佳.鸡粪工厂化堆肥主要营养成分含量近红外光谱分析研究[J].光谱学与光谱分析, 2010, 30(3):677-680.WANG Xiao-yan, HUANG Guang-qun, HAN Lu-jia. Rapid evaluation of primary nutrients during plant-field chicken manure composting using near-infrared reflectance spectroscopy[J]. Spectroscopy and Spectral Analysis, 2010, 30(3):677-680.
[13] Chen L, Xing L, Han L. Review of the application of near-infrared spectroscopy technology to determine the chemical composition of animal manure[J]. Journal of Environmental Quality, 2013, 42(4):1015-1028.
[14]严旭,杜周和,白史且,等.动物粪便近红外光谱的应用潜力[J].光谱学与光谱分析, 2015, 35(12):3382-3387.YAN Xu, DU Zhou-he, BAI Shi-qie, et al. Potential applicability of fecal NIRs:A review[J]. Spectroscopy and Spectral Analysis, 2015, 35(12):3382-3387.
[15]黄光群,韩鲁佳,樊霞,等.近红外光谱分析技术在畜禽粪便堆肥研究中的应用[J].中国农业大学学报, 2006, 111(3):93-97.HUANG Guang-qun, HAN Lu-jia, FAN Xia, et al. Application survey of analytical technique of near infrared spectroscopy on studies of livestock manure compost[J]. Journal of China Agricultural University,2006, 111(3):93-97.
[16]渠清博,杨鹏,翟中葳,等.规模化畜禽养殖粪便主要污染物产生量预测方法研究进展[J].农业资源与环境学报, 2016, 33(5):397-406.QU Qing-bo, YANG Peng, ZHAI Zhong-wei, et al. Prediction methods of major pollutants production in manure from large-scale livestock and poultry farms:A review[J]. Journal of Agricultural Resources and Environment, 2016, 33(5):397-406.
[17] Jancewicz L J, Swift M L, Penner G B, et al. Development of near-infrared spectroscopy calibrations to estimate fecal composition and nutrient digestibility in beef cattle[J]. Canadian Journal of Animal Science, 2016, 97(1):51-64.
[18] Schiborra A, Bulang M, Berk A, et al. Using faecal near-infrared spectroscopy(FNIRS)to estimate nutrient digestibility and chemical composition of diets and faeces of growing pigs[J]. Animal Feed Science and Technology, 2015, 210:234-242.
[19] Nirea K G, Pérez de Nanclares M, Skugor A, et al. Assessment of fecal near-infrared spectroscopy to predict feces chemical composition and apparent total-tract digestibility of nutrients in pigs[J]. Journal of Animal Science, 2018, 96(7):2826-2837.
[20]天津市畜牧兽医局.规模化奶牛场环境监测技术规程DB 12/T655—2016[S].天津：天津市市场监督管理委会, 2016.Tianjin Animal Husbandry and Veterinary Bureau. Technical regulation for environmental monitoring of large-scale dairy farms DB 12/T655—2016[S]. Tianjin:Tianjin Administration for Market Regulation,2016.
[21]国家质量监督检验检疫总局,国家标准化管理委员会.畜禽养殖污水采样技术规范GB/T 27522—2011[S].北京：中国标准出版社,2011.State Administration for Quality Supervision and Inspection and Quarantine, National Standardization Administration of China. Technical specifications or waste water sampling of livestock and poultry farm GB/T 27522—2011[S]. Beijing:China Standards Press, 2011.
[22]国家环境保护局标准处.水质凯氏氮的测定GB 11891—1989[S].北京：中国质检出版社, 1989.Standards Division of Environmental Protection Agency. Water qualitydetermination of Kjeldahl nitrogen GB 11891—1989[S]. Beijing:China Quality Inspection Press, 1989.
[23]国家环境保护局标准处.水质总磷的测定钼酸铵分光光度法GB 11893—1989[S].北京：中国环境出版有限责任公司, 1989.Standards Division of Environmental Protection Agency. Water quality—Determination of total phosphorus—Ammonium molybdate spectrophotometric method GB 11893—1989[S]. Beijing:China Environmental Publishing Co. Ltd, 1989.
[24] Jacobi H F, Moschner C R, Hartung E. Use of near infrared spectroscopy in online-monitoring of feeding substrate quality in anaerobic digestion[J]. Bioresource Technology, 2011, 102(7):4688-4696.
[25] Krapf L C, Nast D, Gronauer A, et al. Transfer of a near infrared spectroscopy laboratory application to an online process analyser for in situ monitoring of anaerobic digestion[J]. Bioresource Technology, 2013,129:39-50.
[26] Villamuelas M, Serrano E, Espunyes J, et al. Predicting herbivore faecal nitrogen using a multispecies near-infrared reflectance spectroscopy calibration[J]. PLoS One, 2017, 12(4):e0176635.