A fecal near-infrared reflectance spectroscopy-aided methodology to determine goat dietary composition in a Mediterranean shrubland

An ecologically sound approach to the problem of brush encroachment onto Israeli rangeland might be their utilization by goats, but better knowledge of the feeding selectivity and ability of goats to thrive in encroached areas is required to devise viable production systems. Direct observation of bites could provide precise and accurate estimates of diet selection, but construction of a sufficiently large database would require too much time. The present study describes the first attempt to construct fecal near-infrared reflectance spectroscopy (NIRS) calibrations of the botanical and nutritional composition of the diet, and of the total intake of free-ranging goats, based on reference values determined with bite-count procedures. Calibration of fecal NIRS was based on 43 observations encompassing 3 goat breeds and 4 periods (spring, summer, and fall of 2004, and spring of 2005). Each observation comprised 242 min of continuous recording of the species and bite-type category selected by a single animal, on each of 2 consecutive days. The mass and chemical quality of each species and bite-type category-a total of more than 200,000 bites-were determined by using the simulated bite technique. Associated feces were scanned in the 1,100- to 2,500-nm range with a reflectance monochromator. Fecal NIRS calibrations had reasonable precision for dietary percentages of the 3 main botanical components: herbaceous vegetation (as one category; R(2) = 0.85), Phillyrea latifolia (R(2) = 0.89), and tannin-rich Pistacia lentiscus (R(2) = 0.77), with SE of cross-validation (SECV) of 7.8, 6.3, and 5.6% of DM, respectively. The R(2) values for dietary percentages of CP, NDF, IVDMD, and polyethylene glycol-binding tannins were 0.93, 0.88, 0.91, and 0.74, respectively, with SECV values of 0.9, 2.1, 4.3, and 0.9% of DM, respectively. The R(2) values for intakes of herbaceous vegetation, P. latifolia, and P. lentiscus were 0.80, 0.75, and 0.65, with SECV values of 71, 64, and 46 g of DM/d, respectively. The R(2) values for the daily nutrient intakes were below 0.60. Fecal NIRS data can be used to expand the databases of botanical and nutritional dietary composition when observed and resident animals graze simultaneously, but intakes should be calculated from fecal NIRS-predicted dietary DM composition and an independent evaluation of DMI.     

黄土高原13种栽培牧草营养成分NIRS模型分析

对2012-2013年黄土高原种植的13个牧草品种、780份干草样品的营养成分建立了近红外光谱(near infrared reflectance spectroscopy,NIRS)的检测模型。豆科牧草的粗脂肪(EE)、酸性洗涤纤维(ADF)和粗灰分(Ash)建模结果最好,其定标决定系数(RSQ)0.94,交叉验证相关系数(1-VR)0.7最高,定标标准分析误差(SEC)在0.071~0.713,交叉校验定标标准分析误差(SECV)在0.160~2.751。禾本科牧草的EE和可溶性糖(WSC)建模结果最好,RSQ分别达0.916和0.859,1-VR分别为0.609和0.810,SEC和SECV分别是0.250、1.488和0.505、3.172。菊科和车前科牧草的模型,除ADF外,其它指标预测的稳定性和准确性较为理想,RSQ在0.85以上,1-VR在0.70以上,SEC和SECV分别在0.361~3.557和0.495~4.602。NIRS对豆科粗蛋白(CP)和WSC的数值预测较差,RSQ仅0.55,对禾本科CP、ADF、中性洗涤纤维(NDF)、Ash及菊科和车前科的ADF的预测稍差,RSQ0.7。     

Energy evaluation of extruded compound foods for dogs by near-infrared spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was used to predict the chemical composition, apparent digestibility and digestible nutrients and energy content of commercial extruded compound foods for dogs. Fifty-six foods of known chemical composition and in vivo apparent digestibility were analysed overall and 51 foods were used to predict gross energy digestibility and digestible energy content. Modified partial least square calibration models were developed for organic matter (OM), crude protein (CP), ether extract (EE), crude fibre (CF), nitrogen free extracts (NFE) and gross energy (GE) content, the apparent digestibility (OMD, CPD, EED, NFED and GED) and the digestible nutrient and energy content (DOM, DCP, DEE, DNFE and DE) of foods. The calibration equations obtained were evaluated by the standard error and the determination coefficient of cross-validation. The cross-validation coefficients of determination (R) were 0.61, 0.99, 0.91, 0.96, 0.94 and 0.92 for OM, CP, EE, CF, NFE and GE, the corresponding standard error of cross-validation (SECV) being 5.80, 3.51, 13.35, 3.64 and 16.95 g/kg dry matter (DM) and 0.29 MJ/kg DM respectively. The prediction of apparent digestibility was slightly less accurate, but NIRS prediction of digestible nutrient (g/kg DM) and DE (MJ/kg DM) gave satisfactory results, with high R (0.93, 0.97, 0.93, 0.83 and 0.93 for DOM, DCP, DEE, DNFE and DE respectively) and relatively low SECV (11.55, 6.85, 12.14 and 22.98 g/kg DM and 0.47 MJ/kg DM). It is concluded that the precision of NIRS in predicting the energy value of compound extruded foods for dogs is similar or better than by proximate analysis, as well as being faster and more accurate.     

用近红外反射光谱方法测定乳清粉的品质性状

本文用近红外反射光谱(NIRS)直接测定乳清粉的蛋白质、脂肪、灰份含量，均有很好的效果。同时对定标光谱进行不同的数学处理和散射处理，并采用不同的统计方法来优化这种成份的NIRS分析。建模时标准误很小而决定系数高。校正标准误、检验工作标准误、校正决定系数以及检验决定系数蛋白质依次分别为0．589，1．448，0．999和0．977，脂肪分别为0.257，0．570，0．989和0．938，灰份分别为0．178，0．206，0．990和0．988。同时对乳清粉品质的NIRS测定作了讨论。     

Relationship between fecal crude protein concentration and diet organic matter digestibility in cattle

The positive relationship between fecal CP concentration and diet OM digestibility in cattle, which is based on increasing undigested microbial CP and decreasing fecal OM as OM digestibility increases, may be used as an indirect method for estimating diet OM digestibility from fecal CP concentration. Results of digestibility trials (445 individual observations) conducted at Hohenheim and Braunschweig, Germany, and at Gumpenstein, Austria, were used to study the relationship between CP concentration in feces (x, g/kg OM) and OM digestibility (y, %). The best fit was obtained with the curvilinear relationship y = ai -107.7e(-0.01515 x x), with a1 = 79.76 and a2 = 72.86 (R2 = 0.82; residual SD = 2.7; SE = 0.13), which takes into account the effects of location (i = 1 for Braunschweig and Hohenheim, and i = 2 for Gumpenstein). Dietary CP and crude fat concentration, and DMI had no effect on fecal CP content, whereas crude fiber content, proportion of concentrate in the diet, and forage type significantly affected CP concentration in feces; however, the magnitude of these effects was less than 2 percentage units, and the direction of the effect of proportion of concentrate in the diet was not uniform. The curvilinear relationship between fecal CP concentration (observed range, 100 to 300 g/kg of OM) and diet OM digestibility (observed range = 57 to 80%) may be used to estimate diet OM digestibility, particularly for field trials, as it requires no feed samples and does not physically restrict the animal.     

近红外光谱分析技术在黑麦草粉粗蛋白测定中的应用

选取河北省吴桥试区不同品种、不同熟期、不同地块的黑麦草样品65份 ,用凯氏定N法进行了常规粗蛋白含量的测定。从中选出30个粗蛋白含量不同的样品作为近红外漫反射技术(NIR技术)测定的定标样品集。结果指出 :用NIR法得到的预测值与用凯氏定N法得到的测定值间的复相关系数达到R2=0.99 ,定标标样的标准误均方RMSEC=0.34 % ;用21个样品作检测样品集 ,凯氏定N法得到的测定值与NIR预测值间的复相关系数为R2=0.98 ,检测集样品预测标准误均方RMSEP=0.42 %。这一结果表明NIR作为一种黑麦草粉粗蛋白快速分析的技术是可行的     

Prediction of Forage Energy Content by Near Infrared Reflectance Spectroscopy and Summative Equations

In livestock nutrition, summative models (SM) are displacing empirical models as a preferred method to predict energy content of forages. The objective of this study was to determine the effect of using near infrared reflectance spectroscopy (NIRS) to determine nutrient subcomponents required of SM on overall ability to predict energy content of corn and legume-grass silages. Corn (n = 90) and legume-grass (n = 70) silages were collected and analyzed for CP, ADF CP, NDF, NDF CP, in vitro (IV) digestible (d) NDF, ash, and fat by standard laboratory techniques. Samples were scanned on a Model 6500 NIRS, and calibration equations were developed for each nutrient. The TDN contents of corn and legume-grass silages were then estimated using a SM, where the model nutrients were determined by laboratory or NIRS methods. The predicted TDN content of corn and legume-grass silages was compared to IV d OM to assess overall utility. The NIRS calibrations were adequate (R² > 0.90) for CP and NDF for both corn and legume-grass silages with standard errors of calibration (SEC) < 0.55 for CP and < 1.09 for NDF. Near infrared calibrations for ADF CP, NDF CP, fat, and ash were less accurate in both corn and legume-grass silages with R² < 0.75. Calibrations for IV d NDF in corn and legume-grass silages had R² = 0.87 and 0.79, respectively, but possible co-dependency with NDF is speculated. The relationship between corn and legume-grass silage SM TDN and IV d OM was excellent when model nutrients were determined by laboratory procedures. The TDN estimates when NIRS was used to determine all SM nutrients were superior to older empirical models, but SM TDN estimates using NIRS-determined nutrients were less accurate as compared with SM TDN prediction when model nutrients were determined by laboratory procedures. In particular, using NIRS to predict IV d NDF and ash for use in SM lead to the greatest challenge in TDN prediction in both corn and legume-grass silages.     

近红外光谱法测定DDGS中粗蛋白含量的试验研究

试验建立DDGS粗蛋白含量测定的近红外光谱分析定标模型。采用化学分析法测定72个DDGS样品中的粗蛋白含量,利用FOSS InfraXact型近红外光谱分析仪采集样品光谱,光谱经2,4,4,1导数和标准正常化+散射处理(SNV+Detrend),用改进最小二乘法(MPLS)回归,获得了较好的定标模型,校正决定系数(RSQ)、交叉验证决定系数(1-VR)、校正标准误差(SEC)、交叉验证标准误差(SECV)分别为0.982 5、0.932 8、0.266 2、0.389 5。利用30个验证集的DDGS样品进行外部检验,预测值与真实值之间差异不显著(P>0.05)。结果表明,定标模型的预测性能较好,可以替代化学分析法快速测定DDGS中的粗蛋白含量。     

Comparison of in vitro and in situ methods in evaluation of forage digestibility in ruminants

The objective of this study was to compare the application of different in vitro and in situ methods in empirical and mechanistic predictions of in vivo OM digestibility (OMD) and their associations to near-infrared reflectance spectroscopy spectra for a variety of forages. Apparent in vivo OMD of silages made from alfalfa (n = 2), corn (n = 9), corn stover (n = 2), grass (n = 11), whole crops of wheat and barley (n = 8) and red clover (n = 7), and fresh alfalfa (n = 1), grass hays (n = 5), and wheat straws (n = 5) had previously been determined in sheep. Concentrations of indigestible NDF (iNDF) in all forage samples were determined by a 288-h ruminal in situ incubation. Gas production of isolated forage NDF was measured by in vitro incubations for 72 h. In vitro pepsin-cellulase OM solubility (OMS) of the forages was determined by a 2-step gravimetric digestion method. Samples were also subjected to a 2-step determination of in vitro OMD based on buffered rumen fluid and pepsin. Further, rumen fluid digestible OM was determined from a single 96-h incubation at 38°C. Digestibility of OM from the in situ and the in vitro incubations was calculated according to published empirical equations, which were either forage specific or general (1 equation for all forages) within method. Indigestible NDF was also used in a mechanistic model to predict OMD. Predictions of OMD were evaluated by residual analysis using the GLM procedure in SAS. In vitro OMS in a general prediction equation of OMD did not display a significant forage-type effect on the residuals (observed - predicted OMD; P = 0.10). Predictions of OMD within forage types were consistent between iNDF and the 2-step in vitro method based on rumen fluid. Root mean square error of OMD was least (0.032) when the prediction was based on a general forage equation of OMS. However, regenerating a simple regression for iNDF by omitting alfalfa and wheat straw reduced the root mean square error of OMD to 0.025. Indigestible NDF in a general forage equation predicted OMD without any bias (P ≥ 0.16), and root mean square error of prediction was smallest among all methods when alfalfa and wheat straw samples were excluded. Our study suggests that compared with the in vitro laboratory methods, iNDF used in forage-specific equations will improve overall predictions of forage in vivo OMD. The in vitro and in situ methods performed equally well in calibrations of iNDF or OMD by near-infrared reflectance spectroscopy.     

Near infrared spectroscopy for biomonitoring: cow milk composition measurement in a spectral region from 1,100 to 2,400 nanometers

The potential of near infrared spectroscopy (NIRS; 1,100 to 2,400 nm) to measure fat, total protein, and lactose content of nonhomogenized milk during milking and the influence of individual characteristics of each cow's milk on the accuracy of determination were studied. Milk fractions were taken during milking, twice per month, for 6 mo. Samples were taken every 2nd and 4th wk at the morning and the evening milkings. Teatcups were removed at each 3 L of milk yield as determined with a fractional sampling milk meter. A total of 260 milk samples were collected and analyzed with an NIRSystem 6500 spectrophotometer with 1-mm sample thickness. Partial least squares (PLS) regression was used to develop calibration models for the examined milk components. The comparison with the reference method was based on standard error of cross validation (SECV). The obtained SECV varied from .107 to .138% for fat content, from .092 to .125% for total protein, and from .066 to .096% for lactose content, and the accuracy of the reference method (AOAC, 1990, method No 972.16) was .05% for all measured milk components. The obtained models had lower SECV when an individual cow's spectral data were used for calibration. The reduction of SECV for each cow's individual calibration, when compared with SECV for the set of all samples, differed with the different constituents. For fat content determination, the reduction reached 22.46%, for protein 26.40%, and for lactose 31.25%. This phenomena was investigated and explained by principle component analysis (PCA) and by comparing loading of PLS factors that account for the most spectral variations for each cow and the measured milk components, respectively. The results of this study indicated that NIRS (1,100 to 2,500 nm, 1-mm sample thickness) was satisfactory for nonhomogenized milk compositional analysis of milk fractions taken in the process of milking.     

Prediction of nutritive values in grass silages: I. Nutrient digestibility and energy concentrations using nutrient compositions and fermentation characteristics

Grass silages (n = 136) were selected from commercial farms across Northern Ireland according to their pH, ammonia nitrogen, DM, and predicted ME concentration. Each silage was offered to four sheep as a sole feed at maintenance feeding level to determine nutrient digestibility and urinary energy output. Dry matter concentration was determined as alcohol-corrected toluene DM and was subsequently used as the basis for all nutrient concentrations. The objectives were to use these data to examine relationships between nutritive value and nutrient concentration or fermentation characteristics in silages and then develop prediction equations for silage nutritive values using stepwise multiple regression techniques. The silages had a large range in quality (DM = 15.5 to 41.3%, ME = 7.7 to 12.9 MJ/kg of DM, pH = 3.5 to 5.5) and a relatively even distribution over the range. There was a positive relationship (P < 0.001) between silage GE and DE or ME concentration. Digestible OM in total DM (DOMD); ME/GE; and digestibility of DM, OM, and GE were positively related (P < 0.05) to CP, soluble CP, ether extract, lactic acid concentration, and lactic acid/ total VFA, whereas they were negatively related (P < 0.05) to ADF, NDF, lignin, individual VFA concentration, pH, and ammonia N/total N. Concentrations of DE and ME and digestibility of CP and NDF had similar relationships with those variables, although some relationships were not significant. Three sets of multiple prediction equations for DE and ME concentration; ME/ GE; DOMD; and digestibility of DM, OM, GE, CP, and NDF were therefore developed using three sets of predictors. The first set included GE, CP, soluble N/total N, DM, ash, NDF, lignin, lactic acid/total VFA, and ammonia N/total N; the second set excluded soluble N/ total N and lignin because they are not typically measured; the third set further excluded the fermentation data. The R2 values generally decreased with exclusion of predictors. The second and third sets of equations, except for NDF digestibility, were validated using the mean-square-prediction-error model and an independent grass silage data set published since 1977 (n = 17 [DM digestibility] to 28 [DOMD and OM digestibility]). The validation indicated that the equations developed in the present experiment could accurately predict DE and ME concentrations and DE/GE and ME/GE in grass silages.     

Cow‐specific diet digestibility predictions based on near‐infrared reflectance spectroscopy scans of faecal samples

This study was designed to obtain information on prediction of diet digestibility from near‐infrared reflectance spectroscopy (NIRS) scans of faecal spot samples from dairy cows at different stages of lactation and to develop a faecal sampling protocol. NIRS was used to predict diet organic matter digestibility (OMD) and indigestible neutral detergent fibre content (iNDF) from faecal samples, and dry matter digestibility (DMD) using iNDF in feed and faecal samples as an internal marker. Acid‐insoluble ash (AIA) as an internal digestibility marker was used as a reference method to evaluate the reliability of NIRS predictions. Feed and composite faecal samples were collected from 44 cows at approximately 50, 150 and 250 days in milk (DIM). The estimated standard deviation for cow‐specific organic matter digestibility analysed by AIA was 12.3 g/kg, which is small considering that the average was 724 g/kg. The phenotypic correlation between direct faecal OMD prediction by NIRS and OMD by AIA over the lactation was 0.51. The low repeatability and small variability estimates for direct OMD predictions by NIRS were not accurate enough to quantify small differences in OMD between cows. In contrast to OMD, the repeatability estimates for DMD by iNDF and especially for direct faecal iNDF predictions were 0.32 and 0.46, respectively, indicating that developing of NIRS predictions for cow‐specific digestibility is possible. A data subset of 20 cows with daily individual faecal samples was used to develop an on‐farm sampling protocol. Based on the assessment of correlations between individual sample combinations and composite samples as well as repeatability estimates for individual sample combinations, we found that collecting up to three individual samples yields a representative composite sample. Collection of samples from all the cows of a herd every third month might be a good choice, because it would yield a better accuracy.     

检测燕麦干草主要营养成分含量的近红外光谱分析模型

为探索NIRS技术在测定燕麦(Avena sative)干草品质上的应用,试验于2020—2021年收集了249份不同品种、年限和生长时期的燕麦干草,通过WinISI III定标软件建立燕麦干草主要营养成分的近红外光谱模型。结果显示：粗蛋白(CP)、中性洗涤纤维(NDF)和粗脂肪(EE)预测模型的定标系数(RSQ)和外部验证决定系数(RSQv)均在0.83以上,校正标准误(SEC)、交叉验证误差(SECV)和预测标准误差(RMSEP)均小于0.02,相对标准误差(RPD)均大于3,预测值逼近化学分析的精度具有良好的预测效果。酸性洗涤纤维含量(ADF)建模效果较差,定标系数和外部验证决定系数分别为0.83和0.84,校正标准误(SEC)、交叉验证误差(SECV)和预测标准误差(RMSEP)均小于0.01,接近化学分析精度,且RPD大于2.50。因此,所建ADF模型也可用于近红外预测。     

近红外光谱法预测紫花苜蓿草捆的营养品质和消化率

优质牧草苜蓿(Medicago sativa)品质的优劣和消化率的高低能在很大程度上影响畜牧业的发展。为探讨近红外光谱技术(NIRS)预测苜蓿草捆中营养成分和消化率的可行性,本试验采集来自我国苜蓿主产区的苜蓿草捆样品229份,利用改进的偏最小二乘法(MPLS),结合不同光谱处理和数学参数设置,建立苜蓿营养品质和消化率的近红外预测模型。结果表明:相对饲喂价值(relative feed value,RFV),中性洗涤纤维(neutral detergent fiber,NDF),酸性洗涤纤维(acid detergent fiber,ADF)和粗蛋白(crude protein,CP)的模型能用于实际含量的分析;半纤维素(Hemicellulose)和干物质体外消化率(In vitro dry matter disappearance,IVDMD)的交叉验证相对分析误差(relative prediction deviation for cross validation,RPDCV)值介于2.5~3之间,能够用于粗略分析,需要对定标集样品进一步扩充和完善以提高预测的准确度。试验初步建立了苜蓿草捆品质的定量分析模型,补充了我国苜蓿草捆营养品质数据库,为苜蓿草产品的生产、流通及动物饲料配方的制定提供了数据支持。     

Estimating the digestibility of Sahelian roughages from faecal crude protein concentration of cattle and small ruminants

Studies on diet selection and feed intake of ruminants in extensive grazing systems often require the use of simple approaches to determine the organic matter digestibility (OMD) of the ingested feed. Therefore, we evaluated the validity of the one-factorial exponential regression established by Lukas et al. [Journal of Animal Science 83 (2005) 1332], which estimates OMD from the faecal crude protein (FCP) concentration. The equation was applied to two sets of data obtained with free grazing and pen-fed cattle, sheep and goats ingesting low and high amounts of green and dry vegetation of Sahelian pastures as well as millet leaves and cowpea hay. Data analysis showed that the livestock species did not influence the precision of estimation of OMD from FCP. For the linear regression between measured and estimated OMD (%) across n = 431 individual observations, a regression coefficient of r2 = 0.65 and a residual standard deviation (RSD) of 5.87 were obtained. The precision of estimation was influenced by the data set (p = 0.033), the type of feed (p < 0.001) and the feeding level (p = 0.009), and interactions occurred between type of feed and feeding level (p = 0.021). Adjusting the intercept and the slope of the established exponential function to the present data resulted in a compression of the curve; while r2 remained unchanged, the RSD of the regression between measured and estimated OMD was reduced, when compared with the results obtained from the equation of Lukas et al. (2005). Estimating OMD from treatment means of FCP greatly improved the correlation between measured and estimated OMD for both the established function and the newly fit equation. However, if anti-nutritional dietary factors increase the concentration of faecal nitrogen from feed or endogenous origin, the approach might considerably overestimate diet digestibility.     

Technical note: using an automated head chamber system to administer an external marker to estimate fecal output by grazing beef cattle

The objective of this experiment was to determine if titanium dioxide (TiO₂) dosed through an automated head chamber system (GreenFeed; C-Lock Inc., Rapid City, SD, USA) is an acceptable method to measure fecal output. The GreenFeed used on this experiment had a 2-hopper bait dispensing system, where hopper 1 contained alfalfa pellets marked with 1% titanium dioxide (TiO₂) and hopper 2 contained unmarked alfalfa pellets. Eleven heifers (BW = 394 ± 18.7 kg) grazing a common pasture were stratified by BW and then randomized to either 1) dosed with TiO₂-marked pellets by hand feeding (HFD; n = 6) or 2) dosed with TiO₂-marked pellets by the GreenFeed (GFFD; n = 5) for 19 d. During the morning (0800), all heifers were offered a pelleted, high-CP supplement at 0.25% of BW in individual feeding stanchions. The HFD heifers also received 32 g of TiO₂-marked pellets at morning feeding, whereas the GFFD heifers received 32 g of unmarked pellets. The GFFD heifers received a single aliquot (32 ± 1.6 g; mean ± SD) of marked pellets at their first visit to the GreenFeed each day with all subsequent 32-g aliquots providing unmarked pellets; HFD heifers received only unmarked pellets. Starting on d 15, fecal samples were collected via rectal grab at feeding and every 12 h for 5 d. A two-one sided t-test method was used to determine agreement and it was determined that the fecal output estimates by HFD and GFFD methods were similar (P = 0.04). There was a difference (P < 0.01; Bartlett’s test for homogenous variances) in variability between the dosing methods for HFD and GFFD (SD = 0.1 and 0.7, respectively). This difference in fecal output variability may have been due to variability of dosing times-of-day for the GFFD heifers (0615 ± 6.2 h) relative to the constant dosing time-of-day for HFD and constant 0800 and 2000 sampling times-of-day for all animals. This research has highlighted the potential for dosing cattle with an external marker through a GreenFeed configured with two (or more) feed hoppers because estimated fecal output means were similar; however, consideration of the increased variability of the fecal output estimates is needed for future experimental designs.     

The use of near infra‐red reflectance spectroscopy for the determination of plant nitrogen

Abstract

A Near Infra‐Red Reflectance Spectrophotometer was calibrated to analyse Italian ryegrass for protein nitrogen (N). Rye‐grass samples having a wide range in N content were analysed by standard “wet” chemistry techniques and the resulting data used to calibrate the Near Infra‐Red Spectrophotometer for ryegrass N analysis. A correlation (r) of 0,99 and standard error of calibration (SEC) of 0,209 resulted from the initial regression analysis between the Near Infra‐Red Spectroscopy (NIRS) estimated and “wet” chemistry data. In order to further evaluate the accuracy of the NIRS calibration a separate set of ryegrass samples were analysed for N content, by both the “wet” chemistry and NIRS methods, resulting in a correlation (r) of 0,98 and standard error of prediction (SEP) of 0,235. The applicability of the NIRS ryegrass calibration to other species was briefly examined by estimating the N contents of kikuyu (Pennisetum clandestinum) (r = 0,97 and SEP = 0,277).     

Fecal Near-Infrared Reflectance Spectroscopy to Predict Leymus chinensis of Diets From Penned Sheep in North China

Selective foraging among free-ranging herbivores can make measuring botanical composition of diets challenging. Using near-infrared reflectance spectroscopy (NIRS) on feces for predicting botanical components of individual animal diets is a novel method for studying diet selection. This study was conducted to determine the ability of fecal NIRS to predict the percentage of consumption of Leymus chinensis (Trin.) Tzvel., a dominant species in north China, by sheep (Ovis aries L.). The calibration data set consisted of 47 diets of known L. chinensis composition, paired with corresponding fecal spectra. These pairs were generated in a trial using restricted feeding. Validation pairs (n = 9) were collected in a similar trial that used ad libitum feeding. Derived coefficients of determination (R²) and standard error of calibration were 0.99% and 2.2% for partial least squares (PLS) regression and 0.89% and 7.3% for stepwise regression, respectively. Derived coefficients of determination (r²) and standard error of prediction were 0.78% and 4.8% for PLS regression and 0.90% and 3.2% for stepwise regression, respectively. PLS regression resulted in better calibration than stepwise regression, but when the calibration data set was small, stepwise regression improved the precision and accuracy of predictions compared with the PLS regression. Results of the present study show that a fecal NIRS equation developed from a restricted feeding trial could be used to predict the percentage of L. chinensis in fecal materials collected from voluntary feeding trials.     

利用近红外光谱技术快速分析全株玉米青贮营养成分

研究旨在利用近红外光谱技术(near infrared reflectance spectroscopy,NIRS)建立全株玉米青贮6种营养成分的近红外预测模型,为生产实践中合理利用全株玉米青贮饲料资源提供理论依据.选取玉米青贮样品64份作为定标集,16份作为验证集.利用NIRS结合改良偏最小二乘法(modified ...