Data mining old digestibility trials for nutritional monitoring in confined goats with aids of fecal near infra-red spectrometry

Application of Near-Infrared Reflectance Spectroscopy for Predicting Chemical Composition of Feces in Holstein Dairy Cows and Calves

Traditional methods for determining the chemical composition of cattle feces are uneconomical. In contrast, near-infrared reflectance spectroscopy (NIRS) has emerged as a successful technique for assessing chemical compositions. Therefore, in this study, the feasibility of NIRS in terms of predicting fecal chemical composition was explored. Cattle fecal samples were subjected to chemical analysis using conventional wet chemistry techniques and a NIRS spectrometer. The resulting fecal spectra were used to construct predictive equations to estimate the chemical composition of the feces in both cows and calves. The coefficients of determination for calibration (RSQ) were employed to evaluate the calibration of the predictive equations. Calibration results for cows (dry matter [DM], RSQ = 0.98; crude protein [CP], RSQ = 0.93; ether extract [EE], RSQ = 0.91; neutral detergent fiber [NDF], RSQ = 0.82; acid detergent fiber [ADF], RSQ = 0.89; ash, RSQ = 0.84) and calves (DM, RSQ = 0.92; CP, RSQ = 0.89; EE, RSQ = 0.77; NDF, RSQ = 0.76; ADF, RSQ = 0.92; ash, RSQ = 0.97) demonstrated that NIRS is a cost-effective and efficient alternative for assessing the chemical composition of dairy cattle feces. This provides a new method for rapidly predicting fecal chemical content in cows and calves.

Direct effects of phenolic compounds on the mammary gland: In vivo and ex vivo evidence

We assessed the potential of Pistacia lentiscus (lentisk) phenolic compounds to enhance production of milk composition in lactating goats and caprine primary mammary epithelial cells (MEC). Damascus goats were given a lentisk infusion (LI) or fresh water (FW) to drink, in a crossover design. Milk from LI vs. FW goats was 0.43% richer in fat and 30% in omega 3 fatty acids. Lentisk infusion enhanced antioxidant capacity of plasma and milk by 37% and 30% respectively, and induced transcriptional activation of antioxidant genes. To assess the direct effect of polyphenols on milk quality in terms of composition and antioxidant capacity, we used plasma collected from goats fed hay (HP) or browsed on phenolic compounds-rich pasture (primarily lentisk; PP) as a conditioning medium for primary culture of MEC. PP increased 2-fold cellular triglyceride content and 2.4-fold intracellular casein, and increased ATP production and non-mitochondrial oxygen consumption. Taken together, the results imply that lentisk phenolic compounds affect blood, MEC and milk oxidative status, which increase fat production by the mammary gland.

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.

The effect of willow fodder feeding on immune cell populations in the blood and milk of late-lactating dairy goats

In a previous study, we showed that access to willow fodder decreased somatic cell counts (SCC) in the milk of local Mamber goats grazing in brushland at the end of lactation. To test whether the consumption of willow affects the cells of the immune system, Alpine crossbred dairy goats grazing in the same environment were either offered free access to freshly cut willow fodder (W, n = 24) or not (C, n = 24) for 2 weeks. The willow fodder contained 7.5 g/kg DM of salicin. The other major secondary compounds were catechin, myricitrin, hyperin and chlorogenic acid (2.2, 2.6, 1.0 and 0.75 g/kg DM, respectively). Udder health status was determined before the experiment, and each of the two groups included five (W) or six (C) goats defined as infected, as established by microbial cfu in milk, and 19 (W) or 18 (C) non-infected goats. Goats ingested, on average, 600 g of DM from willow (25% of food intake), resulting in minor changes in dietary quality compared to the controls, as established by faecal near-IR spectrometry. Throughout the 2 weeks of experiment, differences between groups in dietary CP contents were minor and affected neither by infection nor by access to willow; the dietary percentage of neutral detergent fibre (NDF) decreased in C and increased in W; dietary acid detergent fibre (ADF) increased; and the dietary tannin contents decreased for both treatments. However, milking performance and milk quality attributes in both W and C goats were similar. Initial SCC and milk neutrophil (cluster of differentiation (CD)18⁺ and porcine granulocyte (PG)68) cell counts were higher in infected than in non-infected goats; counts decreased significantly in W but not in C uninfected goats. The percentage of CD8⁺ T-cells increased in all C goats, while in the W group, a significant increase was found only for infected goats. The consumption of willow mitigated an increase in CD8⁺ in blood and triggered an increase in CD8⁺ in milk, suggesting an immune-regulatory effect independent of udder status. To our knowledge, this is the first report of a direct nutraceutical effect of fodder ingestion on the immune status of goats.

Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging

BACKGROUND

To investigate potential effects of herbicide phytotoxic on crops, a major challenge is a lack of non-destructive and rapid methods to detect plant growth that could allow characterization of herbicide-resistant plants. In such a case, hyperspectral imaging can quickly obtain the spectrum for each pixel in the image and monitor status of plants harmlessly.

METHOD

Hyperspectral imaging covering the spectral range of 380-1030 nm was investigated to determine the herbicide toxicity in rice cultivars. Two rice cultivars, Xiushui 134 and Zhejing 88, were respectively treated with quinclorac alone and plus salicylic acid (SA) pre-treatment. After ten days of treatments, we collected hyperspectral images and physiological parameters to analyze the differences. The score images obtained were used to explore the differences among samples under diverse treatments by conducting principal component analysis on hyperspectral images. To get useful information from original data, feature extraction was also conducted by principal component analysis. In order to classify samples under diverse treatments, full-spectra-based support vector classification (SVC) models and extracted-feature-based SVC models were established. The prediction maps of samples under different treatments were constructed by applying the SVC models using extracted features on hyperspectral images, which provided direct visual information of rice growth status under herbicide stress. The physiological analysis with the changes of stress-responsive enzymes confirmed the differences of samples under different treatments.

RESULTS

The physiological analysis showed that SA alleviated the quinclorac toxicity by stimulating enzymatic activity and reducing the levels of reactive oxygen species. The score images indicated there were spectral differences among the samples under different treatments. Full-spectra-based SVC models and extracted-feature-based SVC models obtained good results for the aboveground parts, with classification accuracy over 80% in training, validation and prediction set. The SVC models for Zhejing 88 presented better results than those for Xiushui 134, revealing the different herbicide tolerance between rice cultivars.

CONCLUSION

We develop a reliable and effective model using hyperspectral imaging technique which enables the evaluation and visualization of herbicide toxicity for rice. The reflectance spectra variations of rice could reveal the stress status of herbicide toxicity in rice along with the physiological parameters. The visualization of the herbicide toxicity in rice would help to provide the intuitive vision of herbicide toxicity in rice. A monitoring system for detecting herbicide toxicity and its alleviation by SA will benefit from the remarkable success of SVC models and distribution maps.

The Effect of Time and Method of Storage on the Chemical Composition, Pepsin-Cellulase Digestibility, and Near-Infrared Spectra of Whole-Maize Forage

This study examined the effects of long-term storage conditions on the chemical composition, pepsin-cellulase dry matter digestibility (PCDMD), and visible (VIS)/near infrared spectra (NIR) of forage. Eighteen samples of different whole-crop maize varieties originally harvested in 1987 were used. After drying, these samples were analyzed in the laboratory for ash, crude protein (CP), structural carbohydrates, total soluble carbohydrates (TSC), starch and PCDMD, and the remaining samples were stored frozen (at −20°C) or at barn temperature (ambient temperatures ranged from −8.5 °C to 27.1 °C). In 2016, the samples were analyzed for ash, CP, structural carbohydrates, TSC, starch and PCDMD. The visible/NIR spectra of both storage methods were obtained. Chemical composition and PCDMD analyses revealed significant differences (p < 0.05) between the storage methods for TSC but not for the other parameters (p > 0.05). After sample harvesting in 1987, the analyses were compared with those in 2016. It was found that the post-harvest TSC and ash content were higher (p < 0.05) and lower (p < 0.05), respectively, during 2016. No significant differences were found for starch and PCDMD. Important differences between the VIS/NIR spectra of both storage methods were obtained in the VIS segment, particularly in the area between 630 and 760 nm. We concluded that storing dry forage samples at ambient temperature for a very long time (29 years) did not change their nutritive value compared to the values obtained before storage.

Spatial and Temporal Monitoring of Pasture Ecological Quality: Sentinel-2-Based Estimation of Crude Protein and Neutral Detergent Fiber Contents

Frequent, region-wide monitoring of changes in pasture quality due to human disturbances or climatic conditions is impossible by field measurements or traditional ecological surveying methods. Remote sensing imagery offers distinctive advantages for monitoring spatial and temporal patterns. The chemical parameters that are widely used as indicators of ecological quality are crude protein (CP) content and neutral detergent fiber (NDF) content. In this study, we investigated the relationship between CP, NDF, and reflectance in the visible–near-infrared–shortwave infrared (VIS–NIR–SWIR) spectral range, using field, laboratory measurements, and satellite imagery (Sentinel-2). Statistical models were developed using different calibration and validation data sample sets: (1) a mix of laboratory and field measurements (e.g., fresh and dry vegetation) and (2) random selection. In addition, we used three vegetation indices (Normalized Difference Vegetative Index (NDVI), Soil-adjusted Vegetation Index (SAVI) and Wide Dynamic Range Vegetation Index (WDRVI)) as proxies to CP and NDF estimation. The best models found for predicting CP and NDF contents were based on reflectance measurements (R2 = 0.71, RMSEP = 2.1% for CP; and R2 = 0.78, RMSEP = 5.5% for NDF). These models contained fresh and dry vegetation samples in calibration and validation data sets. Random sample selection in a model generated similar accuracy estimations. Our results also indicate that vegetation indices provide poor accuracy. Eight Sentinel-2 images (December 2015–April 2017) were examined in order to better understand the variability of vegetation quality over spatial and temporal scales. The spatial and temporal patterns of CP and NDF contents exhibit strong seasonal dependence, influenced by climatological (precipitation) and topographical (northern vs. southern hillslopes) conditions. The total CP/NDF content increases/decrease (respectively) from December to March, when the concentrations reach their maximum/minimum values, followed by a decline/incline that begins in April, reaching minimum values in July.

Prediction of Lignin Content in Ruminant Diets and Fecal Samples Using Rapid Analytical Techniques

The measurement of lignin content in ruminant diet and fecal samples is important for digestibility studies, but it is typically time-consuming and costly. The work reported involves correlation of traditional wet chemistry data with those from three rapid instrumental techniques, Fourier transform infrared spectroscopy (FTIR), conventional thermogravimteric analysis (TGA), and high-resolution TGA (MaxRes TGA) to predict the lignin content of diets and feces from digestibility trials. Calibration and performance data indicate that the FTIR model is acceptable for screening, while the conventional and MaxRes TGA predictions are high accuracy for quantitative analysis. Cross validation and model performance data reveal that MaxRes TGA provides the best-performing predictive model. This work shows that MaxRes TGA can accurately predict lignin content in ruminant diet and fecal samples with distinct advantages over traditional wet chemistry: namely, the requirement of small sample size, ease of sample preparation, speed of analysis, and high sample throughput at considerably lower cost.

Assessment of fecal near-infrared spectroscopy to predict feces chemical composition and apparent total-tract digestibility of nutrients in pigs

Apparent total-tract digestibility (ATTD) of nutrients could be an alternative measure of feed efficiency (FE) when breeding for robust animals that are fed fiber-rich diets. Apparent total-tract digestibility of nutrients requires measuring individual feed intake of a large number of animals which is expensive and complex. Alternatively, ATTD of nutrients and feces chemical composition can be predicted using fecal near-infrared reflectance spectroscopy (FNIRS). The objective of this study was to assess if the feces chemical composition and ATTD of nutrients can be predicted using FNIRS that originate from various pig-experimental datasets. Fecal samples together with detailed information on the feces chemical composition and ATTD of nutrients were obtained from four different pig experiments. Feces near-infrared spectroscopy was analyzed from fecal samples of a complete dataset. The model was calibrated using the FNIRS and reference samples of feces chemical composition and ATTD of nutrients. The robustness and predictability of the model were evaluated by the r2 and the closeness between SE of calibration (SEC) and SE of cross-validation (SECV). Prediction of the feces chemical components and ATTD of nutrients were successful as SEC and SECV were equivalent. Calibration model was developed to estimate the ATTD of nutrients and fecal chemical composition from the FNIRS and worked well for OM (r2 = 0.94; SEC = 48.5; SECV = 56.6), CP (r2 = 0.89; SEC = 18.1; SECV = 18.8), GE (r2 = 0.92; SEC = 1.2; SECV = 1.4), NDF (r2 = 0.94; SEC = 55; SECV = 60.2), OM digestibility (r2 = 0.94; SEC = 5.5; SECV = 6.7), GE digestibility (r2 = 0.88; SEC = 2.3; SECV = 2.6), and fat digestibility (r2 = 0.79; SEC = 6, SECV = 6.8). However, the SE of prediction was slightly higher than what has been reported in another study. The prediction of feces chemical composition for fat (r2 = 0.69; SEC = 11.7, SECV = 12.3), CP digestibility (r2 = 0.63; SEC = 2.3; SECV = 2.7), and NDF digestibility (r2 = 0.64, SEC = 7.7, SECV = 8.8) was moderate. We conclude that the FNIRS accurately predicts the chemical composition of feces and ATTD of nutrients for OM, CP, and GE. The approach of FNIRS is a cost-effective method for measuring digestibility and FE in a large-scale pig-breeding programs.

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.

Prediction of diet quality for sheep from faecal characteristics: comparison of near-infrared spectroscopy and conventional chemistry predictive models

This study evaluated the ability of equations developed from the analysis of faecal material by conventional chemistry (F.CHEM), and by near-infrared spectroscopy (F.NIRS), to predict intake and digestibility of forages fed with or without supplements. In vivo datasets were obtained using 30 sheep and 25 diets to provide 124 diet–faecal pairs, with each sheep fed four or five of the diets. The diets were five forages fed alone or with urea, molasses, cottonseed meal or sorghum grain supplements. Ninety-nine diet–faecal pairs were selected at random, but ensuring that all diets were represented and both the F.CHEM and F.NIRS prediction equations were developed from this dataset. The remaining 25 diet–faecal pairs were used as a validation dataset. Regressions for F.CHEM were developed by stepwise regression, and F.NIRS prediction equations were developed by partial least-squares regression. Prediction equations based solely on faecal analyte concentrations (F.CHEMc) had poor predictive ability, and models incorporating faecal constituent excretion rates (F.CHEMe) were the best at predicting feed constituent intakes. These models had slightly lower standard errors of prediction (SEP) for organic matter (OM) intake and digestible OM intake compared with the F.NIRS models that did not include faecal excretion rates. However, F.NIRS models had lower SEP for protein intake and OM digestibility. Good agreement between the F.CHEMe and F.NIRS methods was evident (according to the 95% limits-of-agreement test), and both predicted the reference values precisely and with small bias. Equations derived from a dataset that included representatives of all diets used in the experiment gave much better prediction of diet characteristics than those developed from a dataset constructed entirely at random. Equations for F.NIRS developed in this way successfully predicted the characteristics of diets that included forages fed alone and with the type of supplements used in tropical Australia.

Predicting the quality of browse-containing diets fed to sheep using faecal near-infrared reflectance spectroscopy

The potential of data collected from past feeding trials to derive faecal near-infrared reflectance spectroscopy (fNIRS) calibrations for predicting the attributes of browse-containing sheep diets was examined. Reference data and faecal near-infrared spectrum pairs (n = 240) originated from five feeding trials involving 40 diets consisting of varying levels of fresh browse and oaten chaff. The fNIRS calibrations were developed to predict crude protein (CP), total phenolics (TP), total tannin (TT) and phosphorus (P) contents, protein precipitation capacity of tannin (PPC), in vivo digestibility of dry matter (DMD), organic matter (OMD) and crude protein (CPD) and in vitro OMD (IVOMD), metabolisable energy (ME) and short chain fatty acid production (eSCFA) in the diet. The precision of calibrations was evaluated by the coefficient of determination (R2c) and standard error (SEC) of calibration. The predictive ability of calibrations was evaluated by standard error of cross-validation (SECV), standard error of prediction (SEP), slope of the validation regression and the ratio of the standard deviation of the reference data to the SECV (RPD). For all fNIRS calibrations, R2c was >0.80 and SEC was close to the respective SECV. Slope of the validation regressions did not deviate from 1 for chemical attributes but deviated from 1 for functional attributes (except eSCFA). The RPD of DMD and OMD was <3, whereas the ratio was >3 for CP, TP, TT, PPC, P, CPD, IVOMD, ME and eSCFA calibrations. Data derived from the past feeding trials could be used to derive robust fNIRS calibrations to predict chemical attributes (CP, TP, TT, PPC, P) of browse-containing sheep diets. Although, fNIRS calibrations predicting dietary in vitro functional properties (digestibility and ME) were superior to those predicting in vivo functional properties, both were not so robust. Statistics of fNIRS calibrations derived using reference data originating from in vitro methods needs to be carefully interpreted.