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

J. Anim. Breed. Genet. ISSN 0931-2668

ORIGINAL ARTICLE

Cow-specific diet digestibility predictions based on near-infrared reflectance spectroscopy scans of faecal samples € 1, M. Rinne1, L. Nyholm2, P. Ma €ntysaari1, A. Sairanen3, E.A. Ma €ntysaari1, T. Pitka €nen1 T. Mehtio 1 & M.H. Lidauer 1 Natural Resources Institute Finland (Luke), Green Technology, Jokioinen, Finland 2 Valio Ltd., Farm Services, Valio, Finland 3 Natural Resources Institute Finland (Luke), Green Technology, Maaninka, Finland

Summary

Keywords Dairy cow; digestibility; near-infrared reflectance spectroscopy; repeatability. Correspondence €, Natural Resources Institute Finland T. Mehtio (Luke), Green Technology, Biometrical Genetics, Myllytie 1, FI-31600 Jokioinen, Finland. Tel: + 358 50 383 5995; E-mail: [email protected] Received: 1 April 2015; accepted: 11 August 2015

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 onfarm 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.

Introduction Many different definitions of dairy cow feed efficiency are suggested in literature indicating that feed efficiency in dairy cows is complex and cannot be measured by one trait only. Considering lactating cows only, in general feed efficiency can be defined as a © 2015 Blackwell Verlag GmbH

• J. Anim. Breed. Genet. 133 (2016) 115–125

ratio of energy intake over energy excreted in milk. Gross feed efficiency depends on production level but also on the efficiency with which the nutrients are digested and metabolized to be used for maintenance, growth and milk production. Hence, feed efficiency is influenced by diet, environmental factors, genetic capacity and physiological state of the cow. Up to doi:10.1111/jbg.12183

€ et al. T. Mehtio

Cow-specific digestibility based on NIRS

now, the selection for feed efficiency traits in dairy cows has been indirect due to the difficulties in trait definition and costs associated with measuring feed efficiency in large populations. Vallimont et al. (2011) found a strong genetic correlation between milk yield and feed efficiency, and thus, selection for higher milk yield has indirectly improved feed efficiency. However, at current production levels, further selection for milk yield would have marginal benefits on feed efficiency. Diet digestibility is one factor contributing to the feed efficiency of a cow. The effects of feeding level and diet composition on digestibility are well identified (Huhtanen et al. 2009; Nousiainen et al. 2009), but there are gaps in knowledge on the variability of digestibility within lactation and across cows fed the same diet. Previously, it was presented that little variation exists among cows in their ability to digest a given diet, particularly when intakes are standardized (Korver 1988; Veerkamp & Emmans 1995). More recent studies, however, indicate differences in digestibility among cows (Berry et al. 2007) and between breeds (Beecher et al. 2014). If genetic variation between cows exists and a practical method to measure cow-specific digestibility on-farm is developed, then selection for diet digestibility and feed efficiency in dairy cows could be initiated. Diet digestibility is defined as a proportion of dry matter intake that is digested and not excreted in faeces. It can be determined by the total faecal collection or from feed and faeces using markers, for example, acid-insoluble ash (AIA) (Van Keulen & Young 1977). However, these methods are too expensive and timeconsuming to be used routinely on-farms. Near-infrared reflectance spectroscopy (NIRS) has been shown to be a potential method applied to faeces for assessing diet digestibility of sheep and cattle (Decruyenaere et al. 2009; Fanchone et al. 2009; Nyholm et al. 2009). On the other hand, faecal NIRS has been criticized because the prediction is based on NIRS spectra from faeces, and faeces only consist of undigested diet residues, microbes and endogenous components (Boval et al. 2004). But, because NIRS quantifies all major chemical components, and hence takes into account more indicators of digestibility, it is regarded superior to other methods (Fanchone et al. 2009). To predict the digestibility by NIRS, appropriate calibration equations are required. The difficulty in developing calibration equations is to collect a reference database that is as representative as possible for a given field data diversity. Thus, better calibration statistics are often obtained by increasing the size of reference data (Decruyenaere et al. 2012). Williams 116

(2004) concluded that the coefficient of determination should be higher than 0.8 and the standard error of cross-validation should be close to the standard error of calibration to ensure adequate reliability of the predictions. The quality of the reference measurements is also crucial to avoid a systematic bias in predictions as a systematic error in the level of different sets of reference measurement samples will obscure the calibration. To make the on-farm measurements practically feasible, an optimal protocol for predicting diet digestibility by NIRS scans should be based on as few faecal samples as possible. Therefore, there is a need to study how to collect the most representative samples and address which time periods of the lactation the samples should be collected. The objective of this study was to test a method to predict digestibility by NIRS, to quantify the variability in diet digestibility across cows, and to obtain key parameters for assessing a possible on-farm measurement protocol for collection of faecal samples. In particular, we studied three digestibility predictors, diet dry matter digestibility (DMD), diet organic matter digestibility (OMD) and indigestible neutral detergent fibre (iNDF) concentration in faeces.

Material and methods Diet digestibility traits

The digestibility traits in this study were apparent total tract DMD and OMD, and iNDF concentration in faeces (iNDFfaeces). NIRS scans from faecal samples were used to give direct predictions of cow-specific OMD (OMDfaeces) and iNDFfaeces. The same samples were also assessed by the AIA method to obtain reference measurements for DMD (DMDAIA) and OMD (OMDAIA). The iNDF concentration in the feed (iNDFfeed) was also predicted by NIRS, so it was possible to use iNDF as internal marker for calculating DMD (DMDiNDF). DMDiNDF was calculated iNDFfeed . Moreover, using formula: DMDiNDF ¼ 1 iNDF faeces iNDFfaeces, alone may serve as an indicator trait for DMD, given that the cows in the same contemporary groups have consumed the same diet. If so, ranking of cows by DMDiNDF or by iNDFfaeces should be the same. Design of the animal trial

The trial was designed to quantify cow-specific diet digestibility at three different stages of lactation. Both methods, AIA and NIRS, required the collection of © 2015 Blackwell Verlag GmbH




faecal samples from individual cows. Faecal samples were taken in early lactation (around 50 days in milk, DIM), in mid-lactation (around 150 DIM) and in late lactation (around 250 DIM). By this sampling protocol, it was possible to assess the variability of digestibility within lactation and across cows. The trial included 31 Holstein (HOL) cows of which 13 primiparous and 13 Nordic Red Dairy Cattle (RDC) cows of which two primiparous. The trial was conducted at Luke (former MTT Agrifood Research Finland) Maaninka research farm during 2012–2013. Cows entered the experiment 21 day prior to calving. The calving dates varied between March 6 and September 9 in 2012. After calving, the cows were subjected to either low-concentrate (LC) or high-concentrate (HC) diet. Concentrate proportion in the diet was on average 22% and 49% in LC diet and HC diet, respectively. However, concentrate proportions were altered to keep metabolizable energy (ME) level in diet at 11.5 MJ ME/kg DM and 11.9. MJ ME/kg DM for LC diet and HC diet, respectively. Feed was based on a first cut timothy meadow fescue grass silage supplemented with variable amounts of barley grain and rapeseed meal, and was fed as TMR. Collection and treatment of samples Faecal samples

All faecal samples were collected directly from the rectum of the cows. Samples were collected for five consecutive days every morning and every evening during each sampling week. All 44 cows’ composite samples were obtained by aggregating 100 g of faeces from each sampling time (morning and evening) over the whole 5-day collection period. These ten samples formed one composite sample (one from every lactation stage) resulting in three observations for each cow. An individual sample data from a subset of 20 cows with daily individual morning and evening faecal samples were also retained. These samples were used to estimate between individual samples variation and the repeatability of digestibility predictions across lactation which were used to assess an optimal faeces sampling protocol. These 20 cows comprised of 13 HOL (four primiparous) and seven RDC multiparous cows. Thus, each cow had ten observations from every lactation stage resulting in 30 observations per cow. Feed samples

Feed samples were collected every morning for 5 days so that the first sample was collected 1 day before the © 2015 Blackwell Verlag GmbH


faecal sampling. The weekly feed samples were combined to represent feed given during the faecal sampling period. Preparation and analyses of samples

All feed and faecal samples were frozen at 20°C immediately after sampling. The samples were dried at 60 °C and milled using a cutting mill (Sakomylly KT-3100; Koneteollisuus Oy, Helsinki, Finland) equipped with a 1-mm sieve. The samples were stabilized to air humidity before and after milling. Feed samples and composite faecal samples were also analysed for ash content after ignition in a muffle furnace at 600 °C for 2 h (AOAC, 1990; method 942.05), and for AIA content (Van Keulen & Young 1977) Feed samples were also analysed for CP (6.25 9 N) by the Dumas method (AOAC method 968.06) using a Leco FP 428 nitrogen analyser, for NDF (Van Soest et al. 1991; using sodium-sulphite, without amylase for forages and presented ash free) and for in vitro pepsin cellulase solubility (Huhtanen et al. 2006). NIRS scans

All feed and faecal samples were analysed by NIRS at Valio Ltd. laboratory by scanning duplicate cyvettes for each sample between 400 and 2500 nm in 2-nm increments using a FOSS NIRSystems 6500 spectrometer (Foss Electric A/S, Hillerød, Denmark). Applied NIRS prediction equations for OMDfaeces and iNDFfaeces were developed from a reference data including 234 and 240 samples collected in earlier trials, respectively. Digestibility was determined from these samples either by total faecal collection or by AIA method as part of previous Finnish feeding experiments. The iNDF concentration in reference samples was determined using ruminal incubation for 12 days in small pore size nylon bags using samples ground through a 1-mm screen (Krizsan et al. 2015). The NIRS calibrations were developed with WINISITM 4 software (FOSS Analytical A/S, Hillerød, Denmark) using spectral data between 1100 and 2498 nm. The calibrations were calculated with modified partial least squares regression method (MPLS). Mathematical treatments of the first ‘1,4,4,1’ or second ‘ 2,6,4,1’ derivatives (in which the values represent the number of the derivative, the gap over which the derivative is calculated, the number of points in moving average and the number of nm over which the second smoothing is applied, respectively) were used to enhance the spectral differences. Standard normal variate and detrend (SNV&D) was used as a scatter correction method, 117



and calibration equations were calculated with two outlier elimination passes to remove outlier samples. Calibration accuracy was evaluated by cross-validation by splitting the data set into four subsets. The best NIRS calibration models were selected based on standard error of calibration (SEC), standard error of cross-validation (SECV), the coefficient of determination (R2), cross-validated coefficient of determination (1-VR), and the standard deviation (SD) divided by SECV (SD/SECV). To reduce prediction error, two parallel samples were prepared and scanned from each grinded faecal sample and the average of the predictions from the parallel samples was taken as an observation. For feed samples, NIRS calibration for silage at Valio Ltd. was used. D-value (digestible organic matter in DM) and iNDF predictions of feed samples were based on a silage calibration consisting of grass silages, legume silages (including pure red clover and lucerne silages, and legume and grass silage mixtures), wholecrop silages, hay and haylage samples giving a total of 1566 and 448 observations for D-value and iNDF, respectively. Calculations and statistical analyses

After raw data edits to remove outliers (AIA concentration >0.2), there were 101 observations in the combined AIA and NIRS data for composite samples, and 567 observations in the NIRS data for individual samples. For analyses, the following model of fixed effects and their interactions were considered: breed, parity, lactation stage, concentrate proportion, time of collection (morning or evening sample), day of collection and week of collection (calendar week of faecal sampling). The significances of the fixed effects were determined by F-tests, and non-significant effects were removed. Only final models are reported. Comparison of methods

The assessment of the reliability of NIRS predictions and comparison between different methods was performed by comparing the means and standard deviations and exploring correlations between OMDAIA, DMDAIA, DMDiNDF, OMDfaeces and iNDFfaeces from composite samples from all 44 cows at three lactation stages. To calculate phenotypic correlations between digestibility measures, the residuals from fitting a linear model were used. The applied model removed environmental variation due to lactation stage and concentrate proportion 9 week of collection. The model can be described as: 118

Yipws ¼ ls þ cwpw þ eipws ; where i is animal, ls and cwpw are fixed effects of lactation stage s and interaction between concentrate proportion p and week of collection w, respectively, and εipws is an error term. Correlation between observations from different lactation stages was modelled using unstructured covariance structure for error term, hence 2 3 2 2 3 eipw1 r1 r12 r13 var4 eipw2 5 ¼ 4 r12 r22 r23 5; eipw3 r13 r23 r23 Phenotypic correlations were also calculated between different stages of lactation to assess in which lactation stage the collection of samples would be most reasonable, and to compare the results from different digestibility measures. Phenotypic variation and repeatability across lactation were estimated to obtain information about whether the investigated predictors for cow-specific diet digestibility are reliable enough, and whether there exists variation across cows that supports future research on genetic improvement of cow-specific diet digestibility. A linear mixed model was fitted on repeated observations for OMDAIA, DMDAIA, DMDiNDF, OMDfaeces and iNDFfaeces. The model can be described as: Yipws ¼ ls þ cw pw þ ai þ eipws ; where ls and cwpw are fixed effects of lactation stage s and interaction between concentrate proportion p and week of collection w, respectively, and ai and eipws are random animal effect for cow i and random error term with variances r2a and r2e , respectively. Development of a sampling protocol

To assess how many samples would be required to get a representative composite sample, we explored correlations between sample averages, based on different numbers of individual samples, and composite samples for DMDiNDF and iNDFfaeces. The 20-cow subset data were used, and because averages of individual samples were used as observations, there were only 60 compiled observations within the analysis (three observations per cow). Depending on which day or days of the week were used in the analyses, the results varied considerably. Hence, it was decided to, whenever possible, do repeated analyses with independent sets of observations and average the obtained correlations. This was the case when analyses were made with individual samples or averages from samples of 2 days. For calculating correlations for 2-day © 2015 Blackwell Verlag GmbH




averages, Monday+Tuesday sample and Thursday+Friday sample averages were formed; for 3-day averages, Monday to Wednesday samples were used and for 4-day averages, Monday to Thursday samples were used. The covariances between individual cow samples were utilized to study the effect of using averages of samples on the repeatability of DMDiNDF and iNDFfaeces across lactation stages. Therefore, two 15 9 15 variance–covariance matrices were constructed for the individual samples collected from each cow. Two different types of individual samples were considered as follows: morning samples only or day samples, which were the averages of morning and evening samples. Each cow had for each type of sample 15 observations; five samples from each of the three sampling periods. The applied linear mixed model can be described as: Yipwsd ¼ ls þ cw pw þ ai þ alis þ eipwsd ; where d is day of collection within lactation stage, ls and cwpw are fixed effects of lactation stage s and interaction between concentrate proportion p and week of collection w. Random effects ai and alis are animal and animal–lactation stage interaction for cow i and lactation stage s with variance r2a and r2as , respectively, and eipwsd is an error term with variance r2e . Each constructed matrix had the form: 2 2 3 r2a J5 r2a J5 ðra þ r2as ÞJ5 4 5 þ r2 I15 ; r2a J5 ðr2a þ r2as ÞJ5 r2a J5 e 2 2 2 2 ra J5 ra J5 ðra þ ras ÞJ5 where J5 is a 5 9 5 matrix of ones and I15 is an 15 9 15 identity matrix. The matrices were used to assess repeatabilities of observations (either morning samples or day samples) that are based either on a single daily sample or on a 2-day-, 3-day-, 4-day- and 5-day-average. All statistical analyses were performed using the VARCOMP and MIXED procedures (Littell et al. 1996) in SAS/STAT software version 9.3 (SAS Institute Inc., Cary, NC, USA).

Results Description of the data is shown by breed, concentrate proportion and lactation stage in Table 1. The average CP and NDF concentrations for the LC diet were 171 and 495 g/kg DM and for HC diet 161 and 448 g/kg DM, and the average D-value of the silage (determined by in vitro pepsin cellulase solubility) was 687 g/kg DM. Dry matter intake and milk yield were higher, and dry matter digestibility was slightly lower, when the concentrate proportion in feed was higher. There were hardly any differences between the two breeds. Based on the obtained model selection statistics, best-suitable NIRS prediction models were achieved with 236 and 221 samples using mathematical treatments of the second-order derivative (2,6,4,1) for iNDFfaeces and OMD, respectively. NIRS calibration models for iNDFfeed and D-value were created using mathematical treatments of the first-order derivative (1,4,4,1) and second-order derivative (2,4,4,1), respectively. NIRS estimations for iNDFfeed, D-value and iNDFfaeces were relatively good (Table 2). SECV for all predicted parameters were rather low; 14.9, 18.5, 16.8 and 17.7 for iNDFfeed, D-value, iNDFfaeces and OMD, respectively. The coefficient of determination (R2) was higher than cross-validation coefficient of determination (1-VR) for all calibration models. Cross-validation coefficient of determination (1-VR) was acceptably high for iNDFfeed, D-value and iNDFfaeces but low for OMD. Comparison of methods

Number of the observations, means, standard deviations and ranges for DMDAIA, DMDiNDF, iNDFfaeces, OMDAIA and OMDfaeces of the composite samples are summarized in Table 3. DMDiNDF was clearly on a lower level compared to other digestibility traits. Phenotypic and raw correlations between different digestibility measures are given in Table 4. Correlations from the raw data were high (0.98) between

Table 1 Description of the data by breed (Holstein, HOL; Nordic Red Dairy Cattle, RDC) and concentrate proportion (diet, %) based on dry matter intake (DMI, kg/day), milk yield (kg/day) and dry matter digestibility measured by AIA (DMDAIA, kg/day) in different stages of lactation DMI

Milk yield

DMDAIA

Breed

Diet

N

50

150

250

50

150

250

50

150

250

HOL HOL RDC RDC

22 49 22 49

16 15 6 7

19.4 20.4 20.2 22.3

20.2 22.4 20.1 23.7

18.4 21.7 18.2 21.2

33.7 36.1 33.6 37.6

28.1 34.0 28.5 31.8

25.1 29.7 22.2 27.3

707.8 708.5 712.0 709.8

745.1 713.3 730.4 719.1

737.5 720.7 732.0 728.6

© 2015 Blackwell Verlag GmbH


119



Table 2 Statistics of the best NIRS calibration models for forage D-value, forage and faecal indigestible NDF (iNDF; g/kg) and organic matter digestibility (OMD; g/kg) [standard error of calibration (SEC), standard error of cross-validation (SECV), the coefficient of determination (R2), cross-validated coefficient of determination (1-VR) and the standard deviation (SD) divided by SECV (SD/SECV)] based on data from earlier trials

iNDFfeed D-valuefeed iNDFfaeces OMD

N

SEC

SECV

R2

1-VR

SD/SECV

448 1566 236 221

13.8 17.6 15.3 17.0

14.9 18.5 16.8 17.7

0.874 0.866 0.849 0.690

0.852 0.853 0.818 0.663

2.6 2.6 2.3 1.7

DMDAIA, DMDiNDF OMDAIA, OMDfaeces DMDAIA, OMDAIA DMDiNDF, iNDFfaeces DMDAIA, iNDFfaeces iNDFfaeces, OMDfaeces

Table 3 Number of observations, means, standard deviations (SD) and range of dry matter digestibility (DMD, g/kg) and organic matter digestibility (OMD, g/kg) based on acid-insoluble ash (DMDAIA and OMDAIA), DMD based on indigestible neutral detergent fibre (iNDF) measured by NIRS as a marker (DMDiNDF), OMD based on NIRS (OMDfaeces) and iNDF concentration in faeces (g/kg DM) based on NIRS (iNDFfaeces) at different stages of lactation

DMDAIA All 50 DIM 150 DIM 250 DIM DMDiNDF All 50 DIM 150 DIM 250 DIM iNDFfaeces All 50 DIM 150 DIM 250 DIM OMDAIA All 50 DIM 150 DIM 250 DIM OMDfaeces All 50 DIM 150 DIM 250 DIM

N

Mean

SD

Min

Max

101 29 40 32

723 709 728 729

23.6 24.6 22.7 18.5

666 666 682 699

776 757 776 767

101 29 40 32

675 612 705 695

66.7 75.0 35.3 49.1

477 477 630 613

769 754 769 760

101 29 40 32

198 194 196 204

14.5 15.6 11.9 14.9

157 157 177 164

234 227 231 234

101 29 40 32

724 712 728 731

21.3 20.6 21.9 16.2

679 679 682 704

782 751 782 763

101 29 40 32

731 720 732 738

17.5 14.8 16.5 17.1

695 695 706 704

769 750 765 769

OMDAIA and DMDAIA as expected. Correlation between DMDAIA and DMDiNDF was 0.70, and correlation between OMDAIA and OMDfaeces was 0.57. Correlation between DMDiNDF and iNDFfaeces was 0.25. However, after modelling the data and calculating correlations using residuals, correlation between DMDAIA and DMDiNDF dropped to 0.45, whereas 120

Table 4 Phenotypic (rp) and raw correlations (ra) between dry matter digestibility (DMD, g/kg) and organic matter digestibility (OMD, g/kg) based on acid-insoluble ash (DMDAIA and OMDAIA), DMD based on indigestible neutral detergent fibre (iNDF) measured by NIRS as a marker (DMDiNDF), OMD based on NIRS (OMDfaeces) and iNDF concentration in faeces (g/kg DM) based on NIRS (iNDFfaeces) in lactation stages 50, 150 and 250 days in milk (DIM) and across lactation based on composite samples rp50DIM

rp150DIM

rp250DIM

rp

ra

0.44 0.30 0.98 0.97 0.35 0.06

0.50 0.62 0.98 0.99 0.50 0.13

0.43 0.58 0.98 0.99 0.36 0.01

0.45 0.51 0.98 0.97 0.41 0.04

0.70 0.57 0.97 0.25 0.32 0.04

rp is the correlation between residuals after modelling the digestibility traits. ra is the correlation between observations from the raw data.

Table 5 Phenotypic correlations between lactation stages in dry matter digestibility (DMD, g/kg) and organic matter digestibility (OMD, g/kg) based on acid-insoluble ash (DMDAIA and OMDAIA), DMD based on indigestible neutral detergent fibre (iNDF) measured by NIRS as a marker (DMDiNDF), OMD based on NIRS (OMDfaeces) and iNDF concentration in faeces (g/kg DM) based on NIRS (iNDFfaeces) based on composite samples DIM

OMDAIA

DMDAIA

DMDiNDF

iNDFfaeces

OMDfaeces

50, 150 150, 250 50, 250

0.52 0.77 0.55

0.53 0.80 0.60

0.40 0.52 0.09

0.48 0.52 0.11

0.28 0.26 0.05

correlation between DMDiNDF and iNDFfaeces increased to 0.97. Phenotypic correlations between different stages of lactation for DMDAIA, DMDiNDF, OMDAIA, OMDfaeces and iNDFfaeces are presented in Table 5. Correlations between lactation stages were moderate to high for DMDAIA and OMDAIA, moderate for DMDiNDF and iNDFfaeces, whereas correlations of OMDfaeces were weaker. Correlations were highest between 150 DIM and 250 DIM, which indicates that measurements in mid-lactation have higher repeatability. Correlations between early lactation and mid-lactation and early lactation and late lactation were weaker because the error variance was larger at early and late lactation. The estimates of phenotypic variability and repeatability for OMDAIA, DMDAIA, DMDiNDF, OMDfaeces and iNDFfaeces are presented in Table 6. The repeatability estimates for OMDAIA, DMDAIA and iNDFfaeces were in good agreement with the correlations of these traits between lactation stages (Table 5). The highest repeatability estimates of NIRS predicted digestibility traits, and also, the highest CVs of all traits were found © 2015 Blackwell Verlag GmbH




Table 6 Coefficient of variation (CV, %) and repeatability (r) of dry matter digestibility (DMD, g/kg) and organic matter digestibility (OMD, g/kg) based on acid-insoluble ash (DMDAIA and OMDAIA), DMD based on indigestible neutral detergent fibre (iNDF) measured by NIRS as a marker (DMDiNDF), OMD based on NIRS (OMDfaeces) and iNDF concentration in faeces (g/kg DM) based on NIRS (iNDFfaeces) across lactation Trait

OMDAIA

DMDAIA

DMDiNDF

iNDFfaeces

OMDfaeces

r2animal r2error r CV

151.9 80.5 0.65 1.70

154.2 70.8 0.69 1.72

61.0 131.5 0.32 1.18

30.8 35.7 0.46 2.78

25.9 88.2 0.23 0.70

in iNDFfaeces. Lowest animal variances, repeatability estimates and CVs were obtained for OMDfaeces. In general, variability across cows found in this study for different digestibility traits was small. Development of a sampling protocol

Number of the observations, means, standard deviations and ranges for DMDiNDF and iNDFfaeces of the individual samples of the 20-cow subset are summarized in Table 7. For DMDiNDF, the mean of measurements from individual samples for 50 DIM was lower, but otherwise the means in composite sample data and individual sample data were on the same level. There were no differences in the means of iNDFfaeces, but standard deviations were higher in individual sample data. Morning samples had slightly higher predictions of DMDiNDF and iNDFfaeces, but the differences can be considered practically negligible. The differences between standard deviations in morning and evening samples were also very small. Table 7 Number of observations, means, standard deviations and range of dry matter digestibility (DMDiNDF, g/kg) and iNDF in faeces (iNDFfaeces, g/kg) based on NIRS scans of the individual observations of 20 cows at different lactation stages DMDiNDF

All Morning Evening 50 DIM Morning Evening 150 DIM Morning Evening 250 DIM Morning Evening

iNDFfaeces

n

Mean

SD

Min

Max

Mean

SD

Min

Max

567 291 276

657 660 654

76.9 74.2 79.7

403 412 403

784 784 779

198 199 196

21.8 21.5 22.0

110 135 110

266 246 266

97 93

580 569

50.1 59.3

412 403

656 650

195 190

24.9 27.0

136 110

238 229

96 90

706 701

41.0 39.5

608 610

784 769

198 194

16.4 14.9

151 152

228 231

98 93

694 691

52.2 55.8

564 532

781 779

206 204

21.0 20.4

135 148

246 266



Correlations between composite samples and individual samples were highest at lactation stage DIM 50 for iNDFfaeces and at lactation stages DIM 150 and 250 for DMDiNDF (Table 8). DMDiNDF had lower correlations in early lactation, whereas iNDFfaeces had lower correlations in mid-lactation. In general, correlations became stronger when more individual samples were used in estimation of weekly means. Correlations were somewhat larger for weekly means based on day samples (including morning and evening) compared to weekly means of samples based on morning samples, but the differences were rather small. When more than three individual samples were compiled to weekly samples, the correlation to the composite sample increased only little. Evening samples yielded similar correlations, which indicate that timing of the sampling does not seem to be very important and can be chosen based on the most convenient practical arrangements. Variance components and repeatability estimates of different combinations of individual samples across lactation are summarized in Table 9. The error variances of DMDiNDF were clearly higher than error variances of iNDFfaeces, and this resulted in higher repeatability estimates for iNDFfaeces. The repeatability estimates for iNDFfaeces increased relatively more when the number of samples increased. Again, similarly as for correlations, the additional increase in repeatability was moderate when using more than three individual morning samples for building a composite sample. Discussion The variability estimated for the digestibility traits in this study was in good agreement with the literature (Berry et al. 2007). The estimated standard deviation for OMDAIA was 12.3 g/kg, which is small considering that average was 724 g/kg. However, the average for OMDAIA in lowest quartile was 707 g/kg and in highest quartile 748 g/kg. Thus, the average difference between cows from lowest quartile and highest quartile is 41 g/kg, which has apparent economical and environmental importance. Overall, digestibility was lower at early stage of lactation and increased towards end of lactation. In early lactation, the rapid increase in milk production increases feed intake which may cause a reduction in diet digestibility (Huhtanen et al. 2009). Correlations between mid- and late lactation were higher than correlations between 50 DIM and later lactation stages. This low correlation between early lactation with later lactation stages was due to a higher measurement 121



Table 8 Correlations between composite samples and mean of different combinations of individual samples for dry matter digestibility (DMDiNDF, g/kg) and iNDF in faeces (iNDFfaeces, g/kg) based on NIRS scans of the individual observations of 20 cows at different lactation stages DMDiNDF composite

Mean of different combinations of individual samples a

One day Two daysb Day 1, 2 and 3 Day 1, 2, 3 and 4 All 5 days One morninga Two morningsb Morning 1, 2 and 3 Morning 1, 2, 3 and 4 All five mornings All five evenings

iNDFfaeces composite

50 DIM

150 DIM

250 DIM

50 DIM

150 DIM

250 DIM

0.77 0.84 0.86 0.88 0.91 0.76 0.83 0.86 0.87 0.85 0.87

0.91 0.93 0.95 0.97 0.97 0.88 0.90 0.91 0.94 0.96 0.97

0.95 0.97 0.98 0.98 0.99 0.90 0.94 0.98 0.96 0.97 0.98

0.85 0.90 0.89 0.91 0.94 0.85 0.91 0.91 0.92 0.92 0.90

0.77 0.78 0.84 0.90 0.91 0.77 0.75 0.77 0.84 0.88 0.89

0.83 0.89 0.94 0.94 0.95 0.83 0.82 0.94 0.90 0.92 0.92

a

Average of correlations of separate Monday, Tuesday, Wednesday, Thursday and Friday samples. Average of Monday+Tuesday samples and Thursday+Friday samples.

b

Table 9 Repeatability estimates (r) of different combinations of individual samples across lactation for dry matter digestibility (DMDiNDF, g/kg) and iNDFfaeces (g/kg) based on NIRS scans of the individual observations of 20 cows iNDFfaeces

DMDiNDF

Mean of different sample combinations

r2animal

r2error

r

r2animal

r2error

r

1 day 2 days 3 days 4 days 5 days 1 morning 2 mornings 3 mornings 4 mornings 5 mornings 5 evenings

91.5 91.5 91.5 91.5 91.5 96.9 96.9 96.9 96.9 96.9 132.0

800.3 536.0 447.9 403.8 377.4 972.6 663.0 559.8 508.2 477.2 422.2

0.10 0.15 0.17 0.18 0.20 0.09 0.13 0.15 0.16 0.17 0.24

79.7 79.7 79.7 79.7 79.7 53.3 53.3 53.3 53.3 53.3 129.1

130.4 70.1 49.9 39.9 33.8 173.2 93.0 66.3 52.9 44.9 35.2

0.38 0.53 0.61 0.67 0.70 0.24 0.36 0.45 0.50 0.54 0.79

error in early lactation but may also indicate that digestibility at earlier stage of lactation is a different trait than digestibility in mid- and late lactation. The number of cows involved in this trail was too small to detect differences in digestibility between breeds or parities. Comparison of methods

The iNDF concentration in feed has proven to describe the biological characteristics of forages well (Huhtanen et al. 2006), and it can also be used as marker in digestibility studies (Lee & Hristov 2013). It is measured by incubating samples in nylon bags in the 122

rumen, but the method has been difficult to standardize between laboratories (Eriksson et al. 2012). It also appears that particularly faecal samples may be prone to particle loss during the incubation resulting in incomplete recovery (Krizsan et al. 2015) which could result in too low concentrations in the NIRS reference data used in this study. When studying differences between digestibility traits, we found that DMDiNDF was on a lower level compared to other traits. This may result from a bias in predicting dietary iNDF concentration or faecal iNDF concentration by NIRS or in both. The available NIRS reference data for predicting iNDF in feed samples were based on silage samples, whereas animals in this study were fed TMR. This may result some bias in iNDFfeed predictions. However, as all cows (within diet concentrate levels and collection dates) consumed the same TMR, a possible bias in diet iNDF concentration or faeces iNDF concentration should not affect on the comparisons of individual cow digestibility values, as the applied statistical models are capable to account for such a systematic bias. The raw data correlation between DMDiNDF and DMDAIA were reasonable high considering the relative low number of samples and that observations from both methods (AIA and NIRS) include a measurement error. However, correlations were only moderate after correcting for environmental effects, which can be explained by the relative large standard error of the measurements compared to the magnitude of the phenotypic standard deviation. Nevertheless, the same moderate correlation was obtained between iNDFfaeces and DMDAIA which indicates that © 2015 Blackwell Verlag GmbH




accuracy of iNDFfaeces is more crucial than accuracy of iNDFfeed. Also, the very high phenotypic correlation between DMDiNDF and iNDFfeaces shows that ranking animals for digestibility could be done by iNDFfaeces, which would not require feed sampling at all, but it requires that diet is properly taken into account in the statistical model. The low correlation between OMDAIA and OMDfaeces can be partly explained by the small NIRS calibration data set used for developing prediction equations for OMDfaeces. Decruyenaere et al. (2015) demonstrated the importance of precision of reference data to obtain reliable NIRS calibration equations. In their study, reference data sets for OMDfaeces were approximately four times larger and they obtained for the best prediction models R2 values of 0.9 and higher while the corresponding value in our study was 0.69 (Table 2). Moreover, some of the samples were clearly spectral outliers with Mahalanobis spectral distance (H) >3 from individual spectra to the population centroid (Shenk & Westerhaus 1991). Developing more accurate NIRS prediction equations will require a larger reference data set. For such a data set, it is crucial to cover all the variation in chemical and physical characteristics of the samples, which will be analysed by NIRS (Deaville & Flynn 2000). Different types of samples produce different NIRS spectra, and therefore, calibration data set must cover also smallest spectral variations to generate accurate and precise predictions. The low animal variances, repeatability estimates and CVs which we obtained for OMDfaeces indicate that developed NIRS calibration equations for predicting OMD directly from faeces were not yet accurate enough to quantify small differences in OMD between cows. The repeatability estimates for iNDFfeaces found in this study were relatively high, which indicates that iNDFfeaces may have potential to be used as an indicator trait for the cow’s ability to efficiently digest feed. This is supported by its high correlation with DMD. Moreover, the CV of iNDFfeaces was larger compared to all other measurements studied here, which suggests that future studies on genetic variability in digestibility traits should focus on iNDFfeaces measurements. Development of a sampling protocol

Developing of a sampling protocol was based on results for DMDiNDF and iNDFfaeces. OMDfaeces was not considered because it generally had low correlations with other traits, low variability and repeatability estimates and low correlations between lactation stages. © 2015 Blackwell Verlag GmbH


In a study by Mehti€ o et al. (2014) on the same data, it was showed that repeatability estimates for composite samples were higher than for individual samples, and that analysing of composite samples is preferable. Correlations and repeatability estimates increased when compiled samples included up to three individual samples. Including more than three individual samples yielded only minor improvements. This suggests that it would be preferable to collect composite faecal samples based on spot samples from two or three consecutive days, preferable from the mid-lactation period. However, the cows in a herd are usually at different lactation stages at a given day and sampling from mid-lactation only would be time-consuming. Further, for a proper genetic analysis, the collection of samples from the contemporaries is equally important as obtaining reliable measurement from individual itself. Therefore, in a suitable sampling design, cows are sampled several times during each lactation and all lactating contemporaries are sampled as well. Such a design could be achieved by sampling all lactating cows of a herd in regular intervals (every third or fourth month) depending on the heritability of the trait and on the sampling costs. Conclusions The cow-specific variability for the digestibility traits was small, but even a small improvement would be beneficial for the farmers and also for the environment. The repeatability estimates found in this study indicate that possibilities for genetic selection might exist. Our findings showed that NIRS can be used to assess cow-specific digestibility. The moderate correlation between new NIRS based measures and the standard AIA method cannot increase much, because the animal-wise variation is relatively small relative to the standard error of the assays. In this study, the accuracy of the direct faecal OMD appeared lower than the accuracy based on iNDF as an internal marker. The obtained lower accuracy for direct OMD may be partially explained by the small calibration data set, which should be investigated in future studies when larger calibration data set is available. Based on the assessment of different sampling protocols for sampling faeces of individual cows, we recommend collecting composite faecal samples, made from two or three daily samples, from cows being at least 1 month in milk. In practice, a collection from the cows of a herd every third or fourth month might be a good choice. Then, all the cows in the same contemporary groups would be sampled at the same time. 123



Acknowledgements We gratefully acknowledge the help of the Luke farm staff and technicians during the sample collection, preparation and analysis. The study was funded by Finnish Ministry of Agriculture (DNRO: 1844/312/ 2012), Valio Ltd, Faba co-op, VikingGenetics, Suomen Naudanjalostuss€ a€ ati€ o and RAISIOagro Ltd, which is greatly appreciated. References AOAC International (1990) Official Methods of Analysis. Association of Official Analytical Chemists Inc, Arlington, VA. Beecher M., Buckley F., Waters S.M., Boland T.M., Enriquez-Hidalgo D., Deighton M.H., O’Donovan M., Lewis E. (2014) Gastrointestinal tract size, total-tract digestibility, and rumen microflora in different dairy cow genotypes. J. Dairy Sci., 97, 1–12. Berry D.P., Horan B., O’Donovan M., Buckley F., Kennedy E., McEvoy M., Dillon P. (2007) Genetics of grass dry matter intake, energy balance, and digestibility in grazing Irish dairy cows. J. Dairy Sci., 90, 4835–4845. Boval M., Coates D.B., Lecomte P., Decruyenaere V., Archimede H. (2004) Faecal near infrared reflectance spectroscopy (NIRS) to assess chemical composition, in vivo digestibility and intake of tropical grass by Creole cattle. Anim. Feed Tech., 114, 19–29. Deaville E. R., Flynn P. C. (2000) Near infrared reflectance spectroscopy: an alternative approach to forage quality evaluation. In: A. Givens, et al. (eds), Forage Evaluation in Ruminant Nutrition. CAB International, Wallingford, pp. 301–320. Decruyenaere V., Lecomte P., Demarquilly C., Aufrere J., Dardenne P., Stilmant D., Buldgen A. (2009) Evaluation of green forage intake and digestibility in ruminants using near infrared reflectance spectroscopy (NIRS): Developing a global calibration. Anim. Feed Tech., 148, 138–156. Decruyenaere V., Froidmont E., Bartiaux-Thill N., Buldgen A., Stilmant D. (2012) 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. Anim. Feed Tech., 173, 220–234. Decruyenaere V., Planchon V., Dardenne P., Stilmant D. (2015) Prediction error and repeatability of near infrared reflectance spectroscopy applied to faeces samples in order to predict voluntary intake and digestibility of forages by ruminants. Anim. Feed Tech., 205, 49–59. Eriksson T., Krizsan S. J., Volden H., Eiriksson T., Jalava T., Nissen H., Eriksen C., Brohede L., Vedder H., Weisbjerg M. R. (2012) Nordic ring test on iNDF and NDF contents of ten feed samples. In: Proceedings of the 3rd

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