Faecal near infrared reflectance spectroscopy (NIRS) to assess chemical composition, in vivo digestibility and intake of tropical grass by Creole cattle

Predicting Chemical Composition and Apparent Total Tract Digestibility on Freeze-Dried Not Ground Faeces Using Near-Infrared Spectroscopy in Pigs

The present study aimed to compare NIRS results using freeze-dried ground or not ground (FDG or FDNG) faeces to predict faecal chemical composition and apparent total tract digestibility (ATTD) coefficients. Two different batches of pigs were used (n = 20 mixed sex pens/batch; 11 pigs/pen; Duroc × (Large White × Landrace)). The first batch of pigs (B1; 50.1 ± 3.44 kg body weight (BW)) was used at 13 wks of age and the second batch (B2; 87.0 ± 4.10 kg BW) was used at 18 wks of age. For both B1 and B2, pens were assigned to five diets formulated to obtain a control [10.03 MJ of net energy (NE), 160.0 g of crude protein (CP), and 9.5 g of standardized ileal digestive (SID) lysine (Lys) per kg of feed], low protein (132.0 g CP and 7.5 g SID Lys), high protein (188.0 g CP and 11.5 g SID Lys), low energy (9.61 MJ NE/kg), and high energy (10.45 MJ NE/kg) diets. After a 10-day adaptation period, one faecal sample was collected daily from each pen floor during 6 days in both B1 and B2 (n = 120/batch). Faecal samples were freeze-dried and analysed via NIRS as FDNG and FDG faeces. Dry matter (DM), organic matter (OM), CP, gross energy (GE), fat, and ATTD coefficients were analysed/calculated. The NIRS calibrations were evaluated by cross-validation, splitting the data in four random groups, or using the leave-one-out method. For both FDNG and FDG faeces, coefficients of determination for calibration (R²_cv) and residual predictive deviation (RPD) values were: close to 0.9 and 3 for DM and CP, 0.7-0.8 and ≥2 for OM and GE, 0.6 and <2 for fat, and 0.54-0.75 and ≤2 for ATTD coefficients, respectively. CP was better predicted using FDG faeces (p < 0.05), while DM and OM ATTD were better predicted using FDNG faeces (p < 0.05). In conclusion, NIRS successfully predicts faeces' chemical components and ATTD coefficients of nutrients using FDNG or FDG faeces.

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.

Predicting fecal composition, intake, and nutrient digestibility in beef cattle consuming high forage diets using near infrared spectroscopy

The objective of this study was to develop near infrared spectroscopy (NIRS) calibrations to predict fecal nutrient composition, intake, and diet digestibility from beef cattle fed high forage diets. Heifers were fed 12 different forage-based diets (>95% forage dry matter basis) in 3 total collection digestibility studies, resulting in individual fecal samples and related spectra (n = 135), corresponding nutrient intake, and apparent total tract digestibility (aTTD) data. Fecal samples were also collected from steers grazing two annual and two perennial forage mixtures over two growing seasons. Samples (n = 13/paddock) were composited by paddock resulting in 30 samples from year 1, and 24 from year 2. The grazing fecal spectra (n = 54) were added to the existing fecal composition spectral library. Dried and ground fecal samples were scanned using a FOSS DS2500 scanning monochromator (FOSS, Eden Prairie, MN). Spectra were mathematically treated for detrend and scatter correction and modified partial least squares (MPLS) regression was performed. The coefficient of determination for cross validation (R²_cv) and standard error of cross validation (SECV) were used to evaluate the quality of calibrations. Prediction equations were developed for fecal composition [organic matter (OM), nitrogen (N), amylase-treated ash-corrected neutral detergent fiber (aNDFom), acid detergent fiber (ADF), acid detergent lignin (ADL), undigestible NDF after 240 h of in vitro incubation (uNDF), calcium (Ca), and phosphorus (P)], digestibility [DM, OM, aNDFom, N], and intake [DM, OM, aNDFom, N, uNDF]. The calibrations for fecal OM, N, aNDFom, ADF, ADL, uNDF, Ca, P resulted in R²_cv between 0.86 and 0.97 and SECV of 1.88, 0.07, 1.70, 1.10, 0.61, 2.00, 0.18, and 0.06, respectively. Equations predicting intake of DM, OM, N, aNDFom, ADL, and uNDF resulted in R²_cv values between 0.59 and 0.91, SECV values of 1.12, 1.10, 0.02, 0.69, 0.06, 0.24 kg·d^-1, respectively, and SECV values between 0.00 and 0.16 when expressed as % body weight (BW). Digestibility calibrations for DM, OM, aNDFom, and N resulted in R²_cv ranging from 0.65 to 0.74 and SECV values from 2.20 to 2.82. We confirm the potential of NIRS to predict fecal chemical composition, digestibility, and intake of cattle fed high forage diets. Future steps include validation of the intake calibration equations for grazing cattle using forage internal marker and modelling energetics of grazing growth performance.

Drought-Induced Challenges and Different Responses by Smallholder and Semicommercial Livestock Farmers in Semiarid Limpopo, South Africa—An Indicator-Based Assessment

Increased seasonal climatic variability is a major contributor to uncertainty in livestock-based livelihoods across Southern Africa. Erratic rainfall patterns and prolonged droughts have resulted in the region being identified as a climate ‘vulnerability hotspot’. Based on fieldwork conducted in the dry seasons in a semiarid region of South Africa, we present an interdisciplinary approach to assess the differential effects of drought on two types of livestock systems. Organic matter digestibility, faecal crude protein, C/N ratio and the natural abundance of faecal 15N and 13C isotopes were used as ecophysiological feed quality indicators between smallholder and semicommercial systems. These measurements were complemented with qualitative surveys. In a novel approach, we tested the potential of the isotopic signature to predict feed quality and present a significant relationship between organic matter digestibility and isotopic ratios. Indicators assessed smallholder feed quality to be significantly higher than semicommercial feed. However, animals from semicommercial farms were in significantly better condition than those from smallholding farms. Differential access to feed resources suggests that a complex feed–water–land nexus pushes smallholders into high reliance on off-farm supplements to bridge drought-induced feed deficits. The paper thus offers a contribution to intersectional work on drought effects on livestock keepers of semiarid South Africa and illustrates how ecophysiological indicators mirror socioeconomic differences.

A production-scale evaluation of nutritional monitoring and decision support software for free-ranging cattle in an arid environment

Range cattle in semi-arid regions are commonly limited by lack of nitrogen and other nutrients from grazing low-quality forage, with managers needing to monitor diet quality to address nutrient limitations. Near-infrared spectroscopy of faecal samples (FNIRS) is an accurate method used to determine diet quality in grazing animals. When combined with a nutritional balance software such as the Nutritional Balance Analyser (NUTBAL), FNIRS can monitor nutritional status and estimate weight change. We aimed to test the ability of NUTBAL to predict animal performance as represented by body condition score (BCS) in cattle grazing on a semi-desert rangeland. BCS and faecal samples were collected from a Red Angus herd (n=82) at the Santa Rita Ranch (June 2016–July 2017). Standing biomass and botanical composition were measured before each grazing period, and relative utilisation was measured following each grazing period. During the midpoint of grazing in each pasture, 30 BCS and a faecal composite of 15 samples were collected. Faecal derived diet quality varied between a maximum of 10.75% crude protein (CP) and 61.25% digestible organic matter (DOM) in early August 2016, to a minimum value of 4.22% CP and 57.68% DOM in January 2017. Three NUTBAL evaluations were conducted to determine the likelihood of accurately predicting animal performance: one with typical user defined inputs; one with improved environment and herd descriptive inputs; and one with these improvements plus the use of metabolisable protein in the model. This third evaluation confirmed the ability of FNIRS:NUTBAL to predict future BCS within 0.5 BCS more than 75% of the time. With this information, cattle managers in semi-arid regions can better address animal performance needs and nutrient deficiencies.

Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.

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.

Herbivore-Diet Analysis Based on Illumina MiSeq Sequencing: The Potential Use of an ITS2-Barcoding Approach to Establish Qualitative and Quantitative Predictions of Diet Composition of Mongolian Sheep

DNA-barcoding approaches to estimate the diet compositions of grazing animals have received significant attention, and particularly when combined with next-generation sequencing, these techniques have substantially improved in recent years. In this study, the identity and species composition of plant material ingested by Mongolian sheep were estimated through the use of 350 bp ITS2 gene sequences of the vegetation found in fecal samples. Four diets were formulated using varying amounts of eight plant species that are common in the grasslands of northern China. Sixteen Mongolian sheep were taken from pastures and randomly assigned to four groups, and each group received one of four diets. Each sheep was randomly assigned to one of 16 confinement pens and fed its respective diet for 12 consecutive days. Fecal samples were removed from each pen from days 7-12, preserved, and composited for each pen. All herbage species included in the daily diets were detected in each fecal sample, with the exception of Phragmites australis. Moreover, 12 additional different plant species were retrieved from feces of the experimental sheep. The obtained data provided preliminary support for the use of the ITS2 barcode to determine which plants were consumed. Moreover, the proportions of the herbage DNA sequences recovered from sheep feces and those of the herbage masses in the daily diets did not completely match. These results indicate that the non-Gramineae DNA sequences amplified with ITS2 primers (including those of Chenopodium album, Artemisia scoparia, Artemisia tanacetifolia, and Medicago sativa) far exceeded those of the Gramineae species (including Leymus chinensis and Puccinellia distans), which constitute the largest share of the experimental diets. A significant positive correlation (Spearman's ρ = 0.376, P = 0.003) between the actual herbage mass proportions in the experimental diets and the herbage-DNA-sequence proportions provided sufficiently favorable support for the further investigation of DNA barcoding for the quantification of plants in feces. A significant regression coefficient was found between the relative DNA-sequence proportions of L. chinensis ( R² = 0.82, P < 0.0001), P. distans ( R² = 0.64, P = 0.0017), and C. album ( R² = 0.98, P < 0.0001) and their respective herbage mass proportions. The quantitative relationship can be expressed by the linear-regression equations y = 0.90 x - 0.22, y = 0.98 x - 0.03, and y = 5.00 x - 0.25, respectively. Thus, these results demonstrate that dietary-DNA-barcoding methods exhibited potential in providing valuable quantitative information regarding food-item components. However, it should be noted that this explorative data needs to be further improved by using additional genes and by creating a sophisticated reference database, thus enhancing both quality and accuracy of the obtained results.

Fecal Near-Infrared Reflectance Spectroscopy Prediction of the Feed Value of Temperate Forages for Ruminants and Some Parameters of the Chemical Composition of Feces: Efficiency of Four Calibration Strategies

The forage feed value determined by organic matter digestibility (OMD) and voluntary intake (VI) is hard and expensive. Thus, several indirect methods such as near infrared reflectance (NIR) spectroscopy have been developed for predicting the feed value of forages. In this study, NIR spectra of 1040 samples of feces from sheep fed fresh temperate forages were used to develop calibration models for the prediction of fecal crude ash (CA), fecal crude protein (CP), fresh forage OMD, and VI. Another 136 samples of feces were used to assess these models. Four calibration strategies were compared: (1) species-specific calibration; (2) family-specific calibration; (3) a global procedure; and (4) a LOCAL approach. The first three strategies were based on classical regression models developed on different sample populations, whereas the LOCAL approach is based on the development models from selected samples spectrally similar to the sample to be predicted. The first two strategies use feces-samples grouping based on the species or the family of the forage ingested. Forage calibration data sets gave value ranges of 79-327 g/kg dry matter (DM) for CA, 65-243 g/kg DM for CP, 0.52-0.85 g/g for OMD, and 34.7-100.5 g DM/kg metabolic body weight (BW^0.75) for VI. The prediction of CA and CP content in feces by species-specific fecal NIR (FNIR) spectroscopy models showed lower standard error of prediction (SEP) (CA 15.03 and CP 7.48 g/kg DM) than family-specific (CA 21.93 and CP 7.69 g/kg DM), global (CA 19.83 and CP 7.98 g/kg DM), or LOCAL (CA 30.85 and CP 8.10 g/kg DM) models. For OMD, the LOCAL procedure led to a lower SEP (0.018 g/g) than the other approaches (0.023, 0.024, and 0.023 g/g for species-specific, family-specific, and global models, respectively). For VI, the LOCAL approach again led to a lower SEP (6.15 g/kg BW^0.75) than the other approaches (7.35, 8.00, and 8.13 g/kg BW^0.75 for the species-specific, family-specific, and global models, respectively). LOCAL approach performed on FNIR spectroscopy samples gives more precise models for predicting OMD and VI than species-specific, family-specific, or global approaches.

Characterization of the variation in the daily excretion of faecal constituents and digestibility predictions in beef cattle fed feedlot diets using near-infrared spectroscopy

Six heifers were individually housed and assigned to once (FF1) or twice (FF2) daily feeding regimes over backgrounding and finishing periods. Following adaptation, total faecal collections were conducted at 4-h intervals and at 24-h intervals over 4 d, and near-infrared spectroscopy (NIRS) was used to predict faecal organic matter (OM), starch, nitrogen (N), neutral detergent fibre (NDF), acid detergent fibre (ADF), and acid detergent lignin (ADL). At each interval, NIRS calibrations were used to estimate faecal constituents and ADL to calculate apparent (aTTD) and estimated (eTTD) total tract digestibility. Faecal dry matter (DM) (%), NDF, and ADF varied among 4-h interval samples in the backgrounding period and faecal DM, starch, NDF, ADF, and ADL in the finishing period. Faecal starch was able to predict aTTD during both feeding periods (backgrounding: R2 = 0.96, P < 0.01; finishing: R2 = 0.98, P < 0.01). The NIRS calibrations for predicting aTTD using the 4-h interval samples or the 4-d–24-h composite were least accurate for NDF and ADF. Most 4-h interval samples could be used to predict eTTD of nutrients, and aside from starch in the finishing period, there were no differences in eTTD using faecal samples collected over 4-h intervals versus those collected over 4 d. Spot faecal samples collected at any time point from multiple cattle have the potential to predict digestibility. Timing of sampling after feeding must be standardized to predict starch digestibility during the finishing period, with samples between 0–4 h and 8–16 h generating estimates of both starch concentration and digestibility that were closest to that derived from 4-d–24-h composite samples.

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.

Comparison of stocking methods for beef production in northern Australia: seasonal diet quality and composition

Managing and measuring the grazing and nutrition of cattle are required to improve the productivity and profitability of beef businesses in northern Australia. The quality and composition of the diet selected by cattle grazing in three stocking methods (continuous, extensive rotation and intensive (cell) rotation) on nine commercial properties in Queensland were estimated using near infrared reflectance spectroscopy analyses of fresh faeces; 585 faecal samples were analysed between 2005 and 2009. Sites were in two regions (north and south Queensland) and on two vegetation communities, namely brigalow (Acacia harpophylla F. Muell. ex Benth.) on clay soils and eucalypts on light-textured soil types. Pastures were dominated by perennial sown exotic grass species, predominantly Cenchrus ciliaris L. (buffel grass) at five sites and Urochloa mosambicensis (Hack.) (Sabi grass) at one site, and by native perennial tussock grasses at three sites. Seasonal profiles of dietary crude protein, dry matter digestibility, faecal nitrogen concentration, proportion of non-grass, ratio of crude protein to digestibility and an estimate of liveweight gain are presented for each stocking method. Overall, dietary crude protein, digestibility, faecal nitrogen, the crude protein : digestibility ratio and liveweight gain were significantly higher for animals grazed continuously, with short rest periods, than for animals in extensive or intensive rotations. There was a significant interaction between stocking method and pasture growing conditions, measured as a simulated growth index, for dietary crude protein and faecal nitrogen. There was no difference between stocking methods during periods when the index was <0.2, indicating no pasture growth, but during periods of active growth (index >0.5), crude protein and faecal nitrogen were higher with continuous grazing than in the extensive and intensive rotations. For cattle producers considering alternative stocking methods, the results suggest they can obtain similar ecological responses under any of the three methods and diet quality will be higher during the pasture growing period in continuously grazed pastures.

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.

Application of fecal near-infrared spectroscopy and nutritional balance software to monitor diet quality and body condition in beef cows grazing Arizona rangeland

Monitoring the nutritional status of range cows is difficult. Near-infrared spectroscopy (NIRS) of feces has been used to predict diet quality in cattle. When fecal NIRS is coupled with decision support software such as the Nutritional Balance Analyzer (NUTBAL PRO), nutritional status and animal performance can be monitored. Approximately 120 Hereford and 90 CGC composite (50% Red Angus, 25% Tarentaise, and 25% Charolais) cows grazing in a single herd were used in a study to determine the ability of fecal NIRS and NutbalPro to project BCS (1 = thin and 9 = fat) under commercial scale rangeland conditions in central Arizona. Cattle were rotated across the 31,000 ha allotment at 10 to 20 d intervals. Cattle BCS and fecal samples (approximately 500 g) composited from 5 to 10 cows were collected in the pasture approximately monthly at the midpoint of each grazing period. Samples were frozen and later analyzed by NIRS for prediction of diet crude protein (CP) and digestible organic matter (DOM). Along with fecal NIRS predicted diet quality, animal breed type, reproductive status, and environmental conditions were input to the software for each fecal sampling and BCS date. Three different evaluations were performed. First, fecal NIRS and NutbalPro derived BCS was projected forward from each sampling as if it were a "one-time only" measurement. Second, BCS was derived from the average predicted weight change between 2 sampling dates for a given period. Third, inputs to the model were adjusted to better represent local animals and conditions. Fecal NIRS predicted diet quality varied from a minimum of approximately 5% CP and 57% DOM in winter to a maximum of approximately 11% CP and 60% DOM in summer. Diet quality correlated with observed seasonal changes and precipitation events. In evaluation 1, differences in observed versus projected BCS were not different (P > 0.1) between breed types but these values ranged from 0.1 to 1.1 BCS in Herefords and 0.0 to 0.9 in CGC. In evaluation 2, differences in observed versus projected BCS were not different (P > 0.1) between breed types but these values ranged from 0.00 to 0.46 in Hereford and 0.00 to 0.67 in CGC. In evaluation 3, the range of differences between observed and projected BCS was 0.04 to 0.28. The greatest difference in projected versus observed BCS occurred during periods of lowest diet quality. Body condition was predicted accurately enough to be useful in monitoring the nutrition of range beef cows under the conditions of this study.

Application of rumen microorganisms for anaerobic bioconversion of lignocellulosic biomass

Rumen in the mammalian animals is a natural cellulose-degrading system and the microorganisms inside have been found to be able to effectively digest lignocellulosic biomass. Furthermore, methane or volatile fatty acids, which could be further converted to other biofuels, are the two major products in such a system. This paper offers an overview of recent development in the application of rumen microorganisms for lignocellulosic biomass conversion. Application of recent molecular tools in the analysis of rumen microbial community, progress in the development of artificial rumen reactors, the latest research results about characterizing rumen-dominated anaerobic digestion process and energy products are summarized. Also, the potential application of such a rumen-dominated process is discussed.

Technical note: Use of near-infrared reflectance spectroscopy to predict intake and digestibility in bulls and steers

Multiple fecal samples were collected from growing Angus bulls (264 to 419 kg of BW, 3.0 to 11.4 kg/d of DMI) to predict DMI of a corn-silage-based diet. Contemporaneous digestion trials were conducted with the same diet in 12 steers in yr 1 to 3 and bulls in yr 4. Near-infrared spectra from fecal samples (n = 730 from 282 growing bulls, n = 240 from 36 steers and 12 bulls for digestion trials) were obtained from dried and ground fecal samples, and modified partial least squares regression was used to develop equations to predict DMI and DM digestibility (DMD). Although mean predicted DMI of the growing bulls (7.52 ± 0.04 kg/d or 22.4 ± 0.1 g/kg of BW) was within 2% of mean measured DMI (7.63 ± 0.06 kg/d or 22.7 ± 0.1 g/kg of BW), the mean of paired differences within samples (0.11 ± 0.04 kg/d or 0.3 ± 0.1 g/kg of BW) was greater (P < 0.01) than zero. Measured DMD (72.3 ± 0.5%) was identical (P < 0.97) to predicted DMD (72.3 ± 0.5%), and DMD for bulls in the digestion trial did not differ (P < 0.27) from DMD for steers. Prediction of intake requires incorporation of some measured values from the set of fecal samples to be predicted. Lack of similarity between spectra of fecal grab samples from the growing bulls and daily fecal collection of steers and bulls in the digestion trials in this study indicates the need for further verification before prediction of DMD with fecal grab samples.

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.

Rapid prediction of acid detergent fiber, neutral detergent fiber, and acid detergent lignin of rice materials by near-infrared spectroscopy

A rapid predictive method based on near-infrared spectroscopy (NIRS) was developed to measure acid detergent fiber (ADF), neutral detergent fiber (NDF), and acid detergent lignin (ADL) of rice stem materials. A total of 207 samples were divided into two subsets, one subset (approximately 136 samples) for calibration and cross-validation and the other subset for independent external validation to evaluate the calibration equations. Different mathematical treatments were applied to obtain the best calibration and validation results. The highest coefficient of determination for calibration (R2) and coefficient of determination for cross-validation (1-VR) were 0.968 and 0.949 for ADF, 0.846 and 0.812 for NDF, and 0.897 and 0.843 for ADL, respectively. Independent external validation still gave a high coefficient of determination for external validation (r2) and a low standard error of performance (SEP) for the three parameters; the best validation results were SEP = 0.933 and r2 = 0.959 for ADF, SEP = 2.228 and r2 = 0.775 for NDF, and SEP = 0.616 and r2 = 0.847 for ADL, indicating that NIR gave a sufficiently accurate prediction of ADF and ADL content of rice material but a less satisfactory prediction for NDF. This study suggested that routine screening for these forage quality parameters with large numbers of samples is possible with NIRS in early-generation selection in rice-breeding programs.