Faecal near-infrared reflectance spectroscopy (NIRS) compared with other techniques for estimating the in vivo digestibility and dry matter intake of lactating grazing dairy cows

Use of near-infrared reflectance spectroscopy on feces to estimate digestibility and dry matter intake of dietary nutritional characteristics under grazing conditions in Colombian creole steers

Digestibility and intake are parameters difficult and expensive to estimate under grazing conditions; therefore, the aim of this study was to develop near-infrared reflectance spectroscopy (NIRS) calibrations applied to feces (F-NIRS) and evaluate their accuracy to predict dry matter digestibility (DMD) and dry matter intake (DMI) of Colombian creole cattle. Five digestibility trials using creole steers were conducted; indigestible neutral detergent fiber (iNDF) was used as internal marker and Cr₂O₃ and TiO₂ as external markers. A total of 249 forage and 396 fecal samples from individual animals were collected, dried, and grinded for conventional chemical analysis. For spectral analysis, fecal samples were pooled across collection periods (77 samples). Chemometric analysis was performed using WinISI V4.10 software applying the modified partial least squares method. Cross-validation was performed to avoid overfitting the models. The goodness-of-fit statistics considered were the coefficient of determination in cross-validation and prediction sets (R²cv and r², respectively) and the ratio performance deviation (RPD). Fecal NIRS calibrations developed for forage and supplement DMD showed a satisfactory fit (R²cv =0.87 and RPD=2.77 and R²cv=0.92 and RPD=3.50, respectively). The accuracy of fecal output equations using chromium (Cr) and titanium (Ti) was similar in terms of R²cv (0.92) and RPD (3.63 vs. 3.57). Total DMI equations using Ti performed better compared to Cr (R²cv = 0.82 vs. 0.78; RPD=2.41 vs. 2.17, respectively). The F-NIRS models were validated using a completely independent set of fecal samples showing a moderate fit (r²>0.8 and RPD>2.0). This study showed that F-NIRS is a feasible tool to predict DMD and DMI of creole steers under grazing conditions. However, previous to socialization, this requires an improvement in accuracy of the calibrated equations related to grazing animals in different production contexts.

Estimation of diet organic matter digestibility in grazing dairy cows

Precise and continual information on the energy supply from pasture is mandatory for managing grazing ruminants. Therefore, estimating the organic matter (OM) digestibility from faecal crude protein concentration using the regression equation, OM digestibility [%] = 79.76-107.7 · e^{(-0.01515 · faecal crude protein [g/kg OM])}, is known to be a suitable tool. However, essential information regarding faeces sampling times and the required number of samples are not yet available. We therefore analysed the OM digestibility data of an experiment with dairy cows grazing two pasture types and receiving two concentrate types over 6 d in three independent runs. Both pasture type and grazing day affected the OM digestibility estimates, whereas concentrate type and intake did not, indicating that this method reliably detects small changes in OM digestibility of pasture without being interfered by moderate concentrate supplementation, selective grazing behaviour or differences in feed intake. Likewise, as sampling time did not influence OM digestibility, faeces sampling once daily can be recommended to be sufficient for an accurate estimation of OM digestibility. The variance within pasture type and grazing day amounted for 1.1 percentage units of estimated OM digestibility, which enabled to define the minimum sample number required to detect given differences in OM digestibility with adequate statistical certainty. In conclusion, estimating OM digestibility from faecal crude protein concentration is an applicable and sensitive method to reliably detect differences in the quality of ingested pasture using a limited number of animals. Therefore, instructions for faeces sample collection  were provided.

Rapid quantitative analysis of nitrogen and phosphorus through the whole chain of manure management in dairy farms by fusion model

Field monitoring technology plays a vital role for returning the animal manure back to the cropland with high-efficiency and accuracy, particular in the complex rotation system of manure management in Chinese intensive farms. The comprehensive quantitative analysis models were proposed and built for determining the content of the nitrogen (N) and the phosphorus (P) through the whole chain of manure management in different dairy farms under multiple conditions. 249 manure samples were collected from 31 intensive dairy farms in Tianjin both in summer and autumn. The effect of seasons on the distribution characteristics of the N and P in the manure was analyzed. Near infrared spectra were collected and principal component analysis (PCA) was performed. Partial least squares (PLS) was used to establish the intra-season and inter-season models. It was found that the contents of the N and P in the manure varied with seasons. The prediction performance of intra-season models was better than that of inter-season models. Fusion model of two seasons were also established. The coefficient of determination of external validation (R²_pred) for the N and P were 0.972 and 0.901, respectively. The residual predictive deviations (RPD) were 5.98 and 3.18, respectively. The results showed that the fusion model could enhance the universality and stability for predicting the N and P contents through the whole chain of manure management under the influence of various factors. The study not only supports for the development of on-spot detecting instrument but also guides for the rational recycling of manure in practice as well.

Proxy Measures and Novel Strategies for Estimating Nitrogen Utilisation Efficiency in Dairy Cattle

Simple Summary

Dairy cow diets contain nitrogen, mostly in the form of protein. However, dietary nitrogen is used with a low efficiency for milk production, and much of the unused nitrogen is converted to urea and excreted in urine and faeces (manure). Nitrogen within manure can then be lost to the environment, and this is a particular issue when dairy cows are offered diets containing excess dietary protein. As a result, there is increasing pressure on the dairy sector to improve the efficiency with which dairy cows utilise dietary nitrogen. While nitrogen utilisation efficiency can be measured accurately on research farms, this is more difficult on commercial farms. For that reason, there is much interest in developing low-cost and easy-to-use proximate measures that can provide accurate estimates of nitrogen utilisation. This review examines a number of proximate analyses that are already used as indicators of nitrogen use efficiency in dairy cows (e.g., blood urea and milk urea), and a number of more novel measures that may have potential for use in the future (including analysis of milk, blood, urine, breath, and predictions of intake). These ‘proxy’ measurements can be used to improve feeding management and might be used to monitor adherence to legislation.

Abstract

The efficiency with which dairy cows convert dietary nitrogen (N) to milk N is generally low (typically 25%). As a result, much of the N consumed is excreted in manure, from which N can be lost to the environment. Therefore there is increasing pressure to reduce N excretion and improve N use efficiency (NUE) on dairy farms. However, assessing N excretion and NUE on farms is difficult, thus the need to develop proximate measures that can provide accurate estimates of nitrogen utilisation. This review examines a number of these proximate measures. While a strong relationship exists between blood urea N and urinary N excretion, blood sampling is an invasive technique unsuitable for regular herd monitoring. Milk urea N (MUN) can be measured non-invasively, and while strong relationships exist between dietary crude protein and MUN, and MUN and urinary N excretion, the technique has limitations. Direct prediction of NUE using mid-infrared analysis of milk has real potential, while techniques such as near-infrared spectroscopy analysis of faeces and manure have received little attention. Similarly, techniques such as nitrogen isotope analysis, nuclear magnetic resonance spectroscopy of urine, and breath ammonia analysis may all offer potential in the future, but much research is still required.

Influence of environmental factors on the detection of blood in sheep faeces using visible-near-infrared spectroscopy as a measure of Haemonchus contortus infection

BACKGROUND

Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible-near-infrared (Vis-NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis-NIR spectroscopy in quantifying blood in faeces.

METHODS

Visible-NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387-609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores.

RESULTS

Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated 'healthy' SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57-94%, specificity 44-79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood.

CONCLUSION

This study demonstrates the potential of Vis-NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

Indirect measures of methane emissions of Sahelian zebu cattle in West Africa, role of environment and management

In Northern Senegal, traditional cattle management systems (TRAD) which depend on natural forages coexist with intensified systems (INT) which rely on periodic supplementation with crop residues and local concentrates. This study aims to estimate the effects of seasons and management systems on the methane emissions of Gobra zebu, in relation to the diet's chemical composition and feed intake. Six Gobra zebu cows per management system were individually monitored over 10 months, diet and faeces were sampled each season and their chemical composition and dry matter intake (DMI) were predicted by near infrared spectroscopy. Each diet was fermented in vitro to assess methane production and volatile fatty acid concentration. The DMI and digestible organic matter intake (DOMI) decreased (P < 0.0001) during the dry seasons for both systems in the same range, but INT improved the crude protein of the diets (P < 0.0001). Enteric methane production (mmol.g^-1 dry matter) was lower for TRAD than INT, except during the rainy season when TRAD cows experienced a higher increase (P = 0.002). The methanogenic potential (methane production in vitro × DMI) varied with the seasons and the system with more accentuated variations for TRAD (P < 0.0001). Methanogenic potential shows true reflection of the effects of the seasons and management systems. The results highlight that enteric methane emissions varied with seasonal changes and that intensifying the diet induced no mitigating effect.

Faecal near-IR spectroscopy to determine the nutritional value of diets consumed by beef cattle in east Mediterranean rangelands

Rapid assessment of the nutritional quality of diets ingested by grazing animals is pivotal for successful cow-calf management in east Mediterranean rangelands, which receive unpredictable rainfall and are subject to hot-spells. Clipped vegetation samples are seldom representative of diets consumed, as cows locate and graze selectively. In contrast, faeces are easily sampled and their near-IR spectra contain information about nutrients and their utilization. However, a pre-requisite for successful faecal near-infrared reflectance spectroscopy (FNIRS) is that the calibration database encompass the spectral variability of samples to be analyzed. Using confined beef cows in Northern and Southern Israel, we calibrated prediction equations based on individual pairs of known dietary attributes and the NIR spectra of associated faeces (n=125). Diets were composed of fresh-cut green fodder of monocots (wheat and barley), dicots (safflower and garden pea) and natural pasture collected at various phenological states over 2 consecutive years, and, optionally, supplements of barley grain and dried poultry litter. A total of 48 additional pairs of faeces and diets sourced from cows fed six complete mixed rations covering a wide range of energy and CP concentrations. Precision (linearity of calibration, R2cal, and of cross-validation, R2cv) and accuracy (standard error of cross-validation, SEcv) were criteria for calibration quality. The calibrations for dietary ash, CP, NDF and in vitro dry matter digestibility yielded R2cal values >0.87, R2cv of 0.81 to 0.89 and SEcv values of 16, 13, 39 and 31 g/kg dry matter, respectively. Equations for nutrient intake were of low quality, with the exception of CP. Evaluation of FNIRS predictions was carried out with grazing animals supplemented or not with poultry litter, and implementation of the method in one herd over 2 years is presented. The potential usefulness of equations was also established by calculating the Mahalanobis (H) distance to the spectral centroid of a calibration population of 796 faecal samples collected throughout 2 years in four herds. Seasonal trends in pasture quality and responses to management practices were identified adequately and H<3.0 for 98% of faecal samples collected. We conclude that the development of FNIRS equations with confined animals is not only unexpensive and ethically acceptable, but their predictions are also sufficiently accurate to monitor dietary composition (but not intake) of beef cattle in east Mediterranean rangelands.

Applicability of near-infrared reflectance spectroscopy (NIRS) for determination of crude protein content in cowpea (Vigna unguiculata) leaves

There is uncertainty on how generally applicable near-infrared reflectance spectroscopy (NIRS) calibrations are across genotypes and environments, and this study tests how well a single calibration performs across a wide range of conditions. We also address the optimization of NIRS to perform the analysis of crude protein (CP) content in a variety of cowpea accessions (n = 561) representing genotypic variation as well as grown in a wide range of environmental conditions in Tanzania and Uganda. The samples were submitted to NIRS analysis and a predictive calibration model developed. A modified partial least-squares regression with cross-validation was used to evaluate the models and identify possible spectral outliers. Calibration statistics for CP suggests that NIRS can predict this parameter in a wide range of cowpea leaves from different agro-ecological zones of eastern Africa with high accuracy (R (2)cal = 0.93; standard error of cross-validation = 0.74). NIRS analysis improved when a calibration set was developed from samples selected to represent the range of spectral variability. We conclude from the present results that this technique is a good alternative to chemical analysis for the determination of CP contents in leaf samples from cowpea in the African context, as one of the main advantages of NIRS is the large number of compounds that can be measured at once in the same sample, thus substantially reducing the cost per analysis. The current model is applicable in predicting the CP content of young cowpea leaves for human nutrition from different agro-ecological zones and genetic materials, as cowpea leaves are one of the popular vegetables in the region.