Review of the application of near-infrared spectroscopy technology to determine the chemical composition of animal manure

Application of fourier transform infrared photoacoustic spectroscopy for quantification of nutrient contents and their plant availability in manure and digestate

In this study, we assess the feasibility of using Fourier Transform Infrared Photoacoustic Spectroscopy (FTIR-PAS) to predict macro- and micro-nutrients in a diverse set of manures and digestates. Furthermore, the prediction capabilities of FTIR-PAS were assessed using a novel error tolerance-based interval method in view of the accuracy required for application in agricultural practices. Partial Least-Squares Regression (PLSR) was used to correlate the FTIR-PAS spectra with nutrient contents. The prediction results were then assessed with conventional assessment methods (root mean square error (RMSE), coefficient of determination R2, and the ratio of prediction to deviation (RPD)). The results show the potential of FTIR-PAS to be used as a rapid analysis technique, with promising prediction results (R2 > 0.91 and RPD >2.5) for all elements except for bicarbonate-extractable P, K, and NH4+-N (0.8 < R2 < 0.9 and 2 < RPD <2.5). The results for nitrogen and phosphorus were further evaluated using the proposed error tolerance-based interval method. The probability of prediction for nitrogen within the allowed limit is calculated to be 94.6 % and for phosphorus 83.8 %. The proposed error tolerance-based interval method provides a better measure to decide if the FTIR-PAS in its current state could be used to meet the required accuracy in agriculture for the quantification of nutrient content in manure and digestate.

Investigating fungal community characteristics in co-composted cotton stalk and various livestock manure products

Agricultural wastes, comprising cotton straw and livestock manure, can be effectively managed through aerobic co-composting. Nevertheless, the quality and microbial characteristics of co-composting products from different sources remain unclear. Therefore, this study utilized livestock manure from various sources in Xinjiang, China, including herbivorous sheep manure (G), omnivorous pigeon manure (Y), and pigeon-sheep mixture (GY) alongside cotton stalks, for a 40-day co-composting process. We monitored physicochemical changes, assessed compost characteristics, and investigated fungal community. The results indicate that all three composts met established composting criteria, with compost G exhibiting the fastest microbial growth and achieving the highest quality. Ascomycota emerged as the predominant taxon in three compost products. Remarkably, at the genus level, the biomarker species for G, Y, and GY are Petromyces and Cordyceps, Neurospora, and Neosartorya, respectively. Microorganisms play a pivotal role in organic matter degradation, impacting nutrient composition, demonstrating significant potential for the decomposition and transformation of compost components. Redundancy analysis indicates that potassium, total organic carbon, and C:N are key factors influencing fungal communities. This study elucidates organic matter degradation in co-composting straw and livestock manure diverse sources, optimizing treatment for efficient agricultural waste utilization and sustainable practices.

Rapid and accurate determination methods based on data fusion of laser-induced breakdown spectra and near-infrared spectra for main elemental contents in compound fertilizers

Compound fertilizer occupies a dominant position in the structure of fertilizer products in China. The contents of nitrogen, phosphorus and potassium are the key indicators affecting the fertilization efficiency and the price of compound fertilizers. Laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIRS) are two rapid analytical techniques suitable for online monitoring of the above components in compound fertilizer. However, accurate LIBS analysis needs to overcome matrix effects and interference from environmental elements, and NIRS also has the limitation of not being able to directly detect inorganic components in compound fertilizers. The combination of LIBS and NIRS techniques, namely LIBS-NIRS data fusion, has the potential to reduce interferences in the detection of single spectroscopic techniques and further improve the analysis accuracy. This study compared the LIBS-NIRS data fusion methods under different optimization conditions, and found that CARS-OPF (competitive adaptive reweighted sampling combined with outer product fusion) and CARS-EWF (competitive adaptive reweighted sampling combined with equal weight fusion) are two effective intermediate data fusion methods which can achieve better quantitative analysis results than single spectroscopic methods. The root mean square errors of prediction (RMSEP) for nitrogen, phosphorus, and potassium contents in compound fertilizers by using CARS-OPF are 0.901, 0.693, and 1.52, respectively, and the RMSEP for those indicators by using CARS-EWF are 0.934, 0.719, and 1.60, respectively. In these two methods, the LIBS and NIRS characteristic variables of compound fertilizers are firstly screened by CARS algorithm, and then intermediate data fusion was carried out by using equal weight fusion or outer product fusion. Redundant variables in the original data can be well removed in the data fusion process to ensure the accuracy of the analysis. Therefore, the combined methods of LIBS-NIRS based on CARS-OPF and CARS-EWF could be well applied to the rapid and accurate detection of main elemental contents in compound fertilizers.

Vibrational Spectroscopy as a Tool for Bioanalytical and Biomonitoring Studies

The review briefly describes various types of infrared (IR) and Raman spectroscopy methods. At the beginning of the review, the basic concepts of biological methods of environmental monitoring, namely bioanalytical and biomonitoring methods, are briefly considered. The main part of the review describes the basic principles and concepts of vibration spectroscopy and microspectrophotometry, in particular IR spectroscopy, mid- and near-IR spectroscopy, IR microspectroscopy, Raman spectroscopy, resonance Raman spectroscopy, Surface-enhanced Raman spectroscopy, and Raman microscopy. Examples of the use of various methods of vibration spectroscopy for the study of biological samples, especially in the context of environmental monitoring, are given. Based on the described results, the authors conclude that the near-IR spectroscopy-based methods are the most convenient for environmental studies, and the relevance of the use of IR and Raman spectroscopy in environmental monitoring will increase with time.

Characteristics of bacterial and fungal communities and their impact during cow manure and agroforestry biowaste co-composting

Microbial communities and environmental conditions are both of great importance for efficient utilization of agroforestry resources. Nevertheless, knowledge about the role of soluble nutrients and enzymatic properties, and their inner links with microbial communities remain limited. This is especially the case for the co-composting of agricultural and forestry biowaste. Here, we investigate the succession of key microbes during co-composting (sawdust + cow manure, SA; straw + cow manure, ST), employing amplicon sequencing, enzyme assays, and physicochemical analyses. N-fixing bacteria (Pseudomonas) and C-degrading fungi (Acaulium) have been identified as dominant taxa during such co-composting. Although eight antibiotic resistance genes were found to persist during composting, pathogenic microbes declined with composting time. NO₃^--N content was screened as a determinant structuring the bacterial and fungal communities, with importance also shown for C-degrading enzymes such as cellulose, laccase, and peroxidase activity. These results identify the key microbial taxa and their main interactive environmental factors, which are potentially valuable for the development of a mixed microbial inoculant to accelerate the maturation of agroforestry biowastes composting.

Evaluating the Feasibility of a Low-Field Nuclear Magnetic Resonance (NMR) Sensor for Manure Nutrient Prediction

Livestock manure is typically applied to fertilize crops, however the accurate determination of manure nutrient composition through a reliable method is important to optimize manure application rates that maximize crop yields and prevent environmental contamination. Existing laboratory methods can be time consuming, expensive, and generally the results are not provided prior to manure application. In this study, the evaluation of a low-field nuclear magnetic resonance (NMR) sensor designated for manure nutrient prediction was assessed. Twenty dairy manure samples were analyzed for total solid (TS), total nitrogen (TN), ammoniacal nitrogen (NH4-N), and total phosphorus (TP) in a certified laboratory and in parallel using the NMR analyzer. The linear regression of NMR prediction versus lab measurements for TS had an R2 value of 0.86 for samples with TS < 8%, and values of 0.94 and 0.98 for TN and NH4-N, respectively, indicating good correlations between NMR prediction and lab measurements. The TP prediction of NMR for all samples agreed with the lab analysis with R2 greater than 0.87. The intra- and inter-sample variations of TP measured by NMR were significantly larger than other parameters suggesting less robustness in TP prediction. The results of this study indicate low-field NMR is a rapid method that has a potential to be utilized as an alternative to laboratory analysis of manure nutrients, however, further investigation is needed before wide application for on farm analysis.

Evaluation of Near-Infrared Reflectance and Transflectance Sensing System for Predicting Manure Nutrients

Livestock manure is widely applied onto agriculture soil to fertilize crops and increase soil fertility. However, it is difficult to provide real-time manure nutrient data based on traditional lab analyses during application. Manure sensing using near-infrared (NIR) spectroscopy is an innovative, rapid, and cost-effective technique for inline analysis of animal manure. This study investigated a NIR sensing system with reflectance and transflectance modes to predict N speciation in dairy cow manure using a spiking method. In this study, 20 dairy cow manure samples were collected and spiked to achieve four levels of ammoniacal nitrogen (NH4-N) and organic nitrogen (Org-N) concentrations that resulted in 100 samples in each spiking group. All samples were scanned and analyzed using a NIR system with reflectance and transflectance sensor configurations. NIR calibration models were developed using partial least square regression analysis for NH4-N, Org-N, total solid (TS), ash, and particle size (PS). Coefficient of determination (R2) and root mean square error (RMSE) were selected to evaluate the models. A transflectance probe with a 1 mm path length had the best performance for analyzing manure constituents among three path lengths. Reflectance mode improved the calibration accuracy for NH4-N and Org-N, whereas transflectance mode improved the model predictability for TS, ash, and PS. Reflectance provided good prediction for NH4-N (R2 = 0.83; RMSE = 0.65 mg mL−1) and approximate predictions for Org-N (R2 = 0.66; RMSE = 1.18 mg mL−1). Transflectance was excellent for TS predictions (R2 = 0.97), and provided good quantitative predictions for ash and approximate predictions for PS. The correlations between the accuracy of NH4-N and Org-N calibration models and other manure parameters were not observed indicating the predictions of N contents were not affected by TS, ash, and PS.

Meat Processing Waste as a Source of Nutrients and Its Effect on the Physicochemical Properties of Soil

The aim of this study was to determine the effect of meat processing waste applied in the form of meat and bone meal (MBM) as a source of nutrients on the physicochemical properties of soil. A short–term small–area field experiment using MBM in maize monoculture was conducted in 2014–2017. Each year, MBM was applied presowing at 1.0, 2.0, and 3.0 t ha−1 to maize grown in experimental plots. The application of MBM decreased the bulk density and specific density and increased the pH of Haplic Luvisol Loamic (HLL) soil. The mineral nitrogen (N) content was highest when MBM was applied at 3.0 t ha−1 in HLL soil and 2.0 t ha−1 in Haplic Luvisol Arenic (HLA) soil. The minor differences in the mineral N content of soil between the treatment without fertilization and MBM treatments could be attributed to high N utilization by maize plants. The phosphorus (P) content of soil increased with a rise in the MBM dose. The P content of the arable layer was lower in HLA soil than in HLL soil, which resulted from higher P uptake by maize grain. The highest maize grain yield was achieved in the last year of the study, in response to the highest MBM dose and due to the residual effect of MBM.

Fast and Accurate Quantification of Nitrogen and Phosphorus Constituents in Animal Slurries Using NMR Sensor Technology

The optimal processing of animal slurry with a minimal environmental impact either as an organic fertilizer or as an energy source for biogas production fundamentally requires accurate, fast, cost-effective, and mobile analytical techniques for the measurement of nitrogen and phosphorus in large volumes of liquid animal slurry. Based on more than 300 different slurries from different species and origins, we provide here an extensive analysis of low-field NMR and standard laboratory measurements for animal slurry analysis. It is found that low-field NMR provides higher precision than wet chemistry laboratory measurements for ammonium nitrogen and total nitrogen, while it provides slightly lower precision for total phosphorus measurements. Low-field NMR may, through a square-root dependency between time and precision, be adapted for analysis at farms, in slurry tankers/transporters, in biogas digesters, or in laboratories.

Near-infrared spectroscopy (NIRS) as a tool to monitor exhaust air from poultry operations

Intensive poultry operation systems emit a considerable volume of inorganic and organic matter in the surrounding environment. Monitoring cleaning properties of exhaust air cleaning systems and to detect small but significant changes in emission characteristics during a fattening cycle is important for both emission and fattening process control. In the present study, we evaluated the potential of near-infrared spectroscopy (NIRS) combined with chemometric techniques as a monitoring tool of exhaust air from poultry operation systems. To generate a high-quality data set for evaluation, the exhaust air of two poultry houses was sampled by applying state-of-the-art filter sampling protocols. The two stables were identical except for one crucial difference, the presence or absence of an exhaust air cleaning system. In total, twenty-one exhaust air samples were collected at the two sites to monitor spectral differences caused by the cleaning device, and to follow changes in exhaust air characteristics during a fattening period. The total dust load was analyzed by gravimetric determination and included as a response variable in multivariate data analysis. The filter samples were directly measured with NIR spectroscopy. Principal component analysis (PCA), linear discriminant analysis (LDA), and factor analysis (FA) were effective in classifying the NIR exhaust air spectra according to fattening day and origin. The results indicate that the dust load and the composition of exhaust air (inorganic or organic matter) substantially influence the NIR spectral patterns. In conclusion, NIR spectroscopy as a tool is a promising and very rapid way to detect differences between exhaust air samples based on still not clearly defined circumstances triggered during a fattening period and the availability of an exhaust air cleaning system.

Onsite and online FT-NIR spectroscopy for the estimation of total nitrogen and moisture content in poultry manure

The nitrogen and moisture of manure are highly variable parameters and depend on animal type, husbandry techniques, environmental conditions and storage time. The precision in manure dose estimation for crops fertilization depends on the total nitrogen and moisture content just before its incorporation in the field. The aim of the study is to develop a Fourier Transform Near Infrared (FT-NIR) spectroscopy method to determine the total Kjeldhal nitrogen (TKN%) and moisture (M%) of different types of poultry manure prior to land application. Samples covering a wide range of poultry types and different husbandry conditions were obtained from farms of North-Eastern Italy in order to develop the method. The method was calibrated (R(2) = 0.94 for TKN%, R(2) = 0.99 for M%) and validated (R(2) = 0.82 for TKN%, R(2) = 0.95 for M%) in the laboratory. An external validation was also performed in situ with independent samples, of similar origin to the previous data set, which were collected just before application in the field. Spectra acquisitions for these samples were carried out using the same instrumentation which was placed in a special vehicle for monitoring campaigns. The results showed satisfactory prediction accuracy (R(2) = 0.82 for TKN%, R(2) = 0.93 for M%). Finally, an additional analysis was performed to discriminate the different types of poultry effluents. The TKN and M measurements in the disposal areas indicated that current agronomic practices lead to more than double poultry manure oversupply. The proposed FT-NIR methodology aims to improve the current fertilization management and environmental protection by providing fast and precise estimations of poultry manure doses prior to land application.